Fluid temperatures: Modeling the thermal regime of a river network
Rhonda Mazza; Ashley Steel
2017-01-01
Water temperature drives the complex food web of a river network. Aquatic organisms hatch, feed, and reproduce in thermal niches within the tributaries and mainstem that comprise the river network. Changes in water temperature can synchronize or asynchronize the timing of their life stages throughout the year. The water temperature fluctuates over time and place,...
On effective temperature in network models of collective behavior
International Nuclear Information System (INIS)
Porfiri, Maurizio; Ariel, Gil
2016-01-01
Collective behavior of self-propelled units is studied analytically within the Vectorial Network Model (VNM), a mean-field approximation of the well-known Vicsek model. We propose a dynamical systems framework to study the stochastic dynamics of the VNM in the presence of general additive noise. We establish that a single parameter, which is a linear function of the circular mean of the noise, controls the macroscopic phase of the system—ordered or disordered. By establishing a fluctuation–dissipation relation, we posit that this parameter can be regarded as an effective temperature of collective behavior. The exact critical temperature is obtained analytically for systems with small connectivity, equivalent to low-density ensembles of self-propelled units. Numerical simulations are conducted to demonstrate the applicability of this new notion of effective temperature to the Vicsek model. The identification of an effective temperature of collective behavior is an important step toward understanding order–disorder phase transitions, informing consistent coarse-graining techniques and explaining the physics underlying the emergence of collective phenomena.
Identifying the optimal supply temperature in district heating networks - A modelling approach
DEFF Research Database (Denmark)
Mohammadi, Soma; Bojesen, Carsten
2014-01-01
of this study is to develop a model for thermo-hydraulic calculation of low temperature DH system. The modelling is performed with emphasis on transient heat transfer in pipe networks. The pseudo-dynamic approach is adopted to model the District Heating Network [DHN] behaviour which estimates the temperature...... dynamically while the flow and pressure are calculated on the basis of steady state conditions. The implicit finite element method is applied to simulate the transient temperature behaviour in the network. Pipe network heat losses, pressure drop in the network and return temperature to the plant...... are calculated in the developed model. The model will serve eventually as a basis to find out the optimal supply temperature in an existing DHN in later work. The modelling results are used as decision support for existing DHN; proposing possible modifications to operate at optimal supply temperature....
International Nuclear Information System (INIS)
Xu, Bo; Dan, Han-Cheng; Li, Liang
2017-01-01
Highlights: • Pavement temperature prediction model is presented with improved BP neural network. • Dynamic and static methods are presented to predict pavement temperature. • Pavement temperature can be excellently predicted in next 3 h. - Abstract: Ice cover on pavement threatens traffic safety, and pavement temperature is the main factor used to determine whether the wet pavement is icy or not. In this paper, a temperature prediction model of the pavement in winter is established by introducing an improved Back Propagation (BP) neural network model. Before the application of the BP neural network model, many efforts were made to eliminate chaos and determine the regularity of temperature on the pavement surface (e.g., analyze the regularity of diurnal and monthly variations of pavement temperature). New dynamic and static prediction methods are presented by improving the algorithms to intelligently overcome the prediction inaccuracy at the change point of daily temperature. Furthermore, some scenarios have been compared for different dates and road sections to verify the reliability of the prediction model. According to the analysis results, the daily pavement temperatures can be accurately predicted for the next 3 h from the time of prediction by combining the dynamic and static prediction methods. The presented method in this paper can provide technical references for temperature prediction of the pavement and the development of an early-warning system for icy pavements in cold regions.
Directory of Open Access Journals (Sweden)
R. K. Tiwari
2011-08-01
Full Text Available A novel technique based on the Bayesian neural network (BNN theory is developed and employed to model the temperature variation record from the Western Himalayas. In order to estimate an a posteriori probability function, the BNN is trained with the Hybrid Monte Carlo (HMC/Markov Chain Monte Carlo (MCMC simulations algorithm. The efficacy of the new algorithm is tested on the well known chaotic, first order autoregressive (AR and random models and then applied to model the temperature variation record decoded from the tree-ring widths of the Western Himalayas for the period spanning over 1226–2000 AD. For modeling the actual tree-ring temperature data, optimum network parameters are chosen appropriately and then cross-validation test is performed to ensure the generalization skill of the network on the new data set. Finally, prediction result based on the BNN model is compared with the conventional artificial neural network (ANN and the AR linear models results. The comparative results show that the BNN based analysis makes better prediction than the ANN and the AR models. The new BNN modeling approach provides a viable tool for climate studies and could also be exploited for modeling other kinds of environmental data.
DEFF Research Database (Denmark)
Green, Ole; Shahrak Nadimi, Esmaeil; Blanes-Vidal, Victoria
2009-01-01
the sensor nodes were successfully delivered to the gateway. The reliable performance of the network confirmed the correct choice of network characteristics (i.e., frequency range of 433 MHz, a handshaking communication protocol and 10 mW transmission power). The designed sensor housings were capable......Abstract: By monitoring silage temperature at different locations inside silage stacks, it is possible to detect any significant increases in temperature occurring during silage decomposition. The objectives of this study were: (1) to develop novel noninvasive wireless sensor nodes for measuring...... the temperature inside silage stacks; (2) to design a suitable sensor protection housing that prevents physical and chemical damage to the sensor; and (3) to mathematically model temperature variations inside a silage stack, using system identification techniques. The designed wireless nodes were used to monitor...
DEFF Research Database (Denmark)
Green, O.; Nadimi, E.S.; Blanes-Vidal, V.
2009-01-01
the sensor nodes were successfully delivered to the gateway. The reliable performance of the network confirmed the correct choice of network characteristics (i.e., frequency range of 433 MHz, a handshaking communication protocol, and 10 mW transmission power). The designed sensor housings were capable......By monitoring silage temperature at different locations inside silage stacks, it is possible to detect any significant increases in temperature occurring during silage decomposition. The objectives of this study were: (1) to develop novel noninvasive wireless sensor nodes for measuring...... the temperature inside silage stacks; (2) to design a suitable sensor protection housing that prevents physical and chemical damage to the sensor: and (3) to mathematically model temperature variations inside a silage stack, using system identification techniques. The designed wireless nodes were used to monitor...
Modeling and Prediction of Coal Ash Fusion Temperature based on BP Neural Network
Directory of Open Access Journals (Sweden)
Miao Suzhen
2016-01-01
Full Text Available Coal ash is the residual generated from combustion of coal. The ash fusion temperature (AFT of coal gives detail information on the suitability of a coal source for gasification procedures, and specifically to which extent ash agglomeration or clinkering is likely to occur within the gasifier. To investigate the contribution of oxides in coal ash to AFT, data of coal ash chemical compositions and Softening Temperature (ST in different regions of China were collected in this work and a BP neural network model was established by XD-APC PLATFORM. In the BP model, the inputs were the ash compositions and the output was the ST. In addition, the ash fusion temperature prediction model was obtained by industrial data and the model was generalized by different industrial data. Compared to empirical formulas, the BP neural network obtained better results. By different tests, the best result and the best configurations for the model were obtained: hidden layer nodes of the BP network was setted as three, the component contents (SiO2, Al2O3, Fe2O3, CaO, MgO were used as inputs and ST was used as output of the model.
Response surface and neural network based predictive models of cutting temperature in hard turning
Directory of Open Access Journals (Sweden)
Mozammel Mia
2016-11-01
Full Text Available The present study aimed to develop the predictive models of average tool-workpiece interface temperature in hard turning of AISI 1060 steels by coated carbide insert. The Response Surface Methodology (RSM and Artificial Neural Network (ANN were employed to predict the temperature in respect of cutting speed, feed rate and material hardness. The number and orientation of the experimental trials, conducted in both dry and high pressure coolant (HPC environments, were planned using full factorial design. The temperature was measured by using the tool-work thermocouple. In RSM model, two quadratic equations of temperature were derived from experimental data. The analysis of variance (ANOVA and mean absolute percentage error (MAPE were performed to suffice the adequacy of the models. In ANN model, 80% data were used to train and 20% data were employed for testing. Like RSM, herein, the error analysis was also conducted. The accuracy of the RSM and ANN model was found to be ⩾99%. The ANN models exhibit an error of ∼5% MAE for testing data. The regression coefficient was found to be greater than 99.9% for both dry and HPC. Both these models are acceptable, although the ANN model demonstrated a higher accuracy. These models, if employed, are expected to provide a better control of cutting temperature in turning of hardened steel.
Foreground removal from Planck Sky Model temperature maps using a MLP neural network
DEFF Research Database (Denmark)
Nørgaard-Nielsen, Hans Ulrik; Hebert, K.
2009-01-01
with no systematic errors. To demonstrate the feasibility of a simple multilayer perceptron (MLP) neural network for extracting the CMB temperature signal, we have analyzed a specific data set, namely the Planck Sky Model maps, developed for evaluation of different component separation methods before including them...... in the Planck data analysis pipeline. It is found that a MLP neural network can provide a CMB map of about 80% of the sky to a very high degree uncorrelated with the foreground components. Also the derived power spectrum shows little evidence for systematic errors....
A regional neural network model for predicting mean daily river water temperature
Wagner, Tyler; DeWeber, Jefferson Tyrell
2014-01-01
Water temperature is a fundamental property of river habitat and often a key aspect of river resource management, but measurements to characterize thermal regimes are not available for most streams and rivers. As such, we developed an artificial neural network (ANN) ensemble model to predict mean daily water temperature in 197,402 individual stream reaches during the warm season (May–October) throughout the native range of brook trout Salvelinus fontinalis in the eastern U.S. We compared four models with different groups of predictors to determine how well water temperature could be predicted by climatic, landform, and land cover attributes, and used the median prediction from an ensemble of 100 ANNs as our final prediction for each model. The final model included air temperature, landform attributes and forested land cover and predicted mean daily water temperatures with moderate accuracy as determined by root mean squared error (RMSE) at 886 training sites with data from 1980 to 2009 (RMSE = 1.91 °C). Based on validation at 96 sites (RMSE = 1.82) and separately for data from 2010 (RMSE = 1.93), a year with relatively warmer conditions, the model was able to generalize to new stream reaches and years. The most important predictors were mean daily air temperature, prior 7 day mean air temperature, and network catchment area according to sensitivity analyses. Forest land cover at both riparian and catchment extents had relatively weak but clear negative effects. Predicted daily water temperature averaged for the month of July matched expected spatial trends with cooler temperatures in headwaters and at higher elevations and latitudes. Our ANN ensemble is unique in predicting daily temperatures throughout a large region, while other regional efforts have predicted at relatively coarse time steps. The model may prove a useful tool for predicting water temperatures in sampled and unsampled rivers under current conditions and future projections of climate
A fuzzy neural network model to forecast the percent cloud coverage and cloud top temperature maps
Directory of Open Access Journals (Sweden)
Y. Tulunay
2008-12-01
Full Text Available Atmospheric processes are highly nonlinear. A small group at the METU in Ankara has been working on a fuzzy data driven generic model of nonlinear processes. The model developed is called the Middle East Technical University Fuzzy Neural Network Model (METU-FNN-M. The METU-FNN-M consists of a Fuzzy Inference System (METU-FIS, a data driven Neural Network module (METU-FNN of one hidden layer and several neurons, and a mapping module, which employs the Bezier Surface Mapping technique. In this paper, the percent cloud coverage (%CC and cloud top temperatures (CTT are forecast one month ahead of time at 96 grid locations. The probable influence of cosmic rays and sunspot numbers on cloudiness is considered by using the METU-FNN-M.
International Nuclear Information System (INIS)
Formosa, Fabien; Badel, Adrien; Lottin, Jacques
2014-01-01
Highlights: • An equivalent electrical network modeling of Stirling engine is proposed. • This model is applied to a membrane low temperate double acting Stirling engine. • The operating conditions (self-startup and steady state behavior) are defined. • An experimental engine is presented and tested. • The model is validated against experimental results. - Abstract: This work presents a network model to simulate the periodic behavior of a double acting free piston type Stirling engine. Each component of the engine is considered independently and its equivalent electrical circuit derived. When assembled in a global electrical network, a global model of the engine is established. Its steady behavior can be obtained by the analysis of the transfer function for one phase from the piston to the expansion chamber. It is then possible to simulate the dynamic (steady state stroke and operation frequency) as well as the thermodynamic performances (output power and efficiency) for given mean pressure, heat source and heat sink temperatures. The motion amplitude especially can be determined by the spring-mass properties of the moving parts and the main nonlinear effects which are taken into account in the model. The thermodynamic features of the model have then been validated using the classical isothermal Schmidt analysis for a given stroke. A three-phase low temperature differential double acting free membrane architecture has been built and tested. The experimental results are compared with the model and a satisfactory agreement is obtained. The stroke and operating frequency are predicted with less than 2% error whereas the output power discrepancy is of about 30%. Finally, some optimization routes are suggested to improve the design and maximize the performances aiming at waste heat recovery applications
Foreground removal from Planck Sky Model temperature maps using a MLP neural network
Nørgaard-Nielsen, H. U.; Hebert, K.
2009-08-01
Unfortunately, the Cosmic Microwave Background (CMB) radiation is contaminated by emission originating in the Milky Way (synchrotron, free-free and dust emission). Since the cosmological information is statistically in nature, it is essential to remove this foreground emission and leave the CMB with no systematic errors. To demonstrate the feasibility of a simple multilayer perceptron (MLP) neural network for extracting the CMB temperature signal, we have analyzed a specific data set, namely the Planck Sky Model maps, developed for evaluation of different component separation methods before including them in the Planck data analysis pipeline. It is found that a MLP neural network can provide a CMB map of about 80 % of the sky to a very high degree uncorrelated with the foreground components. Also the derived power spectrum shows little evidence for systematic errors.
Ryu, Duchwan
2013-03-01
The sea surface temperature (SST) is an important factor of the earth climate system. A deep understanding of SST is essential for climate monitoring and prediction. In general, SST follows a nonlinear pattern in both time and location and can be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle filter to estimate the parameters of the dynamic model. We analyze the SST observed in the Caribbean Islands area after a hurricane using the proposed dynamic model. Comparing to the traditional grid-based approach that requires a supercomputer due to its high computational demand, our approach requires much less CPU time and makes real-time forecasting of SST doable on a personal computer. Supplementary materials for this article are available online. © 2013 American Statistical Association.
Kisi, Ozgur; Sanikhani, Hadi; Cobaner, Murat
2017-08-01
The applicability of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at different depths is investigated in this study. Weather data from two stations, Mersin and Adana, Turkey, were used as inputs to the applied models in order to model monthly STs. The first part of the study focused on comparison of ANN, ANFIS, and GP models in modeling ST of two stations at the depths of 10, 50, and 100 cm. GP was found to perform better than the ANN and ANFIS-SC in estimating monthly ST. The effect of periodicity (month of the year) on models' accuracy was also investigated. Including periodicity component in models' inputs considerably increased their accuracies. The root mean square error (RMSE) of ANN models was respectively decreased by 34 and 27 % for the depths of 10 and 100 cm adding the periodicity input. In the second part of the study, the accuracies of the ANN, ANFIS, and GP models were compared in estimating ST of Mersin Station using the climatic data of Adana Station. The ANN models generally performed better than the ANFIS-SC and GP in modeling ST of Mersin Station without local climatic inputs.
International Nuclear Information System (INIS)
Zahedi, Gholamreza; Karami, Zohre; Yaghoobi, Hamed
2009-01-01
In this study, various estimation methods have been reviewed for hydrate formation temperature (HFT) and two procedures have been presented. In the first method, two general correlations have been proposed for HFT. One of the correlations has 11 parameters, and the second one has 18 parameters. In order to obtain constants in proposed equations, 203 experimental data points have been collected from literatures. The Engineering Equation Solver (EES) and Statistical Package for the Social Sciences (SPSS) soft wares have been employed for statistical analysis of the data. Accuracy of the obtained correlations also has been declared by comparison with experimental data and some recent common used correlations. In the second method, HFT is estimated by artificial neural network (ANN) approach. In this case, various architectures have been checked using 70% of experimental data for training of ANN. Among the various architectures multi layer perceptron (MLP) network with trainlm training algorithm was found as the best architecture. Comparing the obtained ANN model results with 30% of unseen data confirms ANN excellent estimation performance. It was found that ANN is more accurate than traditional methods and even our two proposed correlations for HFT estimation.
Czarnik, Piotr; Dziarmaga, Jacek; Oleś, Andrzej M.
2017-07-01
The variational tensor network renormalization approach to two-dimensional (2D) quantum systems at finite temperature is applied to a model suffering the notorious quantum Monte Carlo sign problem—the orbital eg model with spatially highly anisotropic orbital interactions. Coarse graining of the tensor network along the inverse temperature β yields a numerically tractable 2D tensor network representing the Gibbs state. Its bond dimension D —limiting the amount of entanglement—is a natural refinement parameter. Increasing D we obtain a converged order parameter and its linear susceptibility close to the critical point. They confirm the existence of finite order parameter below the critical temperature Tc, provide a numerically exact estimate of Tc, and give the critical exponents within 1 % of the 2D Ising universality class.
Directory of Open Access Journals (Sweden)
Hong Yang
2015-07-01
Full Text Available Thermally modified wood has high dimensional stability and biological durability.But if the process parameters of thermal modification are not appropriate, then there will be a decline in the physical properties of wood.A neural network algorithm was employed in this study to establish the relationship between the process parameters of high-temperature and high-pressure thermal modification and the mechanical properties of the wood. Three important parameters: temperature, relative humidity, and treatment time, were considered as the inputs to the neural network. Back propagation (BP neural network and radial basis function (RBF neural network models for prediction were built and compared. The comparison showed that the RBF neural network model had advantages in network structure, convergence speed, and generalization capacity. On this basis, the inverse model, reflecting the relationship between the process parameters and the mechanical properties of wood, was established. Given the desired mechanical properties of the wood, the thermal modification process parameters could be inversely optimized and predicted. The results indicated that the model has good learning ability and generalization capacity. This is of great importance for the theoretical and applicational studies of the thermal modification of wood.
Ryu, Duchwan; Liang, Faming; Mallick, Bani K.
2013-01-01
be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle
Energy Technology Data Exchange (ETDEWEB)
Duchesne, Marc A. [Chemical and Biological Engineering Department, University of Ottawa, 161 Louis Pasteur, Ottawa, Ont. (Canada); CanmetENERGY, 1 Haanel Drive, Ottawa, Ontario (Canada); Macchi, Arturo [Chemical and Biological Engineering Department, University of Ottawa, 161 Louis Pasteur, Ottawa, Ont. (Canada); Lu, Dennis Y.; Hughes, Robin W.; McCalden, David; Anthony, Edward J. [CanmetENERGY, 1 Haanel Drive, Ottawa, Ontario (Canada)
2010-08-15
Threshold slag viscosity heuristics are often used for the initial assessment of coal gasification projects. Slag viscosity predictions are also required for advanced combustion and gasification models. Due to unsatisfactory performance of theoretical equations, an artificial neural network model was developed to predict slag viscosity over a broad range of temperatures and slag compositions. This model outperforms other slag viscosity models, resulting in an average error factor of 5.05 which is lower than the best obtained with other available models. Genesee coal ash viscosity predictions were made to investigate the effect of adding Canadian limestone and dolomite. The results indicate that magnesium in the fluxing agent provides a greater viscosity reduction than calcium for the threshold slag tapping temperature range. (author)
Spatial Statistical Network Models for Stream and River Temperatures in the Chesapeake Bay Watershed
Numerous metrics have been proposed to describe stream/river thermal regimes, and researchers are still struggling with the need to describe thermal regimes in a parsimonious fashion. Regional temperature models are needed for characterizing and mapping current stream thermal re...
Modeling of High Temperature Oxidation Behavior of FeCrAl Alloy by using Artificial Neural Network
Energy Technology Data Exchange (ETDEWEB)
Kim, Jae Joon; Ryu, Ho Jin [KAIST, Daejeon (Korea, Republic of)
2016-10-15
Refractory alloys are candidate materials for replacing current zirconium-base cladding of light water reactors and they retain significant creep resistance and mechanical strength at high temperatures up to 1500 ℃ due to their high melting temperature. Thermal neutron cross sections of refractory metals are higher than that of zirconium, however the loss of neutron can be overcome by reducing cladding thickness which can be facilitated with enhanced mechanical properties. However, most refractory metals show the poor oxidation resistance at a high temperature. Oxidation behaviors of the various compositions of FeCrAl alloys in high temperature conditions were modeled by using Bayesian neural network. The automatic relevance determination (ARD) technique represented the influence of the composition of alloying elements on the oxidation resistance of FeCrAl alloys. This model can be utilized to understand the tendency of oxidation behavior along the composition of each element and prove the applicability of neural network modeling for the development of new cladding material of light water reactors.
Bachrudin, A.; Mohamed, N. B.; Supian, S.; Sukono; Hidayat, Y.
2018-03-01
Application of existing geostatistical theory of stream networks provides a number of interesting and challenging problems. Most of statistical tools in the traditional geostatistics have been based on a Euclidean distance such as autocovariance functions, but for stream data is not permissible since it deals with a stream distance. To overcome this autocovariance developed a model based on the distance the flow with using convolution kernel approach (moving average construction). Spatial model for a stream networks is widely used to monitor environmental on a river networks. In a case study of a river in province of West Java, the objective of this paper is to analyze a capability of a predictive on two environmental variables, potential of hydrogen (PH) and temperature using ordinary kriging. Several the empirical results show: (1) The best fit of autocovariance functions for temperature and potential hydrogen (ph) of Citarik River is linear which also yields the smallest root mean squared prediction error (RMSPE), (2) the spatial correlation values between the locations on upstream and on downstream of Citarik river exhibit decreasingly
Landeras, G.; López, J. J.; Kisi, O.; Shiri, J.
2012-04-01
The correct observation/estimation of surface incoming solar radiation (RS) is very important for many agricultural, meteorological and hydrological related applications. While most weather stations are provided with sensors for air temperature detection, the presence of sensors necessary for the detection of solar radiation is not so habitual and the data quality provided by them is sometimes poor. In these cases it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional temperature based solar radiation equations were also included in this study and compared with artificial intelligence based approaches. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SSRMSE), MAE-based skill score (SSMAE) and r2 criterion of Nash and Sutcliffe criteria were used to assess the models' performances. An ANN (a four-input multilayer perceptron with ten neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m-2 d-1 of RMSE). A four-input ANFIS model revealed as an interesting alternative to ANNs (3.14 MJ m-2 d-1 of RMSE). Very limited number of studies has been done on estimation of solar radiation based on ANFIS, and the present one demonstrated the ability of ANFIS to model solar radiation based on temperatures and extraterrestrial radiation. By the way this study demonstrated, for the first time, the ability of GEP models to model solar radiation based on daily atmospheric variables. Despite the accuracy of GEP models was slightly lower than the ANFIS and ANN models the genetic programming models (i.e., GEP) are superior to other artificial intelligence models in giving a simple explicit equation for the
Directory of Open Access Journals (Sweden)
Yuan-Chieh Lo
2018-02-01
Full Text Available Thermal characteristic analysis is essential for machine tool spindles because sudden failures may occur due to unexpected thermal issue. This article presents a lumped-parameter Thermal Network Model (TNM and its parameter estimation scheme, including hardware and software, in order to characterize both the steady-state and transient thermal behavior of machine tool spindles. For the hardware, the authors develop a Bluetooth Temperature Sensor Module (BTSM which accompanying with three types of temperature-sensing probes (magnetic, screw, and probe. Its specification, through experimental test, achieves to the precision ±(0.1 + 0.0029|t| °C, resolution 0.00489 °C, power consumption 7 mW, and size Ø40 mm × 27 mm. For the software, the heat transfer characteristics of the machine tool spindle correlative to rotating speed are derived based on the theory of heat transfer and empirical formula. The predictive TNM of spindles was developed by grey-box estimation and experimental results. Even under such complicated operating conditions as various speeds and different initial conditions, the experiments validate that the present modeling methodology provides a robust and reliable tool for the temperature prediction with normalized mean square error of 99.5% agreement, and the present approach is transferable to the other spindles with a similar structure. For realizing the edge computing in smart manufacturing, a reduced-order TNM is constructed by Model Order Reduction (MOR technique and implemented into the real-time embedded system.
Neural networks to predict exosphere temperature corrections
Choury, Anna; Bruinsma, Sean; Schaeffer, Philippe
2013-10-01
Precise orbit prediction requires a forecast of the atmospheric drag force with a high degree of accuracy. Artificial neural networks are universal approximators derived from artificial intelligence and are widely used for prediction. This paper presents a method of artificial neural networking for prediction of the thermosphere density by forecasting exospheric temperature, which will be used by the semiempirical thermosphere Drag Temperature Model (DTM) currently developed. Artificial neural network has shown to be an effective and robust forecasting model for temperature prediction. The proposed model can be used for any mission from which temperature can be deduced accurately, i.e., it does not require specific training. Although the primary goal of the study was to create a model for 1 day ahead forecast, the proposed architecture has been generalized to 2 and 3 days prediction as well. The impact of artificial neural network predictions has been quantified for the low-orbiting satellite Gravity Field and Steady-State Ocean Circulation Explorer in 2011, and an order of magnitude smaller orbit errors were found when compared with orbits propagated using the thermosphere model DTM2009.
Collaborative networks: Reference modeling
Camarinha-Matos, L.M.; Afsarmanesh, H.
2008-01-01
Collaborative Networks: Reference Modeling works to establish a theoretical foundation for Collaborative Networks. Particular emphasis is put on modeling multiple facets of collaborative networks and establishing a comprehensive modeling framework that captures and structures diverse perspectives of
Directory of Open Access Journals (Sweden)
Li Zhang
2017-12-01
Full Text Available Winding hotspot temperature is the key factor affecting the load capacity and service life of transformers. For the early detection of transformer winding hotspot temperature anomalies, a new prediction model for the hotspot temperature fluctuation range based on fuzzy information granulation (FIG and the chaotic particle swarm optimized wavelet neural network (CPSO-WNN is proposed in this paper. The raw data are firstly processed by FIG to extract useful information from each time window. The extracted information is then used to construct a wavelet neural network (WNN prediction model. Furthermore, the structural parameters of WNN are optimized by chaotic particle swarm optimization (CPSO before it is used to predict the fluctuation range of the hotspot temperature. By analyzing the experimental data with four different prediction models, we find that the proposed method is more effective and is of guiding significance for the operation and maintenance of transformers.
Tabaraki, R; Khayamian, T; Ensafi, A A
2006-09-01
A wavelet neural network (WNN) model in quantitative structure property relationship (QSPR) was developed for predicting solubility of 25 anthraquinone dyes in supercritical carbon dioxide over a wide range of pressures (70-770 bar) and temperatures (291-423 K). A large number of descriptors were calculated with Dragon software and a subset of calculated descriptors was selected from 18 classes of Dragon descriptors with a stepwise multiple linear regression (MLR) as a feature selection technique. Six calculated and two experimental descriptors, pressure and temperature, were selected as the most feasible descriptors. The selected descriptors were used as input nodes in a wavelet neural network (WNN) model. The wavelet neural network architecture and its parameters were optimized simultaneously. The data was randomly divided to the training, prediction and validation sets. The predictive ability of the model was evaluated using validation data set. The root mean squares error (RMSE) and mean absolute errors were 0.339 and 0.221, respectively, for the validation data set. The performance of the WNN model was also compared with artificial neural network (ANN) model and the results showed the superiority of the WNN over ANN model.
Curvature and temperature of complex networks.
Krioukov, Dmitri; Papadopoulos, Fragkiskos; Vahdat, Amin; Boguñá, Marián
2009-09-01
We show that heterogeneous degree distributions in observed scale-free topologies of complex networks can emerge as a consequence of the exponential expansion of hidden hyperbolic space. Fermi-Dirac statistics provides a physical interpretation of hyperbolic distances as energies of links. The hidden space curvature affects the heterogeneity of the degree distribution, while clustering is a function of temperature. We embed the internet into the hyperbolic plane and find a remarkable congruency between the embedding and our hyperbolic model. Besides proving our model realistic, this embedding may be used for routing with only local information, which holds significant promise for improving the performance of internet routing.
Modeling Network Interdiction Tasks
2015-09-17
118 xiii Table Page 36 Computation times for weighted, 100-node random networks for GAND Approach testing in Python ...in Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 38 Accuracy measures for weighted, 100-node random networks for GAND...networks [15:p. 1]. A common approach to modeling network interdiction is to formulate the problem in terms of a two-stage strategic game between two
Shakiba, Mohammad; Parson, Nick; Chen, X-Grant
2016-06-30
The hot deformation behavior of Al-0.12Fe-0.1Si alloys with varied amounts of Cu (0.002-0.31 wt %) was investigated by uniaxial compression tests conducted at different temperatures (400 °C-550 °C) and strain rates (0.01-10 s -1 ). The results demonstrated that flow stress decreased with increasing deformation temperature and decreasing strain rate, while flow stress increased with increasing Cu content for all deformation conditions studied due to the solute drag effect. Based on the experimental data, an artificial neural network (ANN) model was developed to study the relationship between chemical composition, deformation variables and high-temperature flow behavior. A three-layer feed-forward back-propagation artificial neural network with 20 neurons in a hidden layer was established in this study. The input parameters were Cu content, temperature, strain rate and strain, while the flow stress was the output. The performance of the proposed model was evaluated using the K-fold cross-validation method. The results showed excellent generalization capability of the developed model. Sensitivity analysis indicated that the strain rate is the most important parameter, while the Cu content exhibited a modest but significant influence on the flow stress.
International Nuclear Information System (INIS)
Max, G
2011-01-01
Traffic models in computer networks can be described as a complicated system. These systems show non-linear features and to simulate behaviours of these systems are also difficult. Before implementing network equipments users wants to know capability of their computer network. They do not want the servers to be overloaded during temporary traffic peaks when more requests arrive than the server is designed for. As a starting point for our study a non-linear system model of network traffic is established to exam behaviour of the network planned. The paper presents setting up a non-linear simulation model that helps us to observe dataflow problems of the networks. This simple model captures the relationship between the competing traffic and the input and output dataflow. In this paper, we also focus on measuring the bottleneck of the network, which was defined as the difference between the link capacity and the competing traffic volume on the link that limits end-to-end throughput. We validate the model using measurements on a working network. The results show that the initial model estimates well main behaviours and critical parameters of the network. Based on this study, we propose to develop a new algorithm, which experimentally determines and predict the available parameters of the network modelled.
Modeling the citation network by network cosmology.
Xie, Zheng; Ouyang, Zhenzheng; Zhang, Pengyuan; Yi, Dongyun; Kong, Dexing
2015-01-01
Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous spacetimes have some restrictions when describing some phenomena in citation networks, e.g., the hot papers receive more citations than other simultaneously published papers. We propose an inhomogenous causal network model to model the citation network, the connection mechanism of which well expresses some features of citation. The node growth trend and degree distributions of the generated networks also fit those of some citation networks well.
DEFF Research Database (Denmark)
Andersen, Kasper Winther
Three main topics are presented in this thesis. The first and largest topic concerns network modelling of functional Magnetic Resonance Imaging (fMRI) and Diffusion Weighted Imaging (DWI). In particular nonparametric Bayesian methods are used to model brain networks derived from resting state f...... for their ability to reproduce node clustering and predict unseen data. Comparing the models on whole brain networks, BCD and IRM showed better reproducibility and predictability than IDM, suggesting that resting state networks exhibit community structure. This also points to the importance of using models, which...... allow for complex interactions between all pairs of clusters. In addition, it is demonstrated how the IRM can be used for segmenting brain structures into functionally coherent clusters. A new nonparametric Bayesian network model is presented. The model builds upon the IRM and can be used to infer...
Modeling Epidemic Network Failures
DEFF Research Database (Denmark)
Ruepp, Sarah Renée; Fagertun, Anna Manolova
2013-01-01
This paper presents the implementation of a failure propagation model for transport networks when multiple failures occur resulting in an epidemic. We model the Susceptible Infected Disabled (SID) epidemic model and validate it by comparing it to analytical solutions. Furthermore, we evaluate...... the SID model’s behavior and impact on the network performance, as well as the severity of the infection spreading. The simulations are carried out in OPNET Modeler. The model provides an important input to epidemic connection recovery mechanisms, and can due to its flexibility and versatility be used...... to evaluate multiple epidemic scenarios in various network types....
Artificial neural network modelling
Samarasinghe, Sandhya
2016-01-01
This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .
Pai, Tzu-Yi; Lin, Kae-Long; Shie, Je-Lung; Chang, Tien-Chin; Chen, Bor-Yann
2011-03-01
A grey model (GM) and an artificial neural network (ANN) were employed to predict co-melting temperature of municipal solid waste incinerator (MSWI) fly ash and sewage sludge ash (SSA) during formation of modified slag. The results indicated that in the aspect of model prediction, the mean absolute percentage error (MAPEs) were between 1.69 and 13.20% when adopting seven different GM (1, N) models. The MAPE were 1.59 and 1.31% when GM (1, 1) and rolling grey model (RGM (1, 1)) were adopted. The MAPEs fell within the range of 0.04 and 0.50% using different types of ANN. In GMs, the MAPE of 1.31% was found to be the lowest when using RGM (1, 1) to predict co-melting temperature. This value was higher than those of ANN2-1 to ANN8-1 by 1.27, 1.25, 1.24, 1.18, 1.16, 1.14 and 0.81%, respectively. GM only required a small amount of data (at least four data). Therefore, GM could be applied successfully in predicting the co-melting temperature of MSWI fly ash and SSA when no sufficient information is available. It also indicates that both the composition of MSWI fly ash and SSA could be applied on the prediction of co-melting temperature.
Directory of Open Access Journals (Sweden)
K. Piotrowski
2005-09-01
Full Text Available The kinetics of Fe2O3->FeO reaction was investigated. The thermogravimetric (TGA data covered the reduction of hematite both by pure species (nitrogen diluted CO or H2 and by their mixture. The conventional analysis has indicated that initially the reduction of hematite is a complex, surface controlled process, however once a thin layer of lower oxidation state iron oxides (magnetite, wüstite is formed on the surface, it changes to diffusion control. Artificial Neural Network (ANN has proved to be a convenient tool for modeling of this complex, heterogeneous reaction runs within the both (kinetic and diffusion regions, correctly considering influence of temperature and gas composition effects and their complex interactions. ANN's model shows the capability to mimic some extreme (minimum of the reaction rate within the determined temperature window, while the Arrhenius dependency is of limited use.
Directory of Open Access Journals (Sweden)
Xike Zhang
2018-05-01
Full Text Available Daily land surface temperature (LST forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD coupled with Machine Learning (ML algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs and a single residue item. Then, the Partial Autocorrelation Function (PACF is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE, Mean Absolute Error (MAE, Mean Absolute Percentage Error (MAPE, Root Mean Square Error (RMSE, Pearson Correlation Coefficient (CC and Nash-Sutcliffe Coefficient of Efficiency (NSCE. To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN, LSTM and Empirical Mode Decomposition (EMD coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other
Zhang, Xike; Zhang, Qiuwen; Zhang, Gui; Nie, Zhiping; Gui, Zifan; Que, Huafei
2018-05-21
Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five
Evaluation of thermal network correction program using test temperature data
Ishimoto, T.; Fink, L. C.
1972-01-01
An evaluation process to determine the accuracy of a computer program for thermal network correction is discussed. The evaluation is required since factors such as inaccuracies of temperatures, insufficient number of temperature points over a specified time period, lack of one-to-one correlation between temperature sensor and nodal locations, and incomplete temperature measurements are not present in the computer-generated information. The mathematical models used in the evaluation are those that describe a physical system composed of both a conventional and a heat pipe platform. A description of the models used, the results of the evaluation of the thermal network correction, and input instructions for the thermal network correction program are presented.
Directory of Open Access Journals (Sweden)
V. S. Kudryashov
2016-01-01
Full Text Available The article is devoted to the development of a correction control algorithm by temperature mode of a periodic rubber mixing process for JSC "Voronezh tire plant". The algorithm is designed to perform in the main controller a section of rubber mixing Siemens S7 CPU319F-3 PN/DP, which forms tasks for the local temperature controllers HESCH HE086 and Jumo dTRON304, operating by tempering stations. To compile the algorithm was performed a systematic analysis of rubber mixing process as an object of control and was developed a mathematical model of the process based on the heat balance equations describing the processes of heat transfer through the walls of technological devices, the change of coolant temperature and the temperature of the rubber compound mixing until discharge from the mixer chamber. Due to the complexity and nonlinearity of the control object – Rubber mixers and the availability of methods and a wide experience of this device control in an industrial environment, a correction algorithm is implemented on the basis of an artificial single-layer neural network and it provides the correction of tasks for local controllers on the cooling water temperature and air temperature in the workshop, which may vary considerably depending on the time of the year, and during prolonged operation of the equipment or its downtime. Tempering stations control is carried out by changing the flow of cold water from the cooler and on/off control of the heating elements. The analysis of the model experiments results and practical research at the main controller programming in the STEP 7 environment at the enterprise showed a decrease in the mixing time for different types of rubbers by reducing of heat transfer process control error.
Pryor, Sara C.; Sullivan, Ryan C.; Schoof, Justin T.
2017-12-01
The static energy content of the atmosphere is increasing on a global scale, but exhibits important subglobal and subregional scales of variability and is a useful parameter for integrating the net effect of changes in the partitioning of energy at the surface and for improving understanding of the causes of so-called warming holes (i.e., locations with decreasing daily maximum air temperatures (T) or increasing trends of lower magnitude than the global mean). Further, measures of the static energy content (herein the equivalent potential temperature, θe) are more strongly linked to excess human mortality and morbidity than air temperature alone, and have great relevance in understanding causes of past heat-related excess mortality and making projections of possible future events that are likely to be associated with negative human health and economic consequences. New nonlinear statistical models for summertime daily maximum and minimum θe are developed and used to advance understanding of drivers of historical change and variability over the eastern USA. The predictor variables are an index of the daily global mean temperature, daily indices of the synoptic-scale meteorology derived from T and specific humidity (Q) at 850 and 500 hPa geopotential heights (Z), and spatiotemporally averaged soil moisture (text">SM). text">SM is particularly important in determining the magnitude of θe over regions that have previously been identified as exhibiting warming holes, confirming the key importance of text">SM in dictating the partitioning of net radiation into sensible and latent heat and dictating trends in near-surface T and θe. Consistent with our a priori expectations, models built using artificial neural networks (ANNs) out-perform linear models that do not permit interaction of the predictor variables (global T, synoptic-scale meteorological conditions and text">SM). This is particularly marked in regions with high variability in minimum and maximum θe, where
Statistical Models for Social Networks
Snijders, Tom A. B.; Cook, KS; Massey, DS
2011-01-01
Statistical models for social networks as dependent variables must represent the typical network dependencies between tie variables such as reciprocity, homophily, transitivity, etc. This review first treats models for single (cross-sectionally observed) networks and then for network dynamics. For
Hot metal temperature prediction by neural networks in the blast furnace
International Nuclear Information System (INIS)
Cantera, C.; Jimenez, J.; Varela, I.; Formoso, A.
2002-01-01
Based on a simplified model, the underlying temperature criteria is proposed as a method to study the temperature trends in a blast furnace. As an application, a neural network able to forecast hot metal temperatures from 2 to 16 h in advance (with decreasing precision) has been built. This neural network has been designed to work at real time in a production plant. (Author)
With the advent of commercial software applications, it is now easy to develop neural network models for predictive microbiology applications. However, different versions of the model may be required to meet the divergent needs of model users. In the current study, the commercial software applicat...
Coevolutionary modeling in network formation
Al-Shyoukh, Ibrahim
2014-12-03
Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.
Coevolutionary modeling in network formation
Al-Shyoukh, Ibrahim; Chasparis, Georgios; Shamma, Jeff S.
2014-01-01
Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.
Modeling online social signed networks
Li, Le; Gu, Ke; Zeng, An; Fan, Ying; Di, Zengru
2018-04-01
People's online rating behavior can be modeled by user-object bipartite networks directly. However, few works have been devoted to reveal the hidden relations between users, especially from the perspective of signed networks. We analyze the signed monopartite networks projected by the signed user-object bipartite networks, finding that the networks are highly clustered with obvious community structure. Interestingly, the positive clustering coefficient is remarkably higher than the negative clustering coefficient. Then, a Signed Growing Network model (SGN) based on local preferential attachment is proposed to generate a user's signed network that has community structure and high positive clustering coefficient. Other structural properties of the modeled networks are also found to be similar to the empirical networks.
A neighbourhood evolving network model
International Nuclear Information System (INIS)
Cao, Y.J.; Wang, G.Z.; Jiang, Q.Y.; Han, Z.X.
2006-01-01
Many social, technological, biological and economical systems are best described by evolved network models. In this short Letter, we propose and study a new evolving network model. The model is based on the new concept of neighbourhood connectivity, which exists in many physical complex networks. The statistical properties and dynamics of the proposed model is analytically studied and compared with those of Barabasi-Albert scale-free model. Numerical simulations indicate that this network model yields a transition between power-law and exponential scaling, while the Barabasi-Albert scale-free model is only one of its special (limiting) cases. Particularly, this model can be used to enhance the evolving mechanism of complex networks in the real world, such as some social networks development
Prediction of Austenite Formation Temperatures Using Artificial Neural Networks
International Nuclear Information System (INIS)
Schulze, P; Schmidl, E; Grund, T; Lampke, T
2016-01-01
For the modeling and design of heat treatments, in consideration of the development/ transformation of the microstructure, different material data depending on the chemical composition, the respective microstructure/phases and the temperature are necessary. Material data are, e.g. the thermal conductivity, heat capacity, thermal expansion and transformation data etc. The quality of thermal simulations strongly depends on the accuracy of the material data. For many materials, the required data - in particular for different microstructures and temperatures - are rare in the literature. In addition, a different chemical composition within the permitted limits of the considered steel alloy cannot be predicted. A solution for this problem is provided by the calculation of material data using Artificial Neural Networks (ANN). In the present study, the start and finish temperatures of the transformation from the bcc lattice to the fcc lattice structure of hypoeutectoid steels are calculated using an Artificial Neural Network. An appropriate database containing different transformation temperatures (austenite formation temperatures) to train the ANN is selected from the literature. In order to find a suitable feedforward network, the network topologies as well as the activation functions of the hidden layers are varied and subsequently evaluated in terms of the prediction accuracy. The transformation temperatures calculated by the ANN exhibit a very good compliance compared to the experimental data. The results show that the prediction performance is even higher compared to classical empirical equations such as Andrews or Brandis. Therefore, it can be assumed that the presented ANN is a convenient tool to distinguish between bcc and fcc phases in hypoeutectoid steels. (paper)
Prediction of Austenite Formation Temperatures Using Artificial Neural Networks
Schulze, P.; Schmidl, E.; Grund, T.; Lampke, T.
2016-03-01
For the modeling and design of heat treatments, in consideration of the development/ transformation of the microstructure, different material data depending on the chemical composition, the respective microstructure/phases and the temperature are necessary. Material data are, e.g. the thermal conductivity, heat capacity, thermal expansion and transformation data etc. The quality of thermal simulations strongly depends on the accuracy of the material data. For many materials, the required data - in particular for different microstructures and temperatures - are rare in the literature. In addition, a different chemical composition within the permitted limits of the considered steel alloy cannot be predicted. A solution for this problem is provided by the calculation of material data using Artificial Neural Networks (ANN). In the present study, the start and finish temperatures of the transformation from the bcc lattice to the fcc lattice structure of hypoeutectoid steels are calculated using an Artificial Neural Network. An appropriate database containing different transformation temperatures (austenite formation temperatures) to train the ANN is selected from the literature. In order to find a suitable feedforward network, the network topologies as well as the activation functions of the hidden layers are varied and subsequently evaluated in terms of the prediction accuracy. The transformation temperatures calculated by the ANN exhibit a very good compliance compared to the experimental data. The results show that the prediction performance is even higher compared to classical empirical equations such as Andrews or Brandis. Therefore, it can be assumed that the presented ANN is a convenient tool to distinguish between bcc and fcc phases in hypoeutectoid steels.
International Nuclear Information System (INIS)
Bonilla, Jose Ebert; Ramirez, Jairo; Ramirez, Oscar; Leon, Gloria and others
2006-01-01
The meteorological phenomena are factors that affect the economy, especially on a country like Colombia, which sustainability is based highly on agricultural products like corns, potato and flowers, plants of a paramour landscape like Bogota savannah. Among this phenomenon is the extreme minimum temperature (frost damage) it is a result of the non-lineal interactions of many atmospheric phenomena, for all that, frost damage forecast is very hard to accomplish with traditional methods. The approach of the project is to process this time series with an artificial neural network; it generals a now casting forecast on the zone of Mosquera
Energy Technology Data Exchange (ETDEWEB)
Saarinen, Lisa; Boman, Katarina
2012-02-15
The supply temperature of the Uppsala district heating network was optimized using a model-based control strategy. Simulation of the network showed that the supply temperature could be decreased by in average 8 deg and the electricity production of the plants supplying the network could be increased with 2.5 % during the period January- April, giving an extra income of 1.2 MSEK due to increased income from electricity sales
Developing Personal Network Business Models
DEFF Research Database (Denmark)
Saugstrup, Dan; Henten, Anders
2006-01-01
The aim of the paper is to examine the issue of business modeling in relation to personal networks, PNs. The paper builds on research performed on business models in the EU 1ST MAGNET1 project (My personal Adaptive Global NET). The paper presents the Personal Network concept and briefly reports...
Mathematical Modelling Plant Signalling Networks
Muraro, D.; Byrne, H.M.; King, J.R.; Bennett, M.J.
2013-01-01
methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This mathematical analysis of sub-cellular molecular mechanisms paves the way for more
Complex Networks in Psychological Models
Wedemann, R. S.; Carvalho, L. S. A. V. D.; Donangelo, R.
We develop schematic, self-organizing, neural-network models to describe mechanisms associated with mental processes, by a neurocomputational substrate. These models are examples of real world complex networks with interesting general topological structures. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, we propose neural network models to explain development of cortical map structure and dynamics of memory access, and unify different mental processes into a single neurocomputational substrate. Based on our neural network models, neurotic behavior may be understood as an associative memory process in the brain, and the linguistic, symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neural network. The models are illustrated through computer simulations, where we varied dopaminergic modulation and observed the self-organizing emergent patterns at the resulting semantic map, interpreting them as different manifestations of mental functioning, from psychotic through to normal and neurotic behavior, and creativity.
Uniformity factor of temperature difference in heat exchanger networks
International Nuclear Information System (INIS)
Chen, Shang; Cui, Guo-min
2016-01-01
Highlights: • A uniformity factor of temperature (UFTD) is proposed to heat exchanger network (HEN). • A novel stage-wise superstructure with inner utilities is presented based on UFTD. • New model and DE method is combined as an optimization method. • Optimal HEN structures with inner utilities can be obtained with new method. - Abstract: A uniformity factor of temperature difference (UFTD) is proposed and set up to guide the optimization of Heat exchanger network (HEN). At first, the factor is presented to evaluate the whole enhancement of HEN by handling the logical mean temperature difference as two-dimensional discrete temperature field in system. Then, the factor is applied to different HENs, of which the comparison indicates that a more uniform discrete temperature field leads to a lower UFTD which correlated with a better whole enhancement to improve the optimization level of HEN. A novel stage-wise superstructure model where inner utility can be generated is presented for further analysis of correlation between UFTD and the efficiency of HEN, and more optimal HEN structures can be obtained as inner utility added. Inner utility appears to violate the thermodynamic law, but it makes the discrete temperature field more uniform and improves the heat transfer efficiency of the whole HEN, which brings much more profit than the side effect of inner utility. In sum, the UFTD can not only evaluate the optimization level of the HEN, but also be an optimization object to design new HEN with higher efficiency of energy utilization and lower total annual cost.
A model of coauthorship networks
Zhou, Guochang; Li, Jianping; Xie, Zonglin
2017-10-01
A natural way of representing the coauthorship of authors is to use a generalization of graphs known as hypergraphs. A random geometric hypergraph model is proposed here to model coauthorship networks, which is generated by placing nodes on a region of Euclidean space randomly and uniformly, and connecting some nodes if the nodes satisfy particular geometric conditions. Two kinds of geometric conditions are designed to model the collaboration patterns of academic authorities and basic researches respectively. The conditions give geometric expressions of two causes of coauthorship: the authority and similarity of authors. By simulation and calculus, we show that the forepart of the degree distribution of the network generated by the model is mixture Poissonian, and the tail is power-law, which are similar to these of some coauthorship networks. Further, we show more similarities between the generated network and real coauthorship networks: the distribution of cardinalities of hyperedges, high clustering coefficient, assortativity, and small-world property
Telecommunications network modelling, planning and design
Evans, Sharon
2003-01-01
Telecommunication Network Modelling, Planning and Design addresses sophisticated modelling techniques from the perspective of the communications industry and covers some of the major issues facing telecommunications network engineers and managers today. Topics covered include network planning for transmission systems, modelling of SDH transport network structures and telecommunications network design and performance modelling, as well as network costs and ROI modelling and QoS in 3G networks.
Time response of temperature sensors using neural networks
International Nuclear Information System (INIS)
Santos, Roberto Carlos dos
2010-01-01
In a PWR nuclear power plant, the primary coolant temperature and feedwater temperature are measured using RTDs (Resistance Temperature Detectors). These RTDs typically feed the plant's control and safety systems and must, therefore, be very accurate and have good dynamic performance. The response time of RTDs is characterized by a single parameter called the Plunge Time Constant defined as the time it takes the sensor output to achieve 63.2 percent of its final value after a step change in temperature. Nuclear reactor service conditions are difficult to reproduce in the laboratory, and an in-situ test method called LCSR (Loop Current Step Response) test was developed to measure remotely the response time of RTDs. >From this test, the time constant of the sensor is identified by means of the LCSR transformation that involves the dynamic response modal time constants determination using a nodal heat-transfer model. This calculation is not simple and requires specialized personnel. For this reason an Artificial Neural Network has been developed to predict the time constant of RTD from LCSR test transient. It eliminates the transformations involved in the LCSR application. A series of LCSR tests on RTDs generates the response transients of the sensors, the input data of the networks. Plunge tests are used to determine the time constants of the RTDs, the desired output of the ANN, trained using these sets of input/output data. This methodology was firstly applied to theoretical data simulating 10 RTDs with different time constant values, resulting in an average error of about 0.74 %. Experimental data from three different RTDs was used to predict time constant resulting in a maximum error of 3,34 %. The time constants values predicted from ANN were compared with those obtained from traditional way resulting in an average error of about 18 % and that shows the network is able to predict accurately the sensor time constant. (author)
Campus network security model study
Zhang, Yong-ku; Song, Li-ren
2011-12-01
Campus network security is growing importance, Design a very effective defense hacker attacks, viruses, data theft, and internal defense system, is the focus of the study in this paper. This paper compared the firewall; IDS based on the integrated, then design of a campus network security model, and detail the specific implementation principle.
Exergy and Energy Analysis of Low Temperature District Heating Network
DEFF Research Database (Denmark)
Li, Hongwei; Svendsen, Svend
is in line with a pilot project that is carrying out in Denmark with network supply/return temperature at 55oC/25 oC. The consumer domestic hot water (DHW) demand is supplied with a special designed district heating (DH) storage tank. The space heating (SH) demand is supplied with a low temperature radiator......Low temperature district heating (LTDH) with reduced network supply and return temperature provides better match of the low quality building thermal demand and the low quality waste heat supply. In this paper, an exemplary LTDH network was designed for 30 low energy demand residential houses, which....... The network thermal and hydraulic conditions were simulated under steady state with an in-house district heating network design and simulation code. Through simulation, the overall system energetic and exergetic efficiencies were calculated and the exergy losses for the major district heating system...
Generalized Network Psychometrics : Combining Network and Latent Variable Models
Epskamp, S.; Rhemtulla, M.; Borsboom, D.
2017-01-01
We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between
Neural network modeling of emotion
Levine, Daniel S.
2007-03-01
This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.
Modeling of fluctuating reaction networks
International Nuclear Information System (INIS)
Lipshtat, A.; Biham, O.
2004-01-01
Full Text:Various dynamical systems are organized as reaction networks, where the population size of one component affects the populations of all its neighbors. Such networks can be found in interstellar surface chemistry, cell biology, thin film growth and other systems. I cases where the populations of reactive species are large, the network can be modeled by rate equations which provide all reaction rates within mean field approximation. However, in small systems that are partitioned into sub-micron size, these populations strongly fluctuate. Under these conditions rate equations fail and the master equation is needed for modeling these reactions. However, the number of equations in the master equation grows exponentially with the number of reactive species, severely limiting its feasibility for complex networks. Here we present a method which dramatically reduces the number of equations, thus enabling the incorporation of the master equation in complex reaction networks. The method is examplified in the context of reaction network on dust grains. Its applicability for genetic networks will be discussed. 1. Efficient simulations of gas-grain chemistry in interstellar clouds. Azi Lipshtat and Ofer Biham, Phys. Rev. Lett. 93 (2004), 170601. 2. Modeling of negative autoregulated genetic networks in single cells. Azi Lipshtat, Hagai B. Perets, Nathalie Q. Balaban and Ofer Biham, Gene: evolutionary genomics (2004), In press
Moustris, Konstantinos; Tsiros, Ioannis X; Tseliou, Areti; Nastos, Panagiotis
2018-04-11
The present study deals with the development and application of artificial neural network models (ANNs) to estimate the values of a complex human thermal comfort-discomfort index associated with urban heat and cool island conditions inside various urban clusters using as only inputs air temperature data from a standard meteorological station. The index used in the study is the Physiologically Equivalent Temperature (PET) index which requires as inputs, among others, air temperature, relative humidity, wind speed, and radiation (short- and long-wave components). For the estimation of PET hourly values, ANN models were developed, appropriately trained, and tested. Model results are compared to values calculated by the PET index based on field monitoring data for various urban clusters (street, square, park, courtyard, and gallery) in the city of Athens (Greece) during an extreme hot weather summer period. For the evaluation of the predictive ability of the developed ANN models, several statistical evaluation indices were applied: the mean bias error, the root mean square error, the index of agreement, the coefficient of determination, the true predictive rate, the false alarm rate, and the Success Index. According to the results, it seems that ANNs present a remarkable ability to estimate hourly PET values within various urban clusters using only hourly values of air temperature. This is very important in cases where the human thermal comfort-discomfort conditions have to be analyzed and the only available parameter is air temperature.
Moustris, Konstantinos; Tsiros, Ioannis X.; Tseliou, Areti; Nastos, Panagiotis
2018-04-01
The present study deals with the development and application of artificial neural network models (ANNs) to estimate the values of a complex human thermal comfort-discomfort index associated with urban heat and cool island conditions inside various urban clusters using as only inputs air temperature data from a standard meteorological station. The index used in the study is the Physiologically Equivalent Temperature (PET) index which requires as inputs, among others, air temperature, relative humidity, wind speed, and radiation (short- and long-wave components). For the estimation of PET hourly values, ANN models were developed, appropriately trained, and tested. Model results are compared to values calculated by the PET index based on field monitoring data for various urban clusters (street, square, park, courtyard, and gallery) in the city of Athens (Greece) during an extreme hot weather summer period. For the evaluation of the predictive ability of the developed ANN models, several statistical evaluation indices were applied: the mean bias error, the root mean square error, the index of agreement, the coefficient of determination, the true predictive rate, the false alarm rate, and the Success Index. According to the results, it seems that ANNs present a remarkable ability to estimate hourly PET values within various urban clusters using only hourly values of air temperature. This is very important in cases where the human thermal comfort-discomfort conditions have to be analyzed and the only available parameter is air temperature.
Energy and exergy analysis of low temperature district heating network
International Nuclear Information System (INIS)
Li, Hongwei; Svendsen, Svend
2012-01-01
Low temperature district heating with reduced network supply and return temperature provides better match of the low quality building heating demand and the low quality heating supply from waste heat or renewable energy. In this paper, a hypothetical low temperature district heating network is designed to supply heating for 30 low energy detached residential houses. The network operational supply/return temperature is set as 55 °C/25 °C, which is in line with a pilot project carried out in Denmark. Two types of in-house substations are analyzed to supply the consumer domestic hot water demand. The space heating demand is supplied through floor heating in the bathroom and low temperature radiators in the rest of rooms. The network thermal and hydraulic conditions are simulated under steady state. A district heating network design and simulation code is developed to incorporate the network optimization procedure and the network simultaneous factor. Through the simulation, the overall system energy and exergy efficiencies are calculated and the exergy losses for the major district heating system components are identified. Based on the results, suggestions are given to further reduce the system energy/exergy losses and increase the quality match between the consumer heating demand and the district heating supply. -- Highlights: ► Exergy and energy analysis for low and medium temperature district heating systems. ► Different district heating network dimensioning methods are analyzed. ► Major exergy losses are identified in the district heating network and the in-house substations. ► Advantages to apply low temperature district heating are highlighted through exergy analysis. ► The influence of thermal by-pass on system exergy/energy performance is analyzed.
Network model of security system
Directory of Open Access Journals (Sweden)
Adamczyk Piotr
2016-01-01
Full Text Available The article presents the concept of building a network security model and its application in the process of risk analysis. It indicates the possibility of a new definition of the role of the network models in the safety analysis. Special attention was paid to the development of the use of an algorithm describing the process of identifying the assets, vulnerability and threats in a given context. The aim of the article is to present how this algorithm reduced the complexity of the problem by eliminating from the base model these components that have no links with others component and as a result and it was possible to build a real network model corresponding to reality.
Current approaches to gene regulatory network modelling
Directory of Open Access Journals (Sweden)
Brazma Alvis
2007-09-01
Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.
Target-Centric Network Modeling
DEFF Research Database (Denmark)
Mitchell, Dr. William L.; Clark, Dr. Robert M.
In Target-Centric Network Modeling: Case Studies in Analyzing Complex Intelligence Issues, authors Robert Clark and William Mitchell take an entirely new approach to teaching intelligence analysis. Unlike any other book on the market, it offers case study scenarios using actual intelligence...... reporting formats, along with a tested process that facilitates the production of a wide range of analytical products for civilian, military, and hybrid intelligence environments. Readers will learn how to perform the specific actions of problem definition modeling, target network modeling......, and collaborative sharing in the process of creating a high-quality, actionable intelligence product. The case studies reflect the complexity of twenty-first century intelligence issues by dealing with multi-layered target networks that cut across political, economic, social, technological, and military issues...
Smooth Information Flow in Temperature Climate Network Reflects Mass Transport
Czech Academy of Sciences Publication Activity Database
Hlinka, Jaroslav; Jajcay, Nikola; Hartman, David; Paluš, Milan
2017-01-01
Roč. 27, č. 3 (2017), č. článku 035811. ISSN 1054-1500 R&D Projects: GA ČR GCP103/11/J068; GA MŠk LH14001 Institutional support: RVO:67985807 Keywords : directed network * causal network * Granger causality * climate network * information flow * temperature network Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 2.283, year: 2016
Application of Wireless Sensor Networks for Indoor Temperature Regulation
DEFF Research Database (Denmark)
Stojkoska, Biljana Risteska; Popovska Avramova, Andrijana; Chatzimisios, Periklis
2014-01-01
Wireless sensor networks take a major part in our everyday lives by enhancing systems for home automation, healthcare, temperature control, energy consumption monitoring, and so forth. In this paper we focus on a system used for temperature regulation for residential, educational, industrial...... energy savings by reducing the amount of data transmissions through the network. Furthermore, the framework explores techniques for localization, such that the location of the nodes can be used by algorithms that regulate temperature settings......., and commercial premises, and so forth. We propose a framework for indoor temperature regulation and optimization using wireless sensor networks based on ZigBee platform. This paper considers architectural design of the system, as well as implementation guidelines. The proposed system favors methods that provide...
Energy and exergy analysis of low temperature district heating network
DEFF Research Database (Denmark)
Li, Hongwei; Svendsen, Svend
2012-01-01
is designed to supply heating for 30 low energy detached residential houses. The network operational supply/return temperature is set as 55 °C/25 °C, which is in line with a pilot project carried out in Denmark. Two types of in-house substations are analyzed to supply the consumer domestic hot water demand...... energy/exergy losses and increase the quality match between the consumer heating demand and the district heating supply.......Low temperature district heating with reduced network supply and return temperature provides better match of the low quality building heating demand and the low quality heating supply from waste heat or renewable energy. In this paper, a hypothetical low temperature district heating network...
Application and Simulation of Fuzzy Neural Network PID Controller in the Aircraft Cabin Temperature
Directory of Open Access Journals (Sweden)
Ding Fang
2013-06-01
Full Text Available Considering complex factors of affecting ambient temperature in Aircraft cabin, and some shortages of traditional PID control like the parameters difficult to be tuned and control ineffective, this paper puts forward the intelligent PID algorithm that makes fuzzy logic method and neural network together, scheming out the fuzzy neural net PID controller. After the correction of the fuzzy inference and dynamic learning of neural network, PID parameters of the controller get the optimal parameters. MATLAB simulation results of the cabin temperature control model show that the performance of the fuzzy neural network PID controller has been greatly improved, with faster response, smaller overshoot and better adaptability.
Wireless Sensor Networks Framework for Indoor Temperature Regulation
DEFF Research Database (Denmark)
Stojkoska, Biljana; Popovska Avramova, Andrijana
2013-01-01
Wireless Sensor Networks take a major part in our everyday lives by enhancing systems for home automation, health-care, temperature control, energy consumption monitoring etc. In this paper we focus on a system used for temperature regulation for homes, educational, industrial, commercial premises...... etc. We propose a framework for indoor regulation and optimization of temperature using wireless sensor networks based on ZigBee. Methods for optimal temperature regulation are suggested and discussed. The framework is based on methods that provide energy savings by reducing the amount of data...... transmissions through prediction methods. Additionally the framework explores techniques for localization, such that the location of the nodes is used for optimization of the temperature settings. Information on node location is used to provide the most optimal tradeo between the time it takes to reach...
Continuum Model for River Networks
Giacometti, Achille; Maritan, Amos; Banavar, Jayanth R.
1995-07-01
The effects of erosion, avalanching, and random precipitation are captured in a simple stochastic partial differential equation for modeling the evolution of river networks. Our model leads to a self-organized structured landscape and to abstraction and piracy of the smaller tributaries as the evolution proceeds. An algebraic distribution of the average basin areas and a power law relationship between the drainage basin area and the river length are found.
Biological transportation networks: Modeling and simulation
Albi, Giacomo
2015-09-15
We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation and angiogenesis) and ion transportation networks (e.g., neural networks) is explained in detail and basic analytical features like the gradient flow structure of the fluid transportation network model and the impact of the model parameters on the geometry and topology of network formation are analyzed. We also present a numerical finite-element based discretization scheme and discuss sample cases of network formation simulations.
Network modelling methods for FMRI.
Smith, Stephen M; Miller, Karla L; Salimi-Khorshidi, Gholamreza; Webster, Matthew; Beckmann, Christian F; Nichols, Thomas E; Ramsey, Joseph D; Woolrich, Mark W
2011-01-15
There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution. Copyright © 2010 Elsevier Inc. All rights reserved.
Artificial neural networks in prediction of mechanical behavior of concrete at high temperature
International Nuclear Information System (INIS)
Mukherjee, A.; Nag Biswas, S.
1997-01-01
The behavior of concrete structures that are exposed to extreme thermo-mechanical loading is an issue of great importance in nuclear engineering. The mechanical behavior of concrete at high temperature is non-linear. The properties that regulate its response are highly temperature dependent and extremely complex. In addition, the constituent materials, e.g. aggregates, influence the response significantly. Attempts have been made to trace the stress-strain curve through mathematical models and rheological models. However, it has been difficult to include all the contributing factors in the mathematical model. This paper examines a new programming paradigm, artificial neural networks, for the problem. Implementing a feedforward network and backpropagation algorithm the stress-strain relationship of the material is captured. The neural networks for the prediction of uniaxial behavior of concrete at high temperature has been presented here. The results of the present investigation are very encouraging. (orig.)
Estimation of dew point temperature using neuro-fuzzy and neural network techniques
Kisi, Ozgur; Kim, Sungwon; Shiri, Jalal
2013-11-01
This study investigates the ability of two different artificial neural network (ANN) models, generalized regression neural networks model (GRNNM) and Kohonen self-organizing feature maps neural networks model (KSOFM), and two different adaptive neural fuzzy inference system (ANFIS) models, ANFIS model with sub-clustering identification (ANFIS-SC) and ANFIS model with grid partitioning identification (ANFIS-GP), for estimating daily dew point temperature. The climatic data that consisted of 8 years of daily records of air temperature, sunshine hours, wind speed, saturation vapor pressure, relative humidity, and dew point temperature from three weather stations, Daego, Pohang, and Ulsan, in South Korea were used in the study. The estimates of ANN and ANFIS models were compared according to the three different statistics, root mean square errors, mean absolute errors, and determination coefficient. Comparison results revealed that the ANFIS-SC, ANFIS-GP, and GRNNM models showed almost the same accuracy and they performed better than the KSOFM model. Results also indicated that the sunshine hours, wind speed, and saturation vapor pressure have little effect on dew point temperature. It was found that the dew point temperature could be successfully estimated by using T mean and R H variables.
Research on the model of home networking
Yun, Xiang; Feng, Xiancheng
2007-11-01
It is the research hotspot of current broadband network to combine voice service, data service and broadband audio-video service by IP protocol to transport various real time and mutual services to terminal users (home). Home Networking is a new kind of network and application technology which can provide various services. Home networking is called as Digital Home Network. It means that PC, home entertainment equipment, home appliances, Home wirings, security, illumination system were communicated with each other by some composing network technology, constitute a networking internal home, and connect with WAN by home gateway. It is a new network technology and application technology, and can provide many kinds of services inside home or between homes. Currently, home networking can be divided into three kinds: Information equipment, Home appliances, Communication equipment. Equipment inside home networking can exchange information with outer networking by home gateway, this information communication is bidirectional, user can get information and service which provided by public networking by using home networking internal equipment through home gateway connecting public network, meantime, also can get information and resource to control the internal equipment which provided by home networking internal equipment. Based on the general network model of home networking, there are four functional entities inside home networking: HA, HB, HC, and HD. (1) HA (Home Access) - home networking connects function entity; (2) HB (Home Bridge) Home networking bridge connects function entity; (3) HC (Home Client) - Home networking client function entity; (4) HD (Home Device) - decoder function entity. There are many physical ways to implement four function entities. Based on theses four functional entities, there are reference model of physical layer, reference model of link layer, reference model of IP layer and application reference model of high layer. In the future home network
Mathematical Modelling Plant Signalling Networks
Muraro, D.
2013-01-01
During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This mathematical analysis of sub-cellular molecular mechanisms paves the way for more comprehensive modelling studies of hormonal transport and signalling in a multi-scale setting. © EDP Sciences, 2013.
Energy modelling in sensor networks
Schmidt, D.; Krämer, M.; Kuhn, T.; Wehn, N.
2007-06-01
Wireless sensor networks are one of the key enabling technologies for the vision of ambient intelligence. Energy resources for sensor nodes are very scarce. A key challenge is the design of energy efficient communication protocols. Models of the energy consumption are needed to accurately simulate the efficiency of a protocol or application design, and can also be used for automatic energy optimizations in a model driven design process. We propose a novel methodology to create models for sensor nodes based on few simple measurements. In a case study the methodology was used to create models for MICAz nodes. The models were integrated in a simulation environment as well as in a SDL runtime framework of a model driven design process. Measurements on a test application that was created automatically from an SDL specification showed an 80% reduction in energy consumption compared to an implementation without power saving strategies.
Network performance of a wireless sensor network for temperature monitoring in vineyards
DEFF Research Database (Denmark)
Liscano, Ramiro; Jacoub, John Khalil; Dersingh, Anand
2011-01-01
Wireless sensor networks (WSNs) are an emerging technology which can be used for outdoor environmental monitoring. This paper presents challenges that arose from the development and deployment of a WSN for environmental monitoring as well as network performance analysis of this network. Different...... components in our sensor network architecture are presented like the physical nodes, the sensor node code, and two messaging protocols; one for collecting sensor and network values and the other for sensor node commands. An information model for sensor nodes to support plug-and-play capabilities in sensor...... networks is also presented....
Biological transportation networks: Modeling and simulation
Albi, Giacomo; Artina, Marco; Foransier, Massimo; Markowich, Peter A.
2015-01-01
We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation
Short-term PV/T module temperature prediction based on PCA-RBF neural network
Li, Jiyong; Zhao, Zhendong; Li, Yisheng; Xiao, Jing; Tang, Yunfeng
2018-02-01
Aiming at the non-linearity and large inertia of temperature control in PV/T system, short-term temperature prediction of PV/T module is proposed, to make the PV/T system controller run forward according to the short-term forecasting situation to optimize control effect. Based on the analysis of the correlation between PV/T module temperature and meteorological factors, and the temperature of adjacent time series, the principal component analysis (PCA) method is used to pre-process the original input sample data. Combined with the RBF neural network theory, the simulation results show that the PCA method makes the prediction accuracy of the network model higher and the generalization performance stronger than that of the RBF neural network without the main component extraction.
Depression of Glass Transition Temperatures of Polymer Networks by Diluents
Brinke, Gerrit ten; Karasz, Frank E.; Ellis, Thomas S.
1983-01-01
A classical thermodynamic theory is used to derive expressions for the depression of the glass transition temperature Tg of a polymer network by a diluent. The enhanced sensitivity of Tg in cross-linked systems to small amounts of diluent is explained. Predictions of the theory are in satisfactory
An evolving network model with community structure
International Nuclear Information System (INIS)
Li Chunguang; Maini, Philip K
2005-01-01
Many social and biological networks consist of communities-groups of nodes within which connections are dense, but between which connections are sparser. Recently, there has been considerable interest in designing algorithms for detecting community structures in real-world complex networks. In this paper, we propose an evolving network model which exhibits community structure. The network model is based on the inner-community preferential attachment and inter-community preferential attachment mechanisms. The degree distributions of this network model are analysed based on a mean-field method. Theoretical results and numerical simulations indicate that this network model has community structure and scale-free properties
Brand Marketing Model on Social Networks
Directory of Open Access Journals (Sweden)
Jolita Jezukevičiūtė
2014-04-01
Full Text Available The paper analyzes the brand and its marketing solutions onsocial networks. This analysis led to the creation of improvedbrand marketing model on social networks, which will contributeto the rapid and cheap organization brand recognition, increasecompetitive advantage and enhance consumer loyalty. Therefore,the brand and a variety of social networks are becoming a hotresearch area for brand marketing model on social networks.The world‘s most successful brand marketing models exploratoryanalysis of a single case study revealed a brand marketingsocial networking tools that affect consumers the most. Basedon information analysis and methodological studies, develop abrand marketing model on social networks.
Energy Technology Data Exchange (ETDEWEB)
Du, Zhimin; Jin, Xinqiao; Yang, Yunyu [School of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai (China)
2009-09-15
Wavelet neural network, the integration of wavelet analysis and neural network, is presented to diagnose the faults of sensors including temperature, flow rate and pressure in variable air volume (VAV) systems to ensure well capacity of energy conservation. Wavelet analysis is used to process the original data collected from the building automation first. With three-level wavelet decomposition, the series of characteristic information representing various operation conditions of the system are obtained. In addition, neural network is developed to diagnose the source of the fault. To improve the diagnosis efficiency, three data groups based on several physical models or balances are classified and constructed. Using the data decomposed by three-level wavelet, the neural network can be well trained and series of convergent networks are obtained. Finally, the new measurements to diagnose are similarly processed by wavelet. And the well-trained convergent neural networks are used to identify the operation condition and isolate the source of the fault. (author)
A novel Direct Small World network model
Directory of Open Access Journals (Sweden)
LIN Tao
2016-10-01
Full Text Available There is a certain degree of redundancy and low efficiency of existing computer networks.This paper presents a novel Direct Small World network model in order to optimize networks.In this model,several nodes construct a regular network.Then,randomly choose and replot some nodes to generate Direct Small World network iteratively.There is no change in average distance and clustering coefficient.However,the network performance,such as hops,is improved.The experiments prove that compared to traditional small world network,the degree,average of degree centrality and average of closeness centrality are lower in Direct Small World network.This illustrates that the nodes in Direct Small World networks are closer than Watts-Strogatz small world network model.The Direct Small World can be used not only in the communication of the community information,but also in the research of epidemics.
Correlation Models for Temperature Fields
North, Gerald R.
2011-05-16
This paper presents derivations of some analytical forms for spatial correlations of evolving random fields governed by a white-noise-driven damped diffusion equation that is the analog of autoregressive order 1 in time and autoregressive order 2 in space. The study considers the two-dimensional plane and the surface of a sphere, both of which have been studied before, but here time is introduced to the problem. Such models have a finite characteristic length (roughly the separation at which the autocorrelation falls to 1/e) and a relaxation time scale. In particular, the characteristic length of a particular temporal Fourier component of the field increases to a finite value as the frequency of the particular component decreases. Some near-analytical formulas are provided for the results. A potential application is to the correlation structure of surface temperature fields and to the estimation of large area averages, depending on how the original datastream is filtered into a distribution of Fourier frequencies (e.g., moving average, low pass, or narrow band). The form of the governing equation is just that of the simple energy balance climate models, which have a long history in climate studies. The physical motivation provided by the derivation from a climate model provides some heuristic appeal to the approach and suggests extensions of the work to nonuniform cases.
Correlation Models for Temperature Fields
North, Gerald R.; Wang, Jue; Genton, Marc G.
2011-01-01
This paper presents derivations of some analytical forms for spatial correlations of evolving random fields governed by a white-noise-driven damped diffusion equation that is the analog of autoregressive order 1 in time and autoregressive order 2 in space. The study considers the two-dimensional plane and the surface of a sphere, both of which have been studied before, but here time is introduced to the problem. Such models have a finite characteristic length (roughly the separation at which the autocorrelation falls to 1/e) and a relaxation time scale. In particular, the characteristic length of a particular temporal Fourier component of the field increases to a finite value as the frequency of the particular component decreases. Some near-analytical formulas are provided for the results. A potential application is to the correlation structure of surface temperature fields and to the estimation of large area averages, depending on how the original datastream is filtered into a distribution of Fourier frequencies (e.g., moving average, low pass, or narrow band). The form of the governing equation is just that of the simple energy balance climate models, which have a long history in climate studies. The physical motivation provided by the derivation from a climate model provides some heuristic appeal to the approach and suggests extensions of the work to nonuniform cases.
RMBNToolbox: random models for biochemical networks
Directory of Open Access Journals (Sweden)
Niemi Jari
2007-05-01
Full Text Available Abstract Background There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models. Results We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language. Conclusion While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.
Neuro-models for discharge air temperature system
International Nuclear Information System (INIS)
Zaheer-uddin, M.; Tudoroiu, N.
2004-01-01
Nonlinear neuro-models for a discharge air temperature (DAT) system are developed. Experimental data gathered in a heating ventilating and air conditioning (HVAC) test facility is used to develop multi-input multi-output (MIMO) and single-input single-output (SISO) neuro-models. Several different network architectures were explored to build the models. Results show that a three layer second order neural network structure is necessary to achieve good accuracy of the predictions. Results from the developed models are compared, and some observations on sensitivity and standard deviation errors are presented
Brand Marketing Model on Social Networks
Jolita Jezukevičiūtė; Vida Davidavičienė
2014-01-01
The paper analyzes the brand and its marketing solutions onsocial networks. This analysis led to the creation of improvedbrand marketing model on social networks, which will contributeto the rapid and cheap organization brand recognition, increasecompetitive advantage and enhance consumer loyalty. Therefore,the brand and a variety of social networks are becoming a hotresearch area for brand marketing model on social networks.The world‘s most successful brand marketing models exploratoryanalys...
Brand marketing model on social networks
Jezukevičiūtė, Jolita; Davidavičienė, Vida
2014-01-01
Paper analyzes the brand and its marketing solutions on social networks. This analysis led to the creation of improved brand marketing model on social networks, which will contribute to the rapid and cheap organization brand recognition, increase competitive advantage and enhance consumer loyalty. Therefore, the brand and a variety of social networks are becoming a hot research area for brand marketing model on social networks. The world‘s most successful brand marketing models exploratory an...
Network Bandwidth Utilization Forecast Model on High Bandwidth Network
Energy Technology Data Exchange (ETDEWEB)
Yoo, Wucherl; Sim, Alex
2014-07-07
With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.
Network bandwidth utilization forecast model on high bandwidth networks
Energy Technology Data Exchange (ETDEWEB)
Yoo, Wuchert (William) [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2015-03-30
With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.
Energy Technology Data Exchange (ETDEWEB)
Linan Garcia, Roberto; Ponce Noyola, David; Guzman Lopez, Arali [Instituto de Investigaciones Electricas, Cuernavaca, Morelos (Mexico); Betancourt Ramirez, Enrique; Tamez Torres, Gerardo [PROLEC GE, (Mexico)
2013-03-01
This paper presents the development of two experimental models in order to ensure reliable operation of power transformers under emergency overload conditions. The first model estimates the moisture distribution in the transformer windings, while the second model estimates the safe operating temperature and time before steam bubbles generation presents. Additionally, an electronic device was designed and built, using the models developed, in order to monitor in real time both parameters. This device allows a more reliable operation of the transmission network, considering the transformers condition. [Spanish] Este documento presenta el desarrollo de dos modelos experimentales para asegurar la operacion confiable de transformadores de potencia bajo condiciones de sobrecarga de emergencia. El primer modelo estima la temperatura de operacion segura y el tiempo antes de que ocurra la generacion de burbujas de vapor. Ademas, se diseno y construyo un dispositivo electronico, usando los modelos desarrollados, con el fin de monitorear en tiempo real ambos parametros. Este dispositivo permite una operacion mas confiable de la red de transmision, considerando la condicion de los transformadores.
An acoustical model based monitoring network
Wessels, P.W.; Basten, T.G.H.; Eerden, F.J.M. van der
2010-01-01
In this paper the approach for an acoustical model based monitoring network is demonstrated. This network is capable of reconstructing a noise map, based on the combination of measured sound levels and an acoustic model of the area. By pre-calculating the sound attenuation within the network the
Spinal Cord Injury Model System Information Network
... the UAB-SCIMS More The UAB-SCIMS Information Network The University of Alabama at Birmingham Spinal Cord Injury Model System (UAB-SCIMS) maintains this Information Network as a resource to promote knowledge in the ...
Eight challenges for network epidemic models
Directory of Open Access Journals (Sweden)
Lorenzo Pellis
2015-03-01
Full Text Available Networks offer a fertile framework for studying the spread of infection in human and animal populations. However, owing to the inherent high-dimensionality of networks themselves, modelling transmission through networks is mathematically and computationally challenging. Even the simplest network epidemic models present unanswered questions. Attempts to improve the practical usefulness of network models by including realistic features of contact networks and of host–pathogen biology (e.g. waning immunity have made some progress, but robust analytical results remain scarce. A more general theory is needed to understand the impact of network structure on the dynamics and control of infection. Here we identify a set of challenges that provide scope for active research in the field of network epidemic models.
Entropy Characterization of Random Network Models
Directory of Open Access Journals (Sweden)
Pedro J. Zufiria
2017-06-01
Full Text Available This paper elaborates on the Random Network Model (RNM as a mathematical framework for modelling and analyzing the generation of complex networks. Such framework allows the analysis of the relationship between several network characterizing features (link density, clustering coefficient, degree distribution, connectivity, etc. and entropy-based complexity measures, providing new insight on the generation and characterization of random networks. Some theoretical and computational results illustrate the utility of the proposed framework.
The model of social crypto-network
Directory of Open Access Journals (Sweden)
Марк Миколайович Орел
2015-06-01
Full Text Available The article presents the theoretical model of social network with the enhanced mechanism of privacy policy. It covers the problems arising in the process of implementing the mentioned type of network. There are presented the methods of solving problems arising in the process of building the social network with privacy policy. It was built a theoretical model of social networks with enhanced information protection methods based on information and communication blocks
Introducing Synchronisation in Deterministic Network Models
DEFF Research Database (Denmark)
Schiøler, Henrik; Jessen, Jan Jakob; Nielsen, Jens Frederik D.
2006-01-01
The paper addresses performance analysis for distributed real time systems through deterministic network modelling. Its main contribution is the introduction and analysis of models for synchronisation between tasks and/or network elements. Typical patterns of synchronisation are presented leading...... to the suggestion of suitable network models. An existing model for flow control is presented and an inherent weakness is revealed and remedied. Examples are given and numerically analysed through deterministic network modelling. Results are presented to highlight the properties of the suggested models...
Comparing various artificial neural network types for water temperature prediction in rivers
Piotrowski, Adam P.; Napiorkowski, Maciej J.; Napiorkowski, Jaroslaw J.; Osuch, Marzena
2015-10-01
A number of methods have been proposed for the prediction of streamwater temperature based on various meteorological and hydrological variables. The present study shows a comparison of few types of data-driven neural networks (multi-layer perceptron, product-units, adaptive-network-based fuzzy inference systems and wavelet neural networks) and nearest neighbour approach for short time streamwater temperature predictions in two natural catchments (mountainous and lowland) located in temperate climate zone, with snowy winters and hot summers. To allow wide applicability of such models, autoregressive inputs are not used and only easily available measurements are considered. Each neural network type is calibrated independently 100 times and the mean, median and standard deviation of the results are used for the comparison. Finally, the ensemble aggregation approach is tested. The results show that simple and popular multi-layer perceptron neural networks are in most cases not outperformed by more complex and advanced models. The choice of neural network is dependent on the way the models are compared. This may be a warning for anyone who wish to promote own models, that their superiority should be verified in different ways. The best results are obtained when mean, maximum and minimum daily air temperatures from the previous days are used as inputs, together with the current runoff and declination of the Sun from two recent days. The ensemble aggregation approach allows reducing the mean square error up to several percent, depending on the case, and noticeably diminishes differences in modelling performance obtained by various neural network types.
Bayesian Network Webserver: a comprehensive tool for biological network modeling.
Ziebarth, Jesse D; Bhattacharya, Anindya; Cui, Yan
2013-11-01
The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). ycui2@uthsc.edu. Supplementary data are available at Bioinformatics online.
Reactor pressure vessel embrittlement: Insights from neural network modelling
Mathew, J.; Parfitt, D.; Wilford, K.; Riddle, N.; Alamaniotis, M.; Chroneos, A.; Fitzpatrick, M. E.
2018-04-01
Irradiation embrittlement of steel pressure vessels is an important consideration for the operation of current and future light water nuclear reactors. In this study we employ an ensemble of artificial neural networks in order to provide predictions of the embrittlement using two literature datasets, one based on US surveillance data and the second from the IVAR experiment. We use these networks to examine trends with input variables and to assess various literature models including compositional effects and the role of flux and temperature. Overall, the networks agree with the existing literature models and we comment on their more general use in predicting irradiation embrittlement.
Dynamic Model of High Temperature PEM Fuel Cell Stack Temperature
DEFF Research Database (Denmark)
Andreasen, Søren Juhl; Kær, Søren Knudsen
2007-01-01
cathode air cooled 30 cell HTPEM fuel cell stack developed at the Institute of Energy Technology at Aalborg University. This fuel cell stack uses PEMEAS Celtec P-1000 membranes, runs on pure hydrogen in a dead end anode configuration with a purge valve. The cooling of the stack is managed by running......The present work involves the development of a model for predicting the dynamic temperature of a high temperature PEM (HTPEM) fuel cell stack. The model is developed to test different thermal control strategies before implementing them in the actual system. The test system consists of a prototype...... the stack at a high stoichiometric air flow. This is possible because of the PBI fuel cell membranes used, and the very low pressure drop in the stack. The model consists of a discrete thermal model dividing the stack into three parts: inlet, middle and end and predicting the temperatures in these three...
How to model wireless mesh networks topology
International Nuclear Information System (INIS)
Sanni, M L; Hashim, A A; Anwar, F; Ali, S; Ahmed, G S M
2013-01-01
The specification of network connectivity model or topology is the beginning of design and analysis in Computer Network researches. Wireless Mesh Networks is an autonomic network that is dynamically self-organised, self-configured while the mesh nodes establish automatic connectivity with the adjacent nodes in the relay network of wireless backbone routers. Researches in Wireless Mesh Networks range from node deployment to internetworking issues with sensor, Internet and cellular networks. These researches require modelling of relationships and interactions among nodes including technical characteristics of the links while satisfying the architectural requirements of the physical network. However, the existing topology generators model geographic topologies which constitute different architectures, thus may not be suitable in Wireless Mesh Networks scenarios. The existing methods of topology generation are explored, analysed and parameters for their characterisation are identified. Furthermore, an algorithm for the design of Wireless Mesh Networks topology based on square grid model is proposed in this paper. The performance of the topology generated is also evaluated. This research is particularly important in the generation of a close-to-real topology for ensuring relevance of design to the intended network and validity of results obtained in Wireless Mesh Networks researches
Model checking mobile ad hoc networks
Ghassemi, Fatemeh; Fokkink, Wan
2016-01-01
Modeling arbitrary connectivity changes within mobile ad hoc networks (MANETs) makes application of automated formal verification challenging. We use constrained labeled transition systems as a semantic model to represent mobility. To model check MANET protocols with respect to the underlying
A network model for characterizing brine channels in sea ice
Lieblappen, Ross M.; Kumar, Deip D.; Pauls, Scott D.; Obbard, Rachel W.
2018-03-01
The brine pore space in sea ice can form complex connected structures whose geometry is critical in the governance of important physical transport processes between the ocean, sea ice, and surface. Recent advances in three-dimensional imaging using X-ray micro-computed tomography have enabled the visualization and quantification of the brine network morphology and variability. Using imaging of first-year sea ice samples at in situ temperatures, we create a new mathematical network model to characterize the topology and connectivity of the brine channels. This model provides a statistical framework where we can characterize the pore networks via two parameters, depth and temperature, for use in dynamical sea ice models. Our approach advances the quantification of brine connectivity in sea ice, which can help investigations of bulk physical properties, such as fluid permeability, that are key in both global and regional sea ice models.
Temperature Calculations in the Coastal Modeling System
2017-04-01
ERDC/CHL CHETN-IV-110 April 2017 Approved for public release; distribution is unlimited . Temperature Calculations in the Coastal Modeling...tide) and river discharge at model boundaries, wave radiation stress, and wind forcing over a model computational domain. Physical processes calculated...calculated in the CMS using the following meteorological parameters: solar radiation, cloud cover, air temperature, wind speed, and surface water temperature
Agent-based modeling and network dynamics
Namatame, Akira
2016-01-01
The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...
The Benefits of Using Dense Temperature Sensor Networks to Monitor Urban Warming
Twine, T. E.; Snyder, P. K.; Kucharik, C. J.; Schatz, J.
2015-12-01
Urban heat islands (UHIs) occur when urban and suburban areas experience temperatures that are elevated relative to their rural surroundings because of differences in the fraction of gray and green infrastructure. Studies have shown that communities most at risk for impacts from climate-related disasters (i.e., lower median incomes, higher poverty, lower education, and minorities) tend to live in the hottest areas of cities. Development of adequate climate adaptation tools for cities relies on knowledge of how temperature varies across space and time. Traditionally, a city's urban heat island has been quantified using near-surface air temperature measurements from a few sites. This methodology assumes (1) that the UHI can be characterized by the difference in air temperature from a small number of points, and (2) that these few points represent the urban and rural signatures of the region. This methodology ignores the rich information that could be gained from measurements across the urban to rural transect. This transect could traverse elevations, water bodies, vegetation fraction, and other land surface properties. Two temperature sensor networks were designed and implemented in the Minneapolis-Saint Paul, MN and Madison, WI metropolitan areas beginning in 2011 and 2012, respectively. Both networks use the same model sensor and record temperature every 15 minutes from ~150 sensors. Data from each network has produced new knowledge of how temperature varies diurnally and seasonally across the cities and how the UHI magnitude is influenced by weather phenomena (e.g., wind, snow cover, heat waves) and land surface characteristics such as proximity to inland lakes. However, the two metropolitan areas differ in size, population, structure, and orientation to water bodies. In addition, the sensor networks were established in very different manners. We describe these differences and present lessons learned from the design and ongoing efforts of these two dense networks
Energy Technology Data Exchange (ETDEWEB)
Cantera, C.; Jimenez, J.; Varela, I.; Formoso, A.
2002-07-01
Based on a simplified model, the underlying temperature criteria is proposed as a method to study the temperature trends in a blast furnace. As an application, a neural network able to forecast hot metal temperatures from 2 to 16 h in advance (with decreasing precision) has been built. This neural network has been designed to work at real time in a production plant. (Author)
Super capacitor modeling with artificial neural network (ANN)
Energy Technology Data Exchange (ETDEWEB)
Marie-Francoise, J.N.; Gualous, H.; Berthon, A. [Universite de Franche-Comte, Lab. en Electronique, Electrotechnique et Systemes (L2ES), UTBM, INRETS (LRE T31) 90 - Belfort (France)
2004-07-01
This paper presents super-capacitors modeling using Artificial Neural Network (ANN). The principle consists on a black box nonlinear multiple inputs single output (MISO) model. The system inputs are temperature and current, the output is the super-capacitor voltage. The learning and the validation of the ANN model from experimental charge and discharge of super-capacitor establish the relationship between inputs and output. The learning and the validation of the ANN model use experimental results of 2700 F, 3700 F and a super-capacitor pack. Once the network is trained, the ANN model can predict the super-capacitor behaviour with temperature variations. The update parameters of the ANN model are performed thanks to Levenberg-Marquardt method in order to minimize the error between the output of the system and the predicted output. The obtained results with the ANN model of super-capacitor and experimental ones are in good agreement. (authors)
Nonparametric Bayesian Modeling of Complex Networks
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Mørup, Morten
2013-01-01
an infinite mixture model as running example, we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model?s fit and predictive performance. We explain how advanced nonparametric models......Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...
Network structure exploration via Bayesian nonparametric models
International Nuclear Information System (INIS)
Chen, Y; Wang, X L; Xiang, X; Tang, B Z; Bu, J Z
2015-01-01
Complex networks provide a powerful mathematical representation of complex systems in nature and society. To understand complex networks, it is crucial to explore their internal structures, also called structural regularities. The task of network structure exploration is to determine how many groups there are in a complex network and how to group the nodes of the network. Most existing structure exploration methods need to specify either a group number or a certain type of structure when they are applied to a network. In the real world, however, the group number and also the certain type of structure that a network has are usually unknown in advance. To explore structural regularities in complex networks automatically, without any prior knowledge of the group number or the certain type of structure, we extend a probabilistic mixture model that can handle networks with any type of structure but needs to specify a group number using Bayesian nonparametric theory. We also propose a novel Bayesian nonparametric model, called the Bayesian nonparametric mixture (BNPM) model. Experiments conducted on a large number of networks with different structures show that the BNPM model is able to explore structural regularities in networks automatically with a stable, state-of-the-art performance. (paper)
Weather Derivatives and Stochastic Modelling of Temperature
Directory of Open Access Journals (Sweden)
Fred Espen Benth
2011-01-01
Full Text Available We propose a continuous-time autoregressive model for the temperature dynamics with volatility being the product of a seasonal function and a stochastic process. We use the Barndorff-Nielsen and Shephard model for the stochastic volatility. The proposed temperature dynamics is flexible enough to model temperature data accurately, and at the same time being analytically tractable. Futures prices for commonly traded contracts at the Chicago Mercantile Exchange on indices like cooling- and heating-degree days and cumulative average temperatures are computed, as well as option prices on them.
Modelling the structure of complex networks
DEFF Research Database (Denmark)
Herlau, Tue
networks has been independently studied as mathematical objects in their own right. As such, there has been both an increased demand for statistical methods for complex networks as well as a quickly growing mathematical literature on the subject. In this dissertation we explore aspects of modelling complex....... The next chapters will treat some of the various symmetries, representer theorems and probabilistic structures often deployed in the modelling complex networks, the construction of sampling methods and various network models. The introductory chapters will serve to provide context for the included written...
Modeling maximum daily temperature using a varying coefficient regression model
Han Li; Xinwei Deng; Dong-Yum Kim; Eric P. Smith
2014-01-01
Relationships between stream water and air temperatures are often modeled using linear or nonlinear regression methods. Despite a strong relationship between water and air temperatures and a variety of models that are effective for data summarized on a weekly basis, such models did not yield consistently good predictions for summaries such as daily maximum temperature...
Building functional networks of spiking model neurons.
Abbott, L F; DePasquale, Brian; Memmesheimer, Raoul-Martin
2016-03-01
Most of the networks used by computer scientists and many of those studied by modelers in neuroscience represent unit activities as continuous variables. Neurons, however, communicate primarily through discontinuous spiking. We review methods for transferring our ability to construct interesting networks that perform relevant tasks from the artificial continuous domain to more realistic spiking network models. These methods raise a number of issues that warrant further theoretical and experimental study.
Load forecasting method considering temperature effect for distribution network
Directory of Open Access Journals (Sweden)
Meng Xiao Fang
2016-01-01
Full Text Available To improve the accuracy of load forecasting, the temperature factor was introduced into the load forecasting in this paper. This paper analyzed the characteristics of power load variation, and researched the rule of the load with the temperature change. Based on the linear regression analysis, the mathematical model of load forecasting was presented with considering the temperature effect, and the steps of load forecasting were given. Used MATLAB, the temperature regression coefficient was calculated. Using the load forecasting model, the full-day load forecasting and time-sharing load forecasting were carried out. By comparing and analyzing the forecast error, the results showed that the error of time-sharing load forecasting method was small in this paper. The forecasting method is an effective method to improve the accuracy of load forecasting.
Modeling, Optimization & Control of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat
2014-01-01
. The nonlinear network model is derived based on the circuit theory. A suitable projection is used to reduce the state vector and to express the model in standard state-space form. Then, the controllability of nonlinear nonaffine hydraulic networks is studied. The Lie algebra-based controllability matrix is used......Water supply systems consist of a number of pumping stations, which deliver water to the customers via pipeline networks and elevated reservoirs. A huge amount of drinking water is lost before it reaches to end-users due to the leakage in pipe networks. A cost effective solution to reduce leakage...... in water network is pressure management. By reducing the pressure in the water network, the leakage can be reduced significantly. Also it reduces the amount of energy consumption in water networks. The primary purpose of this work is to develop control algorithms for pressure control in water supply...
A physically based analytical spatial air temperature and humidity model
Yang, Yang; Endreny, Theodore A.; Nowak, David J.
2013-09-01
Spatial variation of urban surface air temperature and humidity influences human thermal comfort, the settling rate of atmospheric pollutants, and plant physiology and growth. Given the lack of observations, we developed a Physically based Analytical Spatial Air Temperature and Humidity (PASATH) model. The PASATH model calculates spatial solar radiation and heat storage based on semiempirical functions and generates spatially distributed estimates based on inputs of topography, land cover, and the weather data measured at a reference site. The model assumes that for all grids under the same mesoscale climate, grid air temperature and humidity are modified by local variation in absorbed solar radiation and the partitioning of sensible and latent heat. The model uses a reference grid site for time series meteorological data and the air temperature and humidity of any other grid can be obtained by solving the heat flux network equations. PASATH was coupled with the USDA iTree-Hydro water balance model to obtain evapotranspiration terms and run from 20 to 29 August 2010 at a 360 m by 360 m grid scale and hourly time step across a 285 km2 watershed including the urban area of Syracuse, NY. PASATH predictions were tested at nine urban weather stations representing variability in urban topography and land cover. The PASATH model predictive efficiency R2 ranged from 0.81 to 0.99 for air temperature and 0.77 to 0.97 for dew point temperature. PASATH is expected to have broad applications on environmental and ecological models.
Port Hamiltonian modeling of Power Networks
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
Modelling traffic congestion using queuing networks
Indian Academy of Sciences (India)
Flow-density curves; uninterrupted traffic; Jackson networks. ... ness - also suffer from a big handicap vis-a-vis the Indian scenario: most of these models do .... more well-known queuing network models and onsite data, a more exact Road Cell ...
Settings in Social Networks : a Measurement Model
Schweinberger, Michael; Snijders, Tom A.B.
2003-01-01
A class of statistical models is proposed that aims to recover latent settings structures in social networks. Settings may be regarded as clusters of vertices. The measurement model is based on two assumptions. (1) The observed network is generated by hierarchically nested latent transitive
Network interconnections: an architectural reference model
Butscher, B.; Lenzini, L.; Morling, R.; Vissers, C.A.; Popescu-Zeletin, R.; van Sinderen, Marten J.; Heger, D.; Krueger, G.; Spaniol, O.; Zorn, W.
1985-01-01
One of the major problems in understanding the different approaches in interconnecting networks of different technologies is the lack of reference to a general model. The paper develops the rationales for a reference model of network interconnection and focuses on the architectural implications for
Performance modeling of network data services
Energy Technology Data Exchange (ETDEWEB)
Haynes, R.A.; Pierson, L.G.
1997-01-01
Networks at major computational organizations are becoming increasingly complex. The introduction of large massively parallel computers and supercomputers with gigabyte memories are requiring greater and greater bandwidth for network data transfers to widely dispersed clients. For networks to provide adequate data transfer services to high performance computers and remote users connected to them, the networking components must be optimized from a combination of internal and external performance criteria. This paper describes research done at Sandia National Laboratories to model network data services and to visualize the flow of data from source to sink when using the data services.
Continuum Modeling of Biological Network Formation
Albi, Giacomo; Burger, Martin; Haskovec, Jan; Markowich, Peter A.; Schlottbom, Matthias
2017-01-01
We present an overview of recent analytical and numerical results for the elliptic–parabolic system of partial differential equations proposed by Hu and Cai, which models the formation of biological transportation networks. The model describes
Network models in economics and finance
Pardalos, Panos; Rassias, Themistocles
2014-01-01
Using network models to investigate the interconnectivity in modern economic systems allows researchers to better understand and explain some economic phenomena. This volume presents contributions by known experts and active researchers in economic and financial network modeling. Readers are provided with an understanding of the latest advances in network analysis as applied to economics, finance, corporate governance, and investments. Moreover, recent advances in market network analysis that focus on influential techniques for market graph analysis are also examined. Young researchers will find this volume particularly useful in facilitating their introduction to this new and fascinating field. Professionals in economics, financial management, various technologies, and network analysis, will find the network models presented in this book beneficial in analyzing the interconnectivity in modern economic systems.
Synergistic effects in threshold models on networks
Juul, Jonas S.; Porter, Mason A.
2018-01-01
Network structure can have a significant impact on the propagation of diseases, memes, and information on social networks. Different types of spreading processes (and other dynamical processes) are affected by network architecture in different ways, and it is important to develop tractable models of spreading processes on networks to explore such issues. In this paper, we incorporate the idea of synergy into a two-state ("active" or "passive") threshold model of social influence on networks. Our model's update rule is deterministic, and the influence of each meme-carrying (i.e., active) neighbor can—depending on a parameter—either be enhanced or inhibited by an amount that depends on the number of active neighbors of a node. Such a synergistic system models social behavior in which the willingness to adopt either accelerates or saturates in a way that depends on the number of neighbors who have adopted that behavior. We illustrate that our model's synergy parameter has a crucial effect on system dynamics, as it determines whether degree-k nodes are possible or impossible to activate. We simulate synergistic meme spreading on both random-graph models and networks constructed from empirical data. Using a heterogeneous mean-field approximation, which we derive under the assumption that a network is locally tree-like, we are able to determine which synergy-parameter values allow degree-k nodes to be activated for many networks and for a broad family of synergistic models.
Gossip spread in social network Models
Johansson, Tobias
2017-04-01
Gossip almost inevitably arises in real social networks. In this article we investigate the relationship between the number of friends of a person and limits on how far gossip about that person can spread in the network. How far gossip travels in a network depends on two sets of factors: (a) factors determining gossip transmission from one person to the next and (b) factors determining network topology. For a simple model where gossip is spread among people who know the victim it is known that a standard scale-free network model produces a non-monotonic relationship between number of friends and expected relative spread of gossip, a pattern that is also observed in real networks (Lind et al., 2007). Here, we study gossip spread in two social network models (Toivonen et al., 2006; Vázquez, 2003) by exploring the parameter space of both models and fitting them to a real Facebook data set. Both models can produce the non-monotonic relationship of real networks more accurately than a standard scale-free model while also exhibiting more realistic variability in gossip spread. Of the two models, the one given in Vázquez (2003) best captures both the expected values and variability of gossip spread.
Evaluation of EOR Processes Using Network Models
DEFF Research Database (Denmark)
Winter, Anatol; Larsen, Jens Kjell; Krogsbøll, Anette
1998-01-01
The report consists of the following parts: 1) Studies of wetting properties of model fluids and fluid mixtures aimed at an optimal selection of candidates for micromodel experiments. 2) Experimental studies of multiphase transport properties using physical models of porous networks (micromodels......) including estimation of their "petrophysical" properties (e.g. absolute permeability). 3) Mathematical modelling and computer studies of multiphase transport through pore space using mathematical network models. 4) Investigation of link between pore-scale and macroscopic recovery mechanisms....
Towards reproducible descriptions of neuronal network models.
Directory of Open Access Journals (Sweden)
Eilen Nordlie
2009-08-01
Full Text Available Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing--and thinking about--complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain.
Improved Maximum Parsimony Models for Phylogenetic Networks.
Van Iersel, Leo; Jones, Mark; Scornavacca, Celine
2018-05-01
Phylogenetic networks are well suited to represent evolutionary histories comprising reticulate evolution. Several methods aiming at reconstructing explicit phylogenetic networks have been developed in the last two decades. In this article, we propose a new definition of maximum parsimony for phylogenetic networks that permits to model biological scenarios that cannot be modeled by the definitions currently present in the literature (namely, the "hardwired" and "softwired" parsimony). Building on this new definition, we provide several algorithmic results that lay the foundations for new parsimony-based methods for phylogenetic network reconstruction.
Modeling, robust and distributed model predictive control for freeway networks
Liu, S.
2016-01-01
In Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of
DEFF Research Database (Denmark)
Mohammadi, Soma; Bojesen, Carsten
2015-01-01
the temperature in DH systems. The main focus is on modeling transient heat transfer in pipe networks regarding the time delays between the heat supply unit and the consumers, the heat loss in the pipe networks and the consumers’ dynamic heat loads. A pseudo-dynamic approach is adopted and also the implicit...... district heating networks [DHN] characteristics. This paper is presenting a new developed model, which reflects the thermo-dynamic behavior of DHN. It is designed for tree network topologies. The purpose of the model is to serve as a basis for applying a variety of scenarios towards lowering...... finite element method is applied to simulate transient temperature changes in pipe networks. The model is calculating time series data related to supply temperature to the DHN from heat production units, heat loads and return temperature related to each consumer to calculate dynamic temperature changes...
Tool wear modeling using abductive networks
Masory, Oren
1992-09-01
A tool wear model based on Abductive Networks, which consists of a network of `polynomial' nodes, is described. The model relates the cutting parameters, components of the cutting force, and machining time to flank wear. Thus real time measurements of the cutting force can be used to monitor the machining process. The model is obtained by a training process in which the connectivity between the network's nodes and the polynomial coefficients of each node are determined by optimizing a performance criteria. Actual wear measurements of coated and uncoated carbide inserts were used for training and evaluating the established model.
Modelling of virtual production networks
Directory of Open Access Journals (Sweden)
2011-03-01
Full Text Available Nowadays many companies, especially small and medium-sized enterprises (SMEs, specialize in a limited field of production. It requires forming virtual production networks of cooperating enterprises to manufacture better, faster and cheaper. Apart from that, some production orders cannot be realized, because there is not a company of sufficient production potential. In this case the virtual production networks of cooperating companies can realize these production orders. These networks have larger production capacity and many different resources. Therefore it can realize many more production orders together than each of them separately. Such organization allows for executing high quality product. The maintenance costs of production capacity and used resources are not so high. In this paper a methodology of rapid prototyping of virtual production networks is proposed. It allows to execute production orders on time considered existing logistic constraints.
A Network Disruption Modeling Tool
National Research Council Canada - National Science Library
Leinart, James
1998-01-01
Given that network disruption has been identified as a military objective and C2-attack has been identified as the mechanism to accomplish this objective, a target set must be acquired and priorities...
Modeling Epidemics Spreading on Social Contact Networks.
Zhang, Zhaoyang; Wang, Honggang; Wang, Chonggang; Fang, Hua
2015-09-01
Social contact networks and the way people interact with each other are the key factors that impact on epidemics spreading. However, it is challenging to model the behavior of epidemics based on social contact networks due to their high dynamics. Traditional models such as susceptible-infected-recovered (SIR) model ignore the crowding or protection effect and thus has some unrealistic assumption. In this paper, we consider the crowding or protection effect and develop a novel model called improved SIR model. Then, we use both deterministic and stochastic models to characterize the dynamics of epidemics on social contact networks. The results from both simulations and real data set conclude that the epidemics are more likely to outbreak on social contact networks with higher average degree. We also present some potential immunization strategies, such as random set immunization, dominating set immunization, and high degree set immunization to further prove the conclusion.
Spatial Epidemic Modelling in Social Networks
Simoes, Joana Margarida
2005-06-01
The spread of infectious diseases is highly influenced by the structure of the underlying social network. The target of this study is not the network of acquaintances, but the social mobility network: the daily movement of people between locations, in regions. It was already shown that this kind of network exhibits small world characteristics. The model developed is agent based (ABM) and comprehends a movement model and a infection model. In the movement model, some assumptions are made about its structure and the daily movement is decomposed into four types: neighborhood, intra region, inter region and random. The model is Geographical Information Systems (GIS) based, and uses real data to define its geometry. Because it is a vector model, some optimization techniques were used to increase its efficiency.
Implementing network constraints in the EMPS model
Energy Technology Data Exchange (ETDEWEB)
Helseth, Arild; Warland, Geir; Mo, Birger; Fosso, Olav B.
2010-02-15
This report concerns the coupling of detailed market and network models for long-term hydro-thermal scheduling. Currently, the EPF model (Samlast) is the only tool available for this task for actors in the Nordic market. A new prototype for solving the coupled market and network problem has been developed. The prototype is based on the EMPS model (Samkjoeringsmodellen). Results from the market model are distributed to a detailed network model, where a DC load flow detects if there are overloads on monitored lines or intersections. In case of overloads, network constraints are generated and added to the market problem. Theoretical and implementation details for the new prototype are elaborated in this report. The performance of the prototype is tested against the EPF model on a 20-area Nordic dataset. (Author)
Role models for complex networks
Reichardt, J.; White, D. R.
2007-11-01
We present a framework for automatically decomposing (“block-modeling”) the functional classes of agents within a complex network. These classes are represented by the nodes of an image graph (“block model”) depicting the main patterns of connectivity and thus functional roles in the network. Using a first principles approach, we derive a measure for the fit of a network to any given image graph allowing objective hypothesis testing. From the properties of an optimal fit, we derive how to find the best fitting image graph directly from the network and present a criterion to avoid overfitting. The method can handle both two-mode and one-mode data, directed and undirected as well as weighted networks and allows for different types of links to be dealt with simultaneously. It is non-parametric and computationally efficient. The concepts of structural equivalence and modularity are found as special cases of our approach. We apply our method to the world trade network and analyze the roles individual countries play in the global economy.
A physically based model of global freshwater surface temperature
van Beek, Ludovicus P. H.; Eikelboom, Tessa; van Vliet, Michelle T. H.; Bierkens, Marc F. P.
2012-09-01
Temperature determines a range of physical properties of water and exerts a strong control on surface water biogeochemistry. Thus, in freshwater ecosystems the thermal regime directly affects the geographical distribution of aquatic species through their growth and metabolism and indirectly through their tolerance to parasites and diseases. Models used to predict surface water temperature range between physically based deterministic models and statistical approaches. Here we present the initial results of a physically based deterministic model of global freshwater surface temperature. The model adds a surface water energy balance to river discharge modeled by the global hydrological model PCR-GLOBWB. In addition to advection of energy from direct precipitation, runoff, and lateral exchange along the drainage network, energy is exchanged between the water body and the atmosphere by shortwave and longwave radiation and sensible and latent heat fluxes. Also included are ice formation and its effect on heat storage and river hydraulics. We use the coupled surface water and energy balance model to simulate global freshwater surface temperature at daily time steps with a spatial resolution of 0.5° on a regular grid for the period 1976-2000. We opt to parameterize the model with globally available data and apply it without calibration in order to preserve its physical basis with the outlook of evaluating the effects of atmospheric warming on freshwater surface temperature. We validate our simulation results with daily temperature data from rivers and lakes (U.S. Geological Survey (USGS), limited to the USA) and compare mean monthly temperatures with those recorded in the Global Environment Monitoring System (GEMS) data set. Results show that the model is able to capture the mean monthly surface temperature for the majority of the GEMS stations, while the interannual variability as derived from the USGS and NOAA data was captured reasonably well. Results are poorest for
Modeling the interdependent network based on two-mode networks
An, Feng; Gao, Xiangyun; Guan, Jianhe; Huang, Shupei; Liu, Qian
2017-10-01
Among heterogeneous networks, there exist obviously and closely interdependent linkages. Unlike existing research primarily focus on the theoretical research of physical interdependent network model. We propose a two-layer interdependent network model based on two-mode networks to explore the interdependent features in the reality. Specifically, we construct a two-layer interdependent loan network and develop several dependent features indices. The model is verified to enable us to capture the loan dependent features of listed companies based on loan behaviors and shared shareholders. Taking Chinese debit and credit market as case study, the main conclusions are: (1) only few listed companies shoulder the main capital transmission (20% listed companies occupy almost 70% dependent degree). (2) The control of these key listed companies will be more effective of avoiding the spreading of financial risks. (3) Identifying the companies with high betweenness centrality and controlling them could be helpful to monitor the financial risk spreading. (4) The capital transmission channel among Chinese financial listed companies and Chinese non-financial listed companies are relatively strong. However, under greater pressure of demand of capital transmission (70% edges failed), the transmission channel, which constructed by debit and credit behavior, will eventually collapse.
Latent variable models are network models.
Molenaar, Peter C M
2010-06-01
Cramer et al. present an original and interesting network perspective on comorbidity and contrast this perspective with a more traditional interpretation of comorbidity in terms of latent variable theory. My commentary focuses on the relationship between the two perspectives; that is, it aims to qualify the presumed contrast between interpretations in terms of networks and latent variables.
Homophyly/Kinship Model: Naturally Evolving Networks
Li, Angsheng; Li, Jiankou; Pan, Yicheng; Yin, Xianchen; Yong, Xi
2015-10-01
It has been a challenge to understand the formation and roles of social groups or natural communities in the evolution of species, societies and real world networks. Here, we propose the hypothesis that homophyly/kinship is the intrinsic mechanism of natural communities, introduce the notion of the affinity exponent and propose the homophyly/kinship model of networks. We demonstrate that the networks of our model satisfy a number of topological, probabilistic and combinatorial properties and, in particular, that the robustness and stability of natural communities increase as the affinity exponent increases and that the reciprocity of the networks in our model decreases as the affinity exponent increases. We show that both homophyly/kinship and reciprocity are essential to the emergence of cooperation in evolutionary games and that the homophyly/kinship and reciprocity determined by the appropriate affinity exponent guarantee the emergence of cooperation in evolutionary games, verifying Darwin’s proposal that kinship and reciprocity are the means of individual fitness. We propose the new principle of structure entropy minimisation for detecting natural communities of networks and verify the functional module property and characteristic properties by a healthy tissue cell network, a citation network, some metabolic networks and a protein interaction network.
Neural network tagging in a toy model
International Nuclear Information System (INIS)
Milek, Marko; Patel, Popat
1999-01-01
The purpose of this study is a comparison of Artificial Neural Network approach to HEP analysis against the traditional methods. A toy model used in this analysis consists of two types of particles defined by four generic properties. A number of 'events' was created according to the model using standard Monte Carlo techniques. Several fully connected, feed forward multi layered Artificial Neural Networks were trained to tag the model events. The performance of each network was compared to the standard analysis mechanisms and significant improvement was observed
An endogenous model of the credit network
He, Jianmin; Sui, Xin; Li, Shouwei
2016-01-01
In this paper, an endogenous credit network model of firm-bank agents is constructed. The model describes the endogenous formation of firm-firm, firm-bank and bank-bank credit relationships. By means of simulations, the model is capable of showing some obvious similarities with empirical evidence found by other scholars: the upper-tail of firm size distribution can be well fitted with a power-law; the bank size distribution can be lognormally distributed with a power-law tail; the bank in-degrees of the interbank credit network as well as the firm-bank credit network fall into two-power-law distributions.
Modelling and designing electric energy networks
International Nuclear Information System (INIS)
Retiere, N.
2003-11-01
The author gives an overview of his research works in the field of electric network modelling. After a brief overview of technological evolutions from the telegraph to the all-electric fly-by-wire aircraft, he reports and describes various works dealing with a simplified modelling of electric systems and with fractal simulation. Then, he outlines the challenges for the design of electric networks, proposes a design process, gives an overview of various design models, methods and tools, and reports an application in the design of electric networks for future jumbo jets
Temperature Dependent Models of Semiconductor Devices for ...
African Journals Online (AJOL)
The paper presents an investigation of the temperature dependent model of a diode and bipolar transistor built-in to the NAP-2 program and comparison of these models with experimentally measured characteristics of the BA 100 diode and BC 109 transistor. The detail of the modelling technique has been discussed and ...
Queueing Models for Mobile Ad Hoc Networks
de Haan, Roland
2009-01-01
This thesis presents models for the performance analysis of a recent communication paradigm: \\emph{mobile ad hoc networking}. The objective of mobile ad hoc networking is to provide wireless connectivity between stations in a highly dynamic environment. These dynamics are driven by the mobility of
Modeling GMPLS and Optical MPLS Networks
DEFF Research Database (Denmark)
Christiansen, Henrik Lehrmann; Wessing, Henrik
2003-01-01
. The MPLS concept is attractive because it can work as a unifying control structure. covering all technologies. This paper describes how a novel scheme for optical MPLS and circuit switched GMPLS based networks can incorporated in such multi-domain, MPLS-based scenarios and how it could be modeled. Network...
Cyber threat model for tactical radio networks
Kurdziel, Michael T.
2014-05-01
The shift to a full information-centric paradigm in the battlefield has allowed ConOps to be developed that are only possible using modern network communications systems. Securing these Tactical Networks without impacting their capabilities has been a challenge. Tactical networks with fixed infrastructure have similar vulnerabilities to their commercial counterparts (although they need to be secure against adversaries with greater capabilities, resources and motivation). However, networks with mobile infrastructure components and Mobile Ad hoc Networks (MANets) have additional unique vulnerabilities that must be considered. It is useful to examine Tactical Network based ConOps and use them to construct a threat model and baseline cyber security requirements for Tactical Networks with fixed infrastructure, mobile infrastructure and/or ad hoc modes of operation. This paper will present an introduction to threat model assessment. A definition and detailed discussion of a Tactical Network threat model is also presented. Finally, the model is used to derive baseline requirements that can be used to design or evaluate a cyber security solution that can be scaled and adapted to the needs of specific deployments.
Modeling documents with Generative Adversarial Networks
Glover, John
2016-01-01
This paper describes a method for using Generative Adversarial Networks to learn distributed representations of natural language documents. We propose a model that is based on the recently proposed Energy-Based GAN, but instead uses a Denoising Autoencoder as the discriminator network. Document representations are extracted from the hidden layer of the discriminator and evaluated both quantitatively and qualitatively.
Designing Network-based Business Model Ontology
DEFF Research Database (Denmark)
Hashemi Nekoo, Ali Reza; Ashourizadeh, Shayegheh; Zarei, Behrouz
2015-01-01
Survival on dynamic environment is not achieved without a map. Scanning and monitoring of the market show business models as a fruitful tool. But scholars believe that old-fashioned business models are dead; as they are not included the effect of internet and network in themselves. This paper...... is going to propose e-business model ontology from the network point of view and its application in real world. The suggested ontology for network-based businesses is composed of individuals` characteristics and what kind of resources they own. also, their connections and pre-conceptions of connections...... such as shared-mental model and trust. However, it mostly covers previous business model elements. To confirm the applicability of this ontology, it has been implemented in business angel network and showed how it works....
Temperature Modelling of the Biomass Pretreatment Process
DEFF Research Database (Denmark)
Prunescu, Remus Mihail; Blanke, Mogens; Jensen, Jakob M.
2012-01-01
In a second generation biorefinery, the biomass pretreatment stage has an important contribution to the efficiency of the downstream processing units involved in biofuel production. Most of the pretreatment process occurs in a large pressurized thermal reactor that presents an irregular temperature...... that captures the environmental temperature differences inside the reactor using distributed parameters. A Kalman filter is then added to account for any missing dynamics and the overall model is embedded into a temperature soft sensor. The operator of the plant will be able to observe the temperature in any...
Modeling trust context in networks
Adali, Sibel
2013-01-01
We make complex decisions every day, requiring trust in many different entities for different reasons. These decisions are not made by combining many isolated trust evaluations. Many interlocking factors play a role, each dynamically impacting the others.? In this brief, 'trust context' is defined as the system level description of how the trust evaluation process unfolds.Networks today are part of almost all human activity, supporting and shaping it. Applications increasingly incorporate new interdependencies and new trust contexts. Social networks connect people and organizations throughout
Improving Shade Modelling in a Regional River Temperature Model Using Fine-Scale LIDAR Data
Hannah, D. M.; Loicq, P.; Moatar, F.; Beaufort, A.; Melin, E.; Jullian, Y.
2015-12-01
Air temperature is often considered as a proxy of the stream temperature to model the distribution areas of aquatic species water temperature is not available at a regional scale. To simulate the water temperature at a regional scale (105 km²), a physically-based model using the equilibrium temperature concept and including upstream-downstream propagation of the thermal signal was developed and applied to the entire Loire basin (Beaufort et al., submitted). This model, called T-NET (Temperature-NETwork) is based on a hydrographical network topology. Computations are made hourly on 52,000 reaches which average 1.7 km long in the Loire drainage basin. The model gives a median Root Mean Square Error of 1.8°C at hourly time step on the basis of 128 water temperature stations (2008-2012). In that version of the model, tree shadings is modelled by a constant factor proportional to the vegetation cover on 10 meters sides the river reaches. According to sensitivity analysis, improving the shade representation would enhance T-NET accuracy, especially for the maximum daily temperatures, which are currently not very well modelized. This study evaluates the most efficient way (accuracy/computing time) to improve the shade model thanks to 1-m resolution LIDAR data available on tributary of the LoireRiver (317 km long and an area of 8280 km²). Two methods are tested and compared: the first one is a spatially explicit computation of the cast shadow for every LIDAR pixel. The second is based on averaged vegetation cover characteristics of buffers and reaches of variable size. Validation of the water temperature model is made against 4 temperature sensors well spread along the stream, as well as two airborne thermal infrared imageries acquired in summer 2014 and winter 2015 over a 80 km reach. The poster will present the optimal length- and crosswise scale to characterize the vegetation from LIDAR data.
Mathematical model of highways network optimization
Sakhapov, R. L.; Nikolaeva, R. V.; Gatiyatullin, M. H.; Makhmutov, M. M.
2017-12-01
The article deals with the issue of highways network design. Studies show that the main requirement from road transport for the road network is to ensure the realization of all the transport links served by it, with the least possible cost. The goal of optimizing the network of highways is to increase the efficiency of transport. It is necessary to take into account a large number of factors that make it difficult to quantify and qualify their impact on the road network. In this paper, we propose building an optimal variant for locating the road network on the basis of a mathematical model. The article defines the criteria for optimality and objective functions that reflect the requirements for the road network. The most fully satisfying condition for optimality is the minimization of road and transport costs. We adopted this indicator as a criterion of optimality in the economic-mathematical model of a network of highways. Studies have shown that each offset point in the optimal binding road network is associated with all other corresponding points in the directions providing the least financial costs necessary to move passengers and cargo from this point to the other corresponding points. The article presents general principles for constructing an optimal network of roads.
Energetic and Exergetic Analysis of Low and Medium Temperature District Heating Network Integration
DEFF Research Database (Denmark)
Li, Hongwei; Svendsen, Svend
In this paper, energetic and exergetic approaches were applied to an exemplary low temperature district heating (LTDH) network with supply/return water temperature at 55oC/25 oC. The small LTDH network is annexed to a large medium temperature district heating (MTDH) network. The LTDH network can ...... will reduce the amount of water supply from the MTDH network and improve the system energy conversion efficiency. Through the simulation, the system energetic and exergetic efficiencies based on the two network integration approaches were calculated and evaluated.......In this paper, energetic and exergetic approaches were applied to an exemplary low temperature district heating (LTDH) network with supply/return water temperature at 55oC/25 oC. The small LTDH network is annexed to a large medium temperature district heating (MTDH) network. The LTDH network can...... be supplied through upgrading the return water from the MTDH network with a small centralized heat pump. Alternatively, the supply and return water from the MTDH network can be mixed with a shunt at the junction point to supply the LTDH network. Comparing with the second approach, the heat pump system...
Graphical Model Theory for Wireless Sensor Networks
International Nuclear Information System (INIS)
Davis, William B.
2002-01-01
Information processing in sensor networks, with many small processors, demands a theory of computation that allows the minimization of processing effort, and the distribution of this effort throughout the network. Graphical model theory provides a probabilistic theory of computation that explicitly addresses complexity and decentralization for optimizing network computation. The junction tree algorithm, for decentralized inference on graphical probability models, can be instantiated in a variety of applications useful for wireless sensor networks, including: sensor validation and fusion; data compression and channel coding; expert systems, with decentralized data structures, and efficient local queries; pattern classification, and machine learning. Graphical models for these applications are sketched, and a model of dynamic sensor validation and fusion is presented in more depth, to illustrate the junction tree algorithm
Modeling Network Traffic in Wavelet Domain
Directory of Open Access Journals (Sweden)
Sheng Ma
2004-12-01
Full Text Available This work discovers that although network traffic has the complicated short- and long-range temporal dependence, the corresponding wavelet coefficients are no longer long-range dependent. Therefore, a "short-range" dependent process can be used to model network traffic in the wavelet domain. Both independent and Markov models are investigated. Theoretical analysis shows that the independent wavelet model is sufficiently accurate in terms of the buffer overflow probability for Fractional Gaussian Noise traffic. Any model, which captures additional correlations in the wavelet domain, only improves the performance marginally. The independent wavelet model is then used as a unified approach to model network traffic including VBR MPEG video and Ethernet data. The computational complexity is O(N for developing such wavelet models and generating synthesized traffic of length N, which is among the lowest attained.
Sparsity in Model Gene Regulatory Networks
International Nuclear Information System (INIS)
Zagorski, M.
2011-01-01
We propose a gene regulatory network model which incorporates the microscopic interactions between genes and transcription factors. In particular the gene's expression level is determined by deterministic synchronous dynamics with contribution from excitatory interactions. We study the structure of networks that have a particular '' function '' and are subject to the natural selection pressure. The question of network robustness against point mutations is addressed, and we conclude that only a small part of connections defined as '' essential '' for cell's existence is fragile. Additionally, the obtained networks are sparse with narrow in-degree and broad out-degree, properties well known from experimental study of biological regulatory networks. Furthermore, during sampling procedure we observe that significantly different genotypes can emerge under mutation-selection balance. All the preceding features hold for the model parameters which lay in the experimentally relevant range. (author)
Multiple Temperature Model for Near Continuum Flows
International Nuclear Information System (INIS)
XU, Kun; Liu, Hongwei; Jiang, Jianzheng
2007-01-01
In the near continuum flow regime, the flow may have different translational temperatures in different directions. It is well known that for increasingly rarefied flow fields, the predictions from continuum formulation, such as the Navier-Stokes equations, lose accuracy. These inaccuracies may be partially due to the single temperature assumption in the Navier-Stokes equations. Here, based on the gas-kinetic Bhatnagar-Gross-Krook (BGK) equation, a multitranslational temperature model is proposed and used in the flow calculations. In order to fix all three translational temperatures, two constraints are additionally proposed to model the energy exchange in different directions. Based on the multiple temperature assumption, the Navier-Stokes relation between the stress and strain is replaced by the temperature relaxation term, and the Navier-Stokes assumption is recovered only in the limiting case when the flow is close to the equilibrium with the same temperature in different directions. In order to validate the current model, both the Couette and Poiseuille flows are studied in the transition flow regime
The QKD network: model and routing scheme
Yang, Chao; Zhang, Hongqi; Su, Jinhai
2017-11-01
Quantum key distribution (QKD) technology can establish unconditional secure keys between two communicating parties. Although this technology has some inherent constraints, such as the distance and point-to-point mode limits, building a QKD network with multiple point-to-point QKD devices can overcome these constraints. Considering the development level of current technology, the trust relaying QKD network is the first choice to build a practical QKD network. However, the previous research didn't address a routing method on the trust relaying QKD network in detail. This paper focuses on the routing issues, builds a model of the trust relaying QKD network for easily analysing and understanding this network, and proposes a dynamical routing scheme for this network. From the viewpoint of designing a dynamical routing scheme in classical network, the proposed scheme consists of three components: a Hello protocol helping share the network topology information, a routing algorithm to select a set of suitable paths and establish the routing table and a link state update mechanism helping keep the routing table newly. Experiments and evaluation demonstrates the validity and effectiveness of the proposed routing scheme.
2016-11-09
Figure 1. We generally express such networks in terms of the services running in each enclave as well as the routing and firewall rules between the...compromise a server, they can compromise other devices in the same subnet or protected enclave. They probe attached firewalls and routers for open ports and...spam and malware filter would prevent this content from reaching its destination. Content filtering provides another layer of defense to other controls
Statistical Downscaling of Temperature with the Random Forest Model
Directory of Open Access Journals (Sweden)
Bo Pang
2017-01-01
Full Text Available The issues with downscaling the outputs of a global climate model (GCM to a regional scale that are appropriate to hydrological impact studies are investigated using the random forest (RF model, which has been shown to be superior for large dataset analysis and variable importance evaluation. The RF is proposed for downscaling daily mean temperature in the Pearl River basin in southern China. Four downscaling models were developed and validated by using the observed temperature series from 61 national stations and large-scale predictor variables derived from the National Center for Environmental Prediction–National Center for Atmospheric Research reanalysis dataset. The proposed RF downscaling model was compared to multiple linear regression, artificial neural network, and support vector machine models. Principal component analysis (PCA and partial correlation analysis (PAR were used in the predictor selection for the other models for a comprehensive study. It was shown that the model efficiency of the RF model was higher than that of the other models according to five selected criteria. By evaluating the predictor importance, the RF could choose the best predictor combination without using PCA and PAR. The results indicate that the RF is a feasible tool for the statistical downscaling of temperature.
Thermal conductivity model for nanofiber networks
Zhao, Xinpeng; Huang, Congliang; Liu, Qingkun; Smalyukh, Ivan I.; Yang, Ronggui
2018-02-01
Understanding thermal transport in nanofiber networks is essential for their applications in thermal management, which are used extensively as mechanically sturdy thermal insulation or high thermal conductivity materials. In this study, using the statistical theory and Fourier's law of heat conduction while accounting for both the inter-fiber contact thermal resistance and the intrinsic thermal resistance of nanofibers, an analytical model is developed to predict the thermal conductivity of nanofiber networks as a function of their geometric and thermal properties. A scaling relation between the thermal conductivity and the geometric properties including volume fraction and nanofiber length of the network is revealed. This model agrees well with both numerical simulations and experimental measurements found in the literature. This model may prove useful in analyzing the experimental results and designing nanofiber networks for both high and low thermal conductivity applications.
Thermal conductivity model for nanofiber networks
Energy Technology Data Exchange (ETDEWEB)
Zhao, Xinpeng [Department of Mechanical Engineering, University of Colorado, Boulder, Colorado 80309, USA; Huang, Congliang [Department of Mechanical Engineering, University of Colorado, Boulder, Colorado 80309, USA; School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China; Liu, Qingkun [Department of Physics, University of Colorado, Boulder, Colorado 80309, USA; Smalyukh, Ivan I. [Department of Physics, University of Colorado, Boulder, Colorado 80309, USA; Materials Science and Engineering Program, University of Colorado, Boulder, Colorado 80309, USA; Yang, Ronggui [Department of Mechanical Engineering, University of Colorado, Boulder, Colorado 80309, USA; Materials Science and Engineering Program, University of Colorado, Boulder, Colorado 80309, USA; Buildings and Thermal Systems Center, National Renewable Energy Laboratory, Golden, Colorado 80401, USA
2018-02-28
Understanding thermal transport in nanofiber networks is essential for their applications in thermal management, which are used extensively as mechanically sturdy thermal insulation or high thermal conductivity materials. In this study, using the statistical theory and Fourier's law of heat conduction while accounting for both the inter-fiber contact thermal resistance and the intrinsic thermal resistance of nanofibers, an analytical model is developed to predict the thermal conductivity of nanofiber networks as a function of their geometric and thermal properties. A scaling relation between the thermal conductivity and the geometric properties including volume fraction and nanofiber length of the network is revealed. This model agrees well with both numerical simulations and experimental measurements found in the literature. This model may prove useful in analyzing the experimental results and designing nanofiber networks for both high and low thermal conductivity applications.
A quantum-implementable neural network model
Chen, Jialin; Wang, Lingli; Charbon, Edoardo
2017-10-01
A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is proposed in this paper. QPNN can use quantum parallelism to trace all possible network states to improve the result. Due to its unique quantum nature, this model is robust to several quantum noises under certain conditions, which can be efficiently implemented by the qubus quantum computer. Another advantage is that QPNN can be used as memory to retrieve the most relevant data and even to generate new data. The MATLAB experimental results of Iris data classification and MNIST handwriting recognition show that much less neuron resources are required in QPNN to obtain a good result than the classical feedforward neural network. The proposed QPNN model indicates that quantum effects are useful for real-life classification tasks.
Combinatorial explosion in model gene networks
Edwards, R.; Glass, L.
2000-09-01
The explosive growth in knowledge of the genome of humans and other organisms leaves open the question of how the functioning of genes in interacting networks is coordinated for orderly activity. One approach to this problem is to study mathematical properties of abstract network models that capture the logical structures of gene networks. The principal issue is to understand how particular patterns of activity can result from particular network structures, and what types of behavior are possible. We study idealized models in which the logical structure of the network is explicitly represented by Boolean functions that can be represented by directed graphs on n-cubes, but which are continuous in time and described by differential equations, rather than being updated synchronously via a discrete clock. The equations are piecewise linear, which allows significant analysis and facilitates rapid integration along trajectories. We first give a combinatorial solution to the question of how many distinct logical structures exist for n-dimensional networks, showing that the number increases very rapidly with n. We then outline analytic methods that can be used to establish the existence, stability and periods of periodic orbits corresponding to particular cycles on the n-cube. We use these methods to confirm the existence of limit cycles discovered in a sample of a million randomly generated structures of networks of 4 genes. Even with only 4 genes, at least several hundred different patterns of stable periodic behavior are possible, many of them surprisingly complex. We discuss ways of further classifying these periodic behaviors, showing that small mutations (reversal of one or a few edges on the n-cube) need not destroy the stability of a limit cycle. Although these networks are very simple as models of gene networks, their mathematical transparency reveals relationships between structure and behavior, they suggest that the possibilities for orderly dynamics in such
GMDH and neural networks applied in temperature sensors monitoring
International Nuclear Information System (INIS)
Bueno, Elaine Inacio; Pereira, Iraci Martinez; Silva, Antonio Teixeira e
2009-01-01
In this work a monitoring system was developed based on the Group Method of Data Handling (GMDH) and Neural Networks (ANNs) methodologies. This methodology was applied to the IEA-R1 research reactor at IPEN by using a database obtained from a theoretical model of the reactor. The IEA-R1 research reactor is a pool type reactor of 5 MW, cooled and moderated by light water, and uses graphite and beryllium as reflector. The theoretical model was developed using the Matlab GUIDE toolbox. The equations are based in the IEA-R1 mass and energy inventory balance and physical as well as operational aspects are taken into consideration. This methodology was developed by using the GMDH algorithm as input variables to the ANNs. The results obtained using the GMDH and ANNs were better than that obtained using only ANNs. (author)
Neural Network Modeling to Predict Shelf Life of Greenhouse Lettuce
Directory of Open Access Journals (Sweden)
Wei-Chin Lin
2009-04-01
Full Text Available Greenhouse-grown butter lettuce (Lactuca sativa L. can potentially be stored for 21 days at constant 0°C. When storage temperature was increased to 5°C or 10°C, shelf life was shortened to 14 or 10 days, respectively, in our previous observations. Also, commercial shelf life of 7 to 10 days is common, due to postharvest temperature fluctuations. The objective of this study was to establish neural network (NN models to predict the remaining shelf life (RSL under fluctuating postharvest temperatures. A box of 12 - 24 lettuce heads constituted a sample unit. The end of the shelf life of each head was determined when it showed initial signs of decay or yellowing. Air temperatures inside a shipping box were recorded. Daily average temperatures in storage and averaged shelf life of each box were used as inputs, and the RSL was modeled as an output. An R2 of 0.57 could be observed when a simple NN structure was employed. Since the "future" (or remaining storage temperatures were unavailable at the time of making a prediction, a second NN model was introduced to accommodate a range of future temperatures and associated shelf lives. Using such 2-stage NN models, an R2 of 0.61 could be achieved for predicting RSL. This study indicated that NN modeling has potential for cold chain quality control and shelf life prediction.
Complex networks under dynamic repair model
Chaoqi, Fu; Ying, Wang; Kun, Zhao; Yangjun, Gao
2018-01-01
Invulnerability is not the only factor of importance when considering complex networks' security. It is also critical to have an effective and reasonable repair strategy. Existing research on network repair is confined to the static model. The dynamic model makes better use of the redundant capacity of repaired nodes and repairs the damaged network more efficiently than the static model; however, the dynamic repair model is complex and polytropic. In this paper, we construct a dynamic repair model and systematically describe the energy-transfer relationships between nodes in the repair process of the failure network. Nodes are divided into three types, corresponding to three structures. We find that the strong coupling structure is responsible for secondary failure of the repaired nodes and propose an algorithm that can select the most suitable targets (nodes or links) to repair the failure network with minimal cost. Two types of repair strategies are identified, with different effects under the two energy-transfer rules. The research results enable a more flexible approach to network repair.
Performance modeling, stochastic networks, and statistical multiplexing
Mazumdar, Ravi R
2013-01-01
This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of introducing an appropriate mathematical framework for modeling and analysis as well as understanding the phenomenon of statistical multiplexing. The models, techniques, and results presented form the core of traffic engineering methods used to design, control and allocate resources in communication networks.The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the importan
Network Modeling and Simulation A Practical Perspective
Guizani, Mohsen; Khan, Bilal
2010-01-01
Network Modeling and Simulation is a practical guide to using modeling and simulation to solve real-life problems. The authors give a comprehensive exposition of the core concepts in modeling and simulation, and then systematically address the many practical considerations faced by developers in modeling complex large-scale systems. The authors provide examples from computer and telecommunication networks and use these to illustrate the process of mapping generic simulation concepts to domain-specific problems in different industries and disciplines. Key features: Provides the tools and strate
Modeling acquaintance networks based on balance theory
Directory of Open Access Journals (Sweden)
Vukašinović Vida
2014-09-01
Full Text Available An acquaintance network is a social structure made up of a set of actors and the ties between them. These ties change dynamically as a consequence of incessant interactions between the actors. In this paper we introduce a social network model called the Interaction-Based (IB model that involves well-known sociological principles. The connections between the actors and the strength of the connections are influenced by the continuous positive and negative interactions between the actors and, vice versa, the future interactions are more likely to happen between the actors that are connected with stronger ties. The model is also inspired by the social behavior of animal species, particularly that of ants in their colony. A model evaluation showed that the IB model turned out to be sparse. The model has a small diameter and an average path length that grows in proportion to the logarithm of the number of vertices. The clustering coefficient is relatively high, and its value stabilizes in larger networks. The degree distributions are slightly right-skewed. In the mature phase of the IB model, i.e., when the number of edges does not change significantly, most of the network properties do not change significantly either. The IB model was found to be the best of all the compared models in simulating the e-mail URV (University Rovira i Virgili of Tarragona network because the properties of the IB model more closely matched those of the e-mail URV network than the other models
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.
Modeling of concrete response at high temperature
International Nuclear Information System (INIS)
Pfeiffer, P.; Marchertas, A.
1984-01-01
A rate-type creep law is implemented into the computer code TEMP-STRESS for high temperature concrete analysis. The disposition of temperature, pore pressure and moisture for the particular structure in question is provided as input for the thermo-mechanical code. The loss of moisture from concrete also induces material shrinkage which is accounted for in the analytical model. Examples are given to illustrate the numerical results
Optimal transportation networks models and theory
Bernot, Marc; Morel, Jean-Michel
2009-01-01
The transportation problem can be formalized as the problem of finding the optimal way to transport a given measure into another with the same mass. In contrast to the Monge-Kantorovitch problem, recent approaches model the branched structure of such supply networks as minima of an energy functional whose essential feature is to favour wide roads. Such a branched structure is observable in ground transportation networks, in draining and irrigation systems, in electrical power supply systems and in natural counterparts such as blood vessels or the branches of trees. These lectures provide mathematical proof of several existence, structure and regularity properties empirically observed in transportation networks. The link with previous discrete physical models of irrigation and erosion models in geomorphology and with discrete telecommunication and transportation models is discussed. It will be mathematically proven that the majority fit in the simple model sketched in this volume.
Flood routing modelling with Artificial Neural Networks
Directory of Open Access Journals (Sweden)
R. Peters
2006-01-01
Full Text Available For the modelling of the flood routing in the lower reaches of the Freiberger Mulde river and its tributaries the one-dimensional hydrodynamic modelling system HEC-RAS has been applied. Furthermore, this model was used to generate a database to train multilayer feedforward networks. To guarantee numerical stability for the hydrodynamic modelling of some 60 km of streamcourse an adequate resolution in space requires very small calculation time steps, which are some two orders of magnitude smaller than the input data resolution. This leads to quite high computation requirements seriously restricting the application – especially when dealing with real time operations such as online flood forecasting. In order to solve this problem we tested the application of Artificial Neural Networks (ANN. First studies show the ability of adequately trained multilayer feedforward networks (MLFN to reproduce the model performance.
Linear approximation model network and its formation via ...
Indian Academy of Sciences (India)
To overcome the deficiency of `local model network' (LMN) techniques, an alternative `linear approximation model' (LAM) network approach is proposed. Such a network models a nonlinear or practical system with multiple linear models fitted along operating trajectories, where individual models are simply networked ...
Comparison of Different Fuel Temperature Models
Energy Technology Data Exchange (ETDEWEB)
Weddig, Beatrice
2003-02-01
The purpose of this work is to improve the performance of the core calculation system used in Ringhals for in-core fuel management. It has been observed that, whereas the codes yield results that are in good agreement with measurements when the core operates at full nominal power, this agreement deteriorates noticeably when the reactor is running at reduced power. This deficiency of the code system was observed by comparing the calculated and measured boron concentrations in the moderator of the PWR. From the neutronic point of view, the difference between full power and reduced power in the same core is the different temperature of the fuel and the moderator. Whereas the coolant temperature can be measured and is thus relatively well known, the fuel temperature is only inferred from the moderator temperature as well as neutron physics and heat transfer calculations. The most likely reason for the above mentioned discrepancy is therefore the uncertainty of the fuel temperature at low power, and hence the incorrect calculation of the fuel temperature reactivity feedback through the so called Doppler effect. To obtain the fuel temperature at low power, usually some semi-empirical relations, sometimes called correlations, are used. The above-mentioned inaccuracy of the core calculation procedures can thus be tracked down to the insufficiency of these correlations. Therefore, the suggestion is that the above mentioned deficiency of the core calculation codes can be eliminated or reduced if the fuel temperature correlations are improved. An improved model, called the 30% model, is implemented in SIMULATE-3, the core calculation code used at Ringhals. The accuracy of the 30% model was compared to that of the present model by considering a number of cases, where measured values of the boron concentration at low power were available, and comparing them with calculated values using both the present and the new model. It was found that on the whole, the new fuel temperature
Comparison of Different Fuel Temperature Models
International Nuclear Information System (INIS)
Weddig, Beatrice
2003-02-01
The purpose of this work is to improve the performance of the core calculation system used in Ringhals for in-core fuel management. It has been observed that, whereas the codes yield results that are in good agreement with measurements when the core operates at full nominal power, this agreement deteriorates noticeably when the reactor is running at reduced power. This deficiency of the code system was observed by comparing the calculated and measured boron concentrations in the moderator of the PWR. From the neutronic point of view, the difference between full power and reduced power in the same core is the different temperature of the fuel and the moderator. Whereas the coolant temperature can be measured and is thus relatively well known, the fuel temperature is only inferred from the moderator temperature as well as neutron physics and heat transfer calculations. The most likely reason for the above mentioned discrepancy is therefore the uncertainty of the fuel temperature at low power, and hence the incorrect calculation of the fuel temperature reactivity feedback through the so called Doppler effect. To obtain the fuel temperature at low power, usually some semi-empirical relations, sometimes called correlations, are used. The above-mentioned inaccuracy of the core calculation procedures can thus be tracked down to the insufficiency of these correlations. Therefore, the suggestion is that the above mentioned deficiency of the core calculation codes can be eliminated or reduced if the fuel temperature correlations are improved. An improved model, called the 30% model, is implemented in SIMULATE-3, the core calculation code used at Ringhals. The accuracy of the 30% model was compared to that of the present model by considering a number of cases, where measured values of the boron concentration at low power were available, and comparing them with calculated values using both the present and the new model. It was found that on the whole, the new fuel temperature
Modeling Security Aspects of Network
Schoch, Elmar
With more and more widespread usage of computer systems and networks, dependability becomes a paramount requirement. Dependability typically denotes tolerance or protection against all kinds of failures, errors and faults. Sources of failures can basically be accidental, e.g., in case of hardware errors or software bugs, or intentional due to some kind of malicious behavior. These intentional, malicious actions are subject of security. A more complete overview on the relations between dependability and security can be found in [31]. In parallel to the increased use of technology, misuse also has grown significantly, requiring measures to deal with it.
Hydrometeorological network for flood monitoring and modeling
Efstratiadis, Andreas; Koussis, Antonis D.; Lykoudis, Spyros; Koukouvinos, Antonis; Christofides, Antonis; Karavokiros, George; Kappos, Nikos; Mamassis, Nikos; Koutsoyiannis, Demetris
2013-08-01
Due to its highly fragmented geomorphology, Greece comprises hundreds of small- to medium-size hydrological basins, in which often the terrain is fairly steep and the streamflow regime ephemeral. These are typically affected by flash floods, occasionally causing severe damages. Yet, the vast majority of them lack flow-gauging infrastructure providing systematic hydrometric data at fine time scales. This has obvious impacts on the quality and reliability of flood studies, which typically use simplistic approaches for ungauged basins that do not consider local peculiarities in sufficient detail. In order to provide a consistent framework for flood design and to ensure realistic predictions of the flood risk -a key issue of the 2007/60/EC Directive- it is essential to improve the monitoring infrastructures by taking advantage of modern technologies for remote control and data management. In this context and in the research project DEUCALION, we have recently installed and are operating, in four pilot river basins, a telemetry-based hydro-meteorological network that comprises automatic stations and is linked to and supported by relevant software. The hydrometric stations measure stage, using 50-kHz ultrasonic pulses or piezometric sensors, or both stage (piezometric) and velocity via acoustic Doppler radar; all measurements are being temperature-corrected. The meteorological stations record air temperature, pressure, relative humidity, wind speed and direction, and precipitation. Data transfer is made via GPRS or mobile telephony modems. The monitoring network is supported by a web-based application for storage, visualization and management of geographical and hydro-meteorological data (ENHYDRIS), a software tool for data analysis and processing (HYDROGNOMON), as well as an advanced model for flood simulation (HYDROGEIOS). The recorded hydro-meteorological observations are accessible over the Internet through the www-application. The system is operational and its
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, ...
Cloud Impacts on Pavement Temperature in Energy Balance Models
Walker, C. L.
2013-12-01
Forecast systems provide decision support for end-users ranging from the solar energy industry to municipalities concerned with road safety. Pavement temperature is an important variable when considering vehicle response to various weather conditions. A complex, yet direct relationship exists between tire and pavement temperatures. Literature has shown that as tire temperature increases, friction decreases which affects vehicle performance. Many forecast systems suffer from inaccurate radiation forecasts resulting in part from the inability to model different types of clouds and their influence on radiation. This research focused on forecast improvement by determining how cloud type impacts the amount of shortwave radiation reaching the surface and subsequent pavement temperatures. The study region was the Great Plains where surface solar radiation data were obtained from the High Plains Regional Climate Center's Automated Weather Data Network stations. Road pavement temperature data were obtained from the Meteorological Assimilation Data Ingest System. Cloud properties and radiative transfer quantities were obtained from the Clouds and Earth's Radiant Energy System mission via Aqua and Terra Moderate Resolution Imaging Spectroradiometer satellite products. An additional cloud data set was incorporated from the Naval Research Laboratory Cloud Classification algorithm. Statistical analyses using a modified nearest neighbor approach were first performed relating shortwave radiation variability with road pavement temperature fluctuations. Then statistical associations were determined between the shortwave radiation and cloud property data sets. Preliminary results suggest that substantial pavement forecasting improvement is possible with the inclusion of cloud-specific information. Future model sensitivity testing seeks to quantify the magnitude of forecast improvement.
An evolving network model with modular growth
International Nuclear Information System (INIS)
Zou Zhi-Yun; Liu Peng; Lei Li; Gao Jian-Zhi
2012-01-01
In this paper, we propose an evolving network model growing fast in units of module, according to the analysis of the evolution characteristics in real complex networks. Each module is a small-world network containing several interconnected nodes and the nodes between the modules are linked by preferential attachment on degree of nodes. We study the modularity measure of the proposed model, which can be adjusted by changing the ratio of the number of inner-module edges and the number of inter-module edges. In view of the mean-field theory, we develop an analytical function of the degree distribution, which is verified by a numerical example and indicates that the degree distribution shows characteristics of the small-world network and the scale-free network distinctly at different segments. The clustering coefficient and the average path length of the network are simulated numerically, indicating that the network shows the small-world property and is affected little by the randomness of the new module. (interdisciplinary physics and related areas of science and technology)
Modeling of contact tracing in social networks
Tsimring, Lev S.; Huerta, Ramón
2003-07-01
Spreading of certain infections in complex networks is effectively suppressed by using intelligent strategies for epidemic control. One such standard epidemiological strategy consists in tracing contacts of infected individuals. In this paper, we use a recently introduced generalization of the standard susceptible-infectious-removed stochastic model for epidemics in sparse random networks which incorporates an additional (traced) state. We describe a deterministic mean-field description which yields quantitative agreement with stochastic simulations on random graphs. We also discuss the role of contact tracing in epidemics control in small-world and scale-free networks. Effectiveness of contact tracing grows as the rewiring probability is reduced.
A Network Model of Credit Risk Contagion
Directory of Open Access Journals (Sweden)
Ting-Qiang Chen
2012-01-01
Full Text Available A network model of credit risk contagion is presented, in which the effect of behaviors of credit risk holders and the financial market regulators and the network structure are considered. By introducing the stochastic dominance theory, we discussed, respectively, the effect mechanisms of the degree of individual relationship, individual attitude to credit risk contagion, the individual ability to resist credit risk contagion, the monitoring strength of the financial market regulators, and the network structure on credit risk contagion. Then some derived and proofed propositions were verified through numerical simulations.
International Nuclear Information System (INIS)
Phillips, J.C.
2010-01-01
The review multiple successes of the discrete hard-wired dopant network model ZZIP, and comment on the equally numerous failures of continuum models, in describing and predicting the properties of ceramic superconductors. The prediction of transition temperatures can be regarded in several ways, either as an exacting test of theory, or as a tool for identifying theoretical rules for defining new homology models. Popular first principle methods for predicting transition temperatures in conventional crystalline superconductors have failed for cuprate HTSC, as have parameterized models based on CuO2 planes (with or without apical oxygen). Following a path suggested by Bayesian probability, it was found that the glassy, self-organized dopant network percolative model is so successful that it defines a new homology class appropriate to ceramic superconductors. The reasons for this success in an exponentially complex (non-polynomial complete, NPC) problem are discussed, and a critical comparison is made with previous polynomial (PC) theories. The predictions are successful for the superfamily of all ceramics, including new non-cuprates based on FeAs in place of CuO2.
The International Trade Network: weighted network analysis and modelling
International Nuclear Information System (INIS)
Bhattacharya, K; Mukherjee, G; Manna, S S; Saramäki, J; Kaski, K
2008-01-01
Tools of the theory of critical phenomena, namely the scaling analysis and universality, are argued to be applicable to large complex web-like network structures. Using a detailed analysis of the real data of the International Trade Network we argue that the scaled link weight distribution has an approximate log-normal distribution which remains robust over a period of 53 years. Another universal feature is observed in the power-law growth of the trade strength with gross domestic product, the exponent being similar for all countries. Using the 'rich-club' coefficient measure of the weighted networks it has been shown that the size of the rich-club controlling half of the world's trade is actually shrinking. While the gravity law is known to describe well the social interactions in the static networks of population migration, international trade, etc, here for the first time we studied a non-conservative dynamical model based on the gravity law which excellently reproduced many empirical features of the ITN
Keystone Business Models for Network Security Processors
Arthur Low; Steven Muegge
2013-01-01
Network security processors are critical components of high-performance systems built for cybersecurity. Development of a network security processor requires multi-domain experience in semiconductors and complex software security applications, and multiple iterations of both software and hardware implementations. Limited by the business models in use today, such an arduous task can be undertaken only by large incumbent companies and government organizations. Neither the “fabless semiconductor...
Stochastic modeling and analysis of telecoms networks
Decreusefond, Laurent
2012-01-01
This book addresses the stochastic modeling of telecommunication networks, introducing the main mathematical tools for that purpose, such as Markov processes, real and spatial point processes and stochastic recursions, and presenting a wide list of results on stability, performances and comparison of systems.The authors propose a comprehensive mathematical construction of the foundations of stochastic network theory: Markov chains, continuous time Markov chains are extensively studied using an original martingale-based approach. A complete presentation of stochastic recursions from an
Decomposed Implicit Models of Piecewise - Linear Networks
Directory of Open Access Journals (Sweden)
J. Brzobohaty
1992-05-01
Full Text Available The general matrix form of the implicit description of a piecewise-linear (PWL network and the symbolic block diagram of the corresponding circuit model are proposed. Their decomposed forms enable us to determine quite separately the existence of the individual breakpoints of the resultant PWL characteristic and their coordinates using independent network parameters. For the two-diode and three-diode cases all the attainable types of the PWL characteristic are introduced.
Artificial Immune Networks: Models and Applications
Directory of Open Access Journals (Sweden)
Xian Shen
2008-06-01
Full Text Available Artificial Immune Systems (AIS, which is inspired by the nature immune system, has been applied for solving complex computational problems in classification, pattern rec- ognition, and optimization. In this paper, the theory of the natural immune system is first briefly introduced. Next, we compare some well-known AIS and their applications. Several representative artificial immune networks models are also dis- cussed. Moreover, we demonstrate the applications of artificial immune networks in various engineering fields.
Continuum Modeling of Biological Network Formation
Albi, Giacomo
2017-04-10
We present an overview of recent analytical and numerical results for the elliptic–parabolic system of partial differential equations proposed by Hu and Cai, which models the formation of biological transportation networks. The model describes the pressure field using a Darcy type equation and the dynamics of the conductance network under pressure force effects. Randomness in the material structure is represented by a linear diffusion term and conductance relaxation by an algebraic decay term. We first introduce micro- and mesoscopic models and show how they are connected to the macroscopic PDE system. Then, we provide an overview of analytical results for the PDE model, focusing mainly on the existence of weak and mild solutions and analysis of the steady states. The analytical part is complemented by extensive numerical simulations. We propose a discretization based on finite elements and study the qualitative properties of network structures for various parameter values.
Adaptive-network models of collective dynamics
Zschaler, G.
2012-09-01
Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom. In this work, which is essentially my PhD thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system's collective and long-term behaviour by applying tools from dynamical systems theory. We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge
Network Design Models for Container Shipping
DEFF Research Database (Denmark)
Reinhardt, Line Blander; Kallehauge, Brian; Nielsen, Anders Nørrelund
This paper presents a study of the network design problem in container shipping. The paper combines the network design and fleet assignment problem into a mixed integer linear programming model minimizing the overall cost. The major contributions of this paper is that the time of a vessel route...... is included in the calculation of the capacity and that a inhomogeneous fleet is modeled. The model also includes the cost of transshipment which is one of the major cost for the shipping companies. The concept of pseudo simple routes is introduced to expand the set of feasible routes. The linearization...
Model predictive control of room temperature with disturbance compensation
Kurilla, Jozef; Hubinský, Peter
2017-08-01
This paper deals with temperature control of multivariable system of office building. The system is simplified to several single input-single output systems by decoupling their mutual linkages, which are separately controlled by regulator based on generalized model predictive control. Main part of this paper focuses on the accuracy of the office temperature with respect to occupancy profile and effect of disturbance. Shifting of desired temperature and changing of weighting coefficients are used to achieve the desired accuracy of regulation. The final structure of regulation joins advantages of distributed computing power and possibility to use network communication between individual controllers to consider the constraints. The advantage of using decoupled MPC controllers compared to conventional PID regulators is demonstrated in a simulation study.
Characterization and Modeling of Network Traffic
DEFF Research Database (Denmark)
Shawky, Ahmed; Bergheim, Hans; Ragnarsson, Olafur
2011-01-01
-arrival time, IP addresses, port numbers and transport protocol are the only necessary parameters to model network traffic behaviour. In order to recreate this behaviour, a complex model is needed which is able to recreate traffic behaviour based on a set of statistics calculated from the parameters values...
Phenomenological network models: Lessons for epilepsy surgery.
Hebbink, Jurgen; Meijer, Hil; Huiskamp, Geertjan; van Gils, Stephan; Leijten, Frans
2017-10-01
The current opinion in epilepsy surgery is that successful surgery is about removing pathological cortex in the anatomic sense. This contrasts with recent developments in epilepsy research, where epilepsy is seen as a network disease. Computational models offer a framework to investigate the influence of networks, as well as local tissue properties, and to explore alternative resection strategies. Here we study, using such a model, the influence of connections on seizures and how this might change our traditional views of epilepsy surgery. We use a simple network model consisting of four interconnected neuronal populations. One of these populations can be made hyperexcitable, modeling a pathological region of cortex. Using model simulations, the effect of surgery on the seizure rate is studied. We find that removal of the hyperexcitable population is, in most cases, not the best approach to reduce the seizure rate. Removal of normal populations located at a crucial spot in the network, the "driver," is typically more effective in reducing seizure rate. This work strengthens the idea that network structure and connections may be more important than localizing the pathological node. This can explain why lesionectomy may not always be sufficient. © 2017 The Authors. Epilepsia published by Wiley Periodicals, Inc. on behalf of International League Against Epilepsy.
Agent based modeling of energy networks
International Nuclear Information System (INIS)
Gonzalez de Durana, José María; Barambones, Oscar; Kremers, Enrique; Varga, Liz
2014-01-01
Highlights: • A new approach for energy network modeling is designed and tested. • The agent-based approach is general and no technology dependent. • The models can be easily extended. • The range of applications encompasses from small to large energy infrastructures. - Abstract: Attempts to model any present or future power grid face a huge challenge because a power grid is a complex system, with feedback and multi-agent behaviors, integrated by generation, distribution, storage and consumption systems, using various control and automation computing systems to manage electricity flows. Our approach to modeling is to build upon an established model of the low voltage electricity network which is tested and proven, by extending it to a generalized energy model. But, in order to address the crucial issues of energy efficiency, additional processes like energy conversion and storage, and further energy carriers, such as gas, heat, etc., besides the traditional electrical one, must be considered. Therefore a more powerful model, provided with enhanced nodes or conversion points, able to deal with multidimensional flows, is being required. This article addresses the issue of modeling a local multi-carrier energy network. This problem can be considered as an extension of modeling a low voltage distribution network located at some urban or rural geographic area. But instead of using an external power flow analysis package to do the power flow calculations, as used in electric networks, in this work we integrate a multiagent algorithm to perform the task, in a concurrent way to the other simulation tasks, and not only for the electric fluid but also for a number of additional energy carriers. As the model is mainly focused in system operation, generation and load models are not developed
Delay and Disruption Tolerant Networking MACHETE Model
Segui, John S.; Jennings, Esther H.; Gao, Jay L.
2011-01-01
To verify satisfaction of communication requirements imposed by unique missions, as early as 2000, the Communications Networking Group at the Jet Propulsion Laboratory (JPL) saw the need for an environment to support interplanetary communication protocol design, validation, and characterization. JPL's Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE), described in Simulator of Space Communication Networks (NPO-41373) NASA Tech Briefs, Vol. 29, No. 8 (August 2005), p. 44, combines various commercial, non-commercial, and in-house custom tools for simulation and performance analysis of space networks. The MACHETE environment supports orbital analysis, link budget analysis, communications network simulations, and hardware-in-the-loop testing. As NASA is expanding its Space Communications and Navigation (SCaN) capabilities to support planned and future missions, building infrastructure to maintain services and developing enabling technologies, an important and broader role is seen for MACHETE in design-phase evaluation of future SCaN architectures. To support evaluation of the developing Delay Tolerant Networking (DTN) field and its applicability for space networks, JPL developed MACHETE models for DTN Bundle Protocol (BP) and Licklider/Long-haul Transmission Protocol (LTP). DTN is an Internet Research Task Force (IRTF) architecture providing communication in and/or through highly stressed networking environments such as space exploration and battlefield networks. Stressed networking environments include those with intermittent (predictable and unknown) connectivity, large and/or variable delays, and high bit error rates. To provide its services over existing domain specific protocols, the DTN protocols reside at the application layer of the TCP/IP stack, forming a store-and-forward overlay network. The key capabilities of the Bundle Protocol include custody-based reliability, the ability to cope with intermittent connectivity
A comprehensive Network Security Risk Model for process control networks.
Henry, Matthew H; Haimes, Yacov Y
2009-02-01
The risk of cyber attacks on process control networks (PCN) is receiving significant attention due to the potentially catastrophic extent to which PCN failures can damage the infrastructures and commodity flows that they support. Risk management addresses the coupled problems of (1) reducing the likelihood that cyber attacks would succeed in disrupting PCN operation and (2) reducing the severity of consequences in the event of PCN failure or manipulation. The Network Security Risk Model (NSRM) developed in this article provides a means of evaluating the efficacy of candidate risk management policies by modeling the baseline risk and assessing expectations of risk after the implementation of candidate measures. Where existing risk models fall short of providing adequate insight into the efficacy of candidate risk management policies due to shortcomings in their structure or formulation, the NSRM provides model structure and an associated modeling methodology that captures the relevant dynamics of cyber attacks on PCN for risk analysis. This article develops the NSRM in detail in the context of an illustrative example.
Directory of Open Access Journals (Sweden)
Hossein Foroozand
2018-03-01
Full Text Available Recently, the Entropy Ensemble Filter (EEF method was proposed to mitigate the computational cost of the Bootstrap AGGregatING (bagging method. This method uses the most informative training data sets in the model ensemble rather than all ensemble members created by the conventional bagging. In this study, we evaluate, for the first time, the application of the EEF method in Neural Network (NN modeling of El Nino-southern oscillation. Specifically, we forecast the first five principal components (PCs of sea surface temperature monthly anomaly fields over tropical Pacific, at different lead times (from 3 to 15 months, with a three-month increment for the period 1979–2017. We apply the EEF method in a multiple-linear regression (MLR model and two NN models, one using Bayesian regularization and one Levenberg-Marquardt algorithm for training, and evaluate their performance and computational efficiency relative to the same models with conventional bagging. All models perform equally well at the lead time of 3 and 6 months, while at higher lead times, the MLR model’s skill deteriorates faster than the nonlinear models. The neural network models with both bagging methods produce equally successful forecasts with the same computational efficiency. It remains to be shown whether this finding is sensitive to the dataset size.
Discrete dynamic modeling of cellular signaling networks.
Albert, Réka; Wang, Rui-Sheng
2009-01-01
Understanding signal transduction in cellular systems is a central issue in systems biology. Numerous experiments from different laboratories generate an abundance of individual components and causal interactions mediating environmental and developmental signals. However, for many signal transduction systems there is insufficient information on the overall structure and the molecular mechanisms involved in the signaling network. Moreover, lack of kinetic and temporal information makes it difficult to construct quantitative models of signal transduction pathways. Discrete dynamic modeling, combined with network analysis, provides an effective way to integrate fragmentary knowledge of regulatory interactions into a predictive mathematical model which is able to describe the time evolution of the system without the requirement for kinetic parameters. This chapter introduces the fundamental concepts of discrete dynamic modeling, particularly focusing on Boolean dynamic models. We describe this method step-by-step in the context of cellular signaling networks. Several variants of Boolean dynamic models including threshold Boolean networks and piecewise linear systems are also covered, followed by two examples of successful application of discrete dynamic modeling in cell biology.
Effective model for deconfinement at high temperature
International Nuclear Information System (INIS)
Skokov, Vladimir
2013-01-01
In this talk I consider the deconfining phase transition at nonzero temperature in a SU(N) gauge theory, using a matrix model. I present some results including the position of the deconfining critical endpoint, where the first order transition for deconfinement is washed out by the presence of massive, dynamical quarks, and properites of the phase transition in the limit of large N. I show that the model is soluble at infinite N, and exhibits a Gross-Witten-Wadia transition
Neural network modeling of associative memory: Beyond the Hopfield model
Dasgupta, Chandan
1992-07-01
A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying dynamics are used to store and associatively recall information, are described. In the first class of models, a hierarchical structure is used to store an exponentially large number of strongly correlated memories. The second class of models uses limit cycles to store and retrieve individual memories. A neurobiologically plausible network that generates low-amplitude periodic variations of activity, similar to the oscillations observed in electroencephalographic recordings, is also described. Results obtained from analytic and numerical studies of the properties of these networks are discussed.
Robust/optimal temperature profile control of a high-speed aerospace vehicle using neural networks.
Yadav, Vivek; Padhi, Radhakant; Balakrishnan, S N
2007-07-01
An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperature for a high-speed aerospace vehicle is synthesized in this paper. A 1-D distributed parameter model of a fin is developed from basic thermal physics principles. "Snapshot" solutions of the dynamics are generated with a simple dynamic inversion-based feedback controller. Empirical basis functions are designed using the "proper orthogonal decomposition" (POD) technique and the snapshot solutions. A low-order nonlinear lumped parameter system to characterize the infinite dimensional system is obtained by carrying out a Galerkin projection. An ADP-based neurocontroller with a dual heuristic programming (DHP) formulation is obtained with a single-network-adaptive-critic (SNAC) controller for this approximate nonlinear model. Actual control in the original domain is calculated with the same POD basis functions through a reverse mapping. Further contribution of this paper includes development of an online robust neurocontroller to account for unmodeled dynamics and parametric uncertainties inherent in such a complex dynamic system. A neural network (NN) weight update rule that guarantees boundedness of the weights and relaxes the need for persistence of excitation (PE) condition is presented. Simulation studies show that in a fairly extensive but compact domain, any desired temperature profile can be achieved starting from any initial temperature profile. Therefore, the ADP and NN-based controllers appear to have the potential to become controller synthesis tools for nonlinear distributed parameter systems.
Enhanced battery model including temperature effects
Rosca, B.; Wilkins, S.
2013-01-01
Within electric and hybrid vehicles, batteries are used to provide/buffer the energy required for driving. However, battery performance varies throughout the temperature range specific to automotive applications, and as such, models that describe this behaviour are required. This paper presents a
Constitutive modelling of composite biopolymer networks.
Fallqvist, B; Kroon, M
2016-04-21
The mechanical behaviour of biopolymer networks is to a large extent determined at a microstructural level where the characteristics of individual filaments and the interactions between them determine the response at a macroscopic level. Phenomena such as viscoelasticity and strain-hardening followed by strain-softening are observed experimentally in these networks, often due to microstructural changes (such as filament sliding, rupture and cross-link debonding). Further, composite structures can also be formed with vastly different mechanical properties as compared to the individual networks. In this present paper, we present a constitutive model presented in a continuum framework aimed at capturing these effects. Special care is taken to formulate thermodynamically consistent evolution laws for dissipative effects. This model, incorporating possible anisotropic network properties, is based on a strain energy function, split into an isochoric and a volumetric part. Generalisation to three dimensions is performed by numerical integration over the unit sphere. Model predictions indicate that the constitutive model is well able to predict the elastic and viscoelastic response of biological networks, and to an extent also composite structures. Copyright © 2016 Elsevier Ltd. All rights reserved.
Modelling students' knowledge organisation: Genealogical conceptual networks
Koponen, Ismo T.; Nousiainen, Maija
2018-04-01
Learning scientific knowledge is largely based on understanding what are its key concepts and how they are related. The relational structure of concepts also affects how concepts are introduced in teaching scientific knowledge. We model here how students organise their knowledge when they represent their understanding of how physics concepts are related. The model is based on assumptions that students use simple basic linking-motifs in introducing new concepts and mostly relate them to concepts that were introduced a few steps earlier, i.e. following a genealogical ordering. The resulting genealogical networks have relatively high local clustering coefficients of nodes but otherwise resemble networks obtained with an identical degree distribution of nodes but with random linking between them (i.e. the configuration-model). However, a few key nodes having a special structural role emerge and these nodes have a higher than average communicability betweenness centralities. These features agree with the empirically found properties of students' concept networks.
Modelling Users` Trust in Online Social Networks
Directory of Open Access Journals (Sweden)
Iacob Cătoiu
2014-02-01
Full Text Available Previous studies (McKnight, Lankton and Tripp, 2011; Liao, Lui and Chen, 2011 have shown the crucial role of trust when choosing to disclose sensitive information online. This is the case of online social networks users, who must disclose a certain amount of personal data in order to gain access to these online services. Taking into account privacy calculus model and the risk/benefit ratio, we propose a model of users’ trust in online social networks with four variables. We have adapted metrics for the purpose of our study and we have assessed their reliability and validity. We use a Partial Least Squares (PLS based structural equation modelling analysis, which validated all our initial assumptions, indicating that our three predictors (privacy concerns, perceived benefits and perceived risks explain 48% of the variation of users’ trust in online social networks, the resulting variable of our study. We also discuss the implications and further research opportunities of our study.
Bayesian network modelling of upper gastrointestinal bleeding
Aisha, Nazziwa; Shohaimi, Shamarina; Adam, Mohd Bakri
2013-09-01
Bayesian networks are graphical probabilistic models that represent causal and other relationships between domain variables. In the context of medical decision making, these models have been explored to help in medical diagnosis and prognosis. In this paper, we discuss the Bayesian network formalism in building medical support systems and we learn a tree augmented naive Bayes Network (TAN) from gastrointestinal bleeding data. The accuracy of the TAN in classifying the source of gastrointestinal bleeding into upper or lower source is obtained. The TAN achieves a high classification accuracy of 86% and an area under curve of 92%. A sensitivity analysis of the model shows relatively high levels of entropy reduction for color of the stool, history of gastrointestinal bleeding, consistency and the ratio of blood urea nitrogen to creatinine. The TAN facilitates the identification of the source of GIB and requires further validation.
2016-02-04
of complex systems [1]. Although the ODD protocol was originally intended for individual-based or agent-based models ( ABM ), we adopt this protocol for...applies to information transfer between air-gapped systems . Trust relationships between devices (e.g. a trust relationship created by a domain controller...prevention systems , and data leakage protection systems . 2.2 ATTACKER The model specifies an attacker who gains access to internal enclaves by
Data Driven Broiler Weight Forecasting using Dynamic Neural Network Models
DEFF Research Database (Denmark)
Johansen, Simon Vestergaard; Bendtsen, Jan Dimon; Riisgaard-Jensen, Martin
2017-01-01
In this article, the dynamic influence of environmental broiler house conditions and broiler growth is investigated. Dynamic neural network forecasting models have been trained on farm-scale broiler batch production data from 12 batches from the same house. The model forecasts future broiler weight...... and uses environmental conditions such as heating, ventilation, and temperature along with broiler behavior such as feed and water consumption. Training data and forecasting data is analyzed to explain when the model might fail at generalizing. We present ensemble broiler weight forecasts to day 7, 14, 21...
Modeling and optimization of potable water network
Energy Technology Data Exchange (ETDEWEB)
Djebedjian, B.; Rayan, M.A. [Mansoura Univ., El-Mansoura (Egypt); Herrick, A. [Suez Canal Authority, Ismailia (Egypt)
2000-07-01
Software was developed in order to optimize the design of water distribution systems and pipe networks. While satisfying all the constraints imposed such as pipe diameter and nodal pressure, it was based on a mathematical model treating looped networks. The optimum network configuration and cost are determined considering parameters like pipe diameter, flow rate, corresponding pressure and hydraulic losses. It must be understood that minimum cost is relative to the different objective functions selected. The determination of the proper objective function often depends on the operating policies of a particular company. The solution for the optimization technique was obtained by using a non-linear technique. To solve the optimal design of network, the model was derived using the sequential unconstrained minimization technique (SUMT) of Fiacco and McCormick, which decreased the number of iterations required. The pipe diameters initially assumed were successively adjusted to correspond to the existing commercial pipe diameters. The technique was then applied to a two-loop network without pumps or valves. Fed by gravity, it comprised eight pipes, 1000 m long each. The first evaluation of the method proved satisfactory. As with other methods, it failed to find the global optimum. In the future, research efforts will be directed to the optimization of networks with pumps and reservoirs. 24 refs., 3 tabs., 1 fig.
Modelling dendritic ecological networks in space: An integrated network perspective
Erin E. Peterson; Jay M. Ver Hoef; Dan J. Isaak; Jeffrey A. Falke; Marie-Josee Fortin; Chris E. Jordan; Kristina McNyset; Pascal Monestiez; Aaron S. Ruesch; Aritra Sengupta; Nicholas Som; E. Ashley Steel; David M. Theobald; Christian E. Torgersen; Seth J. Wenger
2013-01-01
Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of...
PREDIKSI FOREX MENGGUNAKAN MODEL NEURAL NETWORK
Directory of Open Access Journals (Sweden)
R. Hadapiningradja Kusumodestoni
2015-11-01
Full Text Available ABSTRAK Prediksi adalah salah satu teknik yang paling penting dalam menjalankan bisnis forex. Keputusan dalam memprediksi adalah sangatlah penting, karena dengan prediksi dapat membantu mengetahui nilai forex di waktu tertentu kedepan sehingga dapat mengurangi resiko kerugian. Tujuan dari penelitian ini dimaksudkan memprediksi bisnis fores menggunakan model neural network dengan data time series per 1 menit untuk mengetahui nilai akurasi prediksi sehingga dapat mengurangi resiko dalam menjalankan bisnis forex. Metode penelitian pada penelitian ini meliputi metode pengumpulan data kemudian dilanjutkan ke metode training, learning, testing menggunakan neural network. Setelah di evaluasi hasil penelitian ini menunjukan bahwa penerapan algoritma Neural Network mampu untuk memprediksi forex dengan tingkat akurasi prediksi 0.431 +/- 0.096 sehingga dengan prediksi ini dapat membantu mengurangi resiko dalam menjalankan bisnis forex. Kata kunci: prediksi, forex, neural network.
Temperature dependence of the multistability of lactose utilization network of Escherichia coli
Nepal, Sudip; Kumar, Pradeep
Biological systems are capable of producing multiple states out of a single set of inputs. Multistability acts like a biological switch that allows organisms to respond differently to different environmental conditions and hence plays an important role in adaptation to changing environment. One of the widely studied gene regulatory networks underlying the metabolism of bacteria is the lactose utilization network, which exhibits a multistable behavior as a function of lactose concentration. We have studied the effect of temperature on multistability of the lactose utilization network at various concentrations of thio-methylgalactoside (TMG), a synthetic lactose. We find that while the lactose utilization network exhibits a bistable behavior for temperature T >20° C , a graded response arises for temperature T lactose utilization network as a function of temperature and TMG concentration. Our results suggest that environmental conditions, in this case temperature, can alter the nature of cellular regulation of metabolism.
Artificial neural network cardiopulmonary modeling and diagnosis
Kangas, Lars J.; Keller, Paul E.
1997-01-01
The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.
Green Network Planning Model for Optical Backbones
DEFF Research Database (Denmark)
Gutierrez Lopez, Jose Manuel; Riaz, M. Tahir; Jensen, Michael
2010-01-01
on the environment in general. In network planning there are existing planning models focused on QoS provisioning, investment minimization or combinations of both and other parameters. But there is a lack of a model for designing green optical backbones. This paper presents novel ideas to be able to define......Communication networks are becoming more essential for our daily lives and critically important for industry and governments. The intense growth in the backbone traffic implies an increment of the power demands of the transmission systems. This power usage might have a significant negative effect...
A Model for Telestrok Network Evaluation
DEFF Research Database (Denmark)
Storm, Anna; Günzel, Franziska; Theiss, Stephan
2011-01-01
analysis lacking, current telestroke reimbursement by third-party payers is limited to special contracts and not included in the regular billing system. Based on a systematic literature review and expert interviews with health care economists, third-party payers and neurologists, a Markov model...... was developed from the third-party payer perspective. In principle, it enables telestroke networks to conduct cost-effectiveness studies, because the majority of the required data can be extracted from health insurance companies’ databases and the telestroke network itself. The model presents a basis...
STEADY-STATE modeling and simulation of pipeline networks for compressible fluids
Directory of Open Access Journals (Sweden)
A.L.H. Costa
1998-12-01
Full Text Available This paper presents a model and an algorithm for the simulation of pipeline networks with compressible fluids. The model can predict pressures, flow rates, temperatures and gas compositions at any point of the network. Any network configuration can be simulated; the existence of cycles is not an obstacle. Numerical results from simulated data on a proposed network are shown for illustration. The potential of the simulator is explored by the analysis of a pressure relief network, using a stochastic procedure for the evaluation of system performance.
Can spatial statistical river temperature models be transferred between catchments?
Jackson, Faye L.; Fryer, Robert J.; Hannah, David M.; Malcolm, Iain A.
2017-09-01
There has been increasing use of spatial statistical models to understand and predict river temperature (Tw) from landscape covariates. However, it is not financially or logistically feasible to monitor all rivers and the transferability of such models has not been explored. This paper uses Tw data from four river catchments collected in August 2015 to assess how well spatial regression models predict the maximum 7-day rolling mean of daily maximum Tw (Twmax) within and between catchments. Models were fitted for each catchment separately using (1) landscape covariates only (LS models) and (2) landscape covariates and an air temperature (Ta) metric (LS_Ta models). All the LS models included upstream catchment area and three included a river network smoother (RNS) that accounted for unexplained spatial structure. The LS models transferred reasonably to other catchments, at least when predicting relative levels of Twmax. However, the predictions were biased when mean Twmax differed between catchments. The RNS was needed to characterise and predict finer-scale spatially correlated variation. Because the RNS was unique to each catchment and thus non-transferable, predictions were better within catchments than between catchments. A single model fitted to all catchments found no interactions between the landscape covariates and catchment, suggesting that the landscape relationships were transferable. The LS_Ta models transferred less well, with particularly poor performance when the relationship with the Ta metric was physically implausible or required extrapolation outside the range of the data. A single model fitted to all catchments found catchment-specific relationships between Twmax and the Ta metric, indicating that the Ta metric was not transferable. These findings improve our understanding of the transferability of spatial statistical river temperature models and provide a foundation for developing new approaches for predicting Tw at unmonitored locations across
Operational Modelling of High Temperature Electrolysis (HTE)
International Nuclear Information System (INIS)
Patrick Lovera; Franck Blein; Julien Vulliet
2006-01-01
Solid Oxide Fuel Cells (SOFC) and High Temperature Electrolysis (HTE) work on two opposite processes. The basic equations (Nernst equation, corrected by a term of over-voltage) are thus very similar, only a few signs are different. An operational model, based on measurable quantities, was finalized for HTE process, and adapted to SOFCs. The model is analytical, which requires some complementary assumptions (proportionality of over-tensions to the current density, linearization of the logarithmic term in Nernst equation). It allows determining hydrogen production by HTE using a limited number of parameters. At a given temperature, only one macroscopic parameter, related to over-voltages, is needed for adjusting the model to the experimental results (SOFC), in a wide range of hydrogen flow-rates. For a given cell, this parameter follows an Arrhenius law with a satisfactory precision. The prevision in HTE process is compared to the available experimental results. (authors)
PROJECT ACTIVITY ANALYSIS WITHOUT THE NETWORK MODEL
Directory of Open Access Journals (Sweden)
S. Munapo
2012-01-01
Full Text Available
ENGLISH ABSTRACT: This paper presents a new procedure for analysing and managing activity sequences in projects. The new procedure determines critical activities, critical path, start times, free floats, crash limits, and other useful information without the use of the network model. Even though network models have been successfully used in project management so far, there are weaknesses associated with the use. A network is not easy to generate, and dummies that are usually associated with it make the network diagram complex – and dummy activities have no meaning in the original project management problem. The network model for projects can be avoided while still obtaining all the useful information that is required for project management. What are required are the activities, their accurate durations, and their predecessors.
AFRIKAANSE OPSOMMING: Die navorsing beskryf ’n nuwerwetse metode vir die ontleding en bestuur van die sekwensiële aktiwiteite van projekte. Die voorgestelde metode bepaal kritiese aktiwiteite, die kritieke pad, aanvangstye, speling, verhasing, en ander groothede sonder die gebruik van ’n netwerkmodel. Die metode funksioneer bevredigend in die praktyk, en omseil die administratiewe rompslomp van die tradisionele netwerkmodelle.
Mobility Models for Next Generation Wireless Networks Ad Hoc, Vehicular and Mesh Networks
Santi, Paolo
2012-01-01
Mobility Models for Next Generation Wireless Networks: Ad Hoc, Vehicular and Mesh Networks provides the reader with an overview of mobility modelling, encompassing both theoretical and practical aspects related to the challenging mobility modelling task. It also: Provides up-to-date coverage of mobility models for next generation wireless networksOffers an in-depth discussion of the most representative mobility models for major next generation wireless network application scenarios, including WLAN/mesh networks, vehicular networks, wireless sensor networks, and
Modeling Renewable Penertration Using a Network Economic Model
Lamont, A.
2001-03-01
This paper evaluates the accuracy of a network economic modeling approach in designing energy systems having renewable and conventional generators. The network approach models the system as a network of processes such as demands, generators, markets, and resources. The model reaches a solution by exchanging prices and quantity information between the nodes of the system. This formulation is very flexible and takes very little time to build and modify models. This paper reports an experiment designing a system with photovoltaic and base and peak fossil generators. The level of PV penetration as a function of its price and the capacities of the fossil generators were determined using the network approach and using an exact, analytic approach. It is found that the two methods agree very closely in terms of the optimal capacities and are nearly identical in terms of annual system costs.
Security Modeling on the Supply Chain Networks
Directory of Open Access Journals (Sweden)
Marn-Ling Shing
2007-10-01
Full Text Available In order to keep the price down, a purchaser sends out the request for quotation to a group of suppliers in a supply chain network. The purchaser will then choose a supplier with the best combination of price and quality. A potential supplier will try to collect the related information about other suppliers so he/she can offer the best bid to the purchaser. Therefore, confidentiality becomes an important consideration for the design of a supply chain network. Chen et al. have proposed the application of the Bell-LaPadula model in the design of a secured supply chain network. In the Bell-LaPadula model, a subject can be in one of different security clearances and an object can be in one of various security classifications. All the possible combinations of (Security Clearance, Classification pair in the Bell-LaPadula model can be thought as different states in the Markov Chain model. This paper extends the work done by Chen et al., provides more details on the Markov Chain model and illustrates how to use it to monitor the security state transition in the supply chain network.
An evolving model of online bipartite networks
Zhang, Chu-Xu; Zhang, Zi-Ke; Liu, Chuang
2013-12-01
Understanding the structure and evolution of online bipartite networks is a significant task since they play a crucial role in various e-commerce services nowadays. Recently, various attempts have been tried to propose different models, resulting in either power-law or exponential degree distributions. However, many empirical results show that the user degree distribution actually follows a shifted power-law distribution, the so-called Mandelbrot’s law, which cannot be fully described by previous models. In this paper, we propose an evolving model, considering two different user behaviors: random and preferential attachment. Extensive empirical results on two real bipartite networks, Delicious and CiteULike, show that the theoretical model can well characterize the structure of real networks for both user and object degree distributions. In addition, we introduce a structural parameter p, to demonstrate that the hybrid user behavior leads to the shifted power-law degree distribution, and the region of power-law tail will increase with the increment of p. The proposed model might shed some lights in understanding the underlying laws governing the structure of real online bipartite networks.
Gormley, Kate S G; Porter, Joanne S; Bell, Michael C; Hull, Angela D; Sanderson, William G
2013-01-01
The aims of this study were to determine the extent and distribution of an OSPAR priority habitat under current baseline ocean temperatures; to illustrate the prospect for habitat loss under a changing ocean temperature scenario; and to demonstrate the potential application of predictive habitat mapping in "future-proofing" conservation and biodiversity management. Maxent modelling and GIS environmental envelope analysis of the biogenic bed forming species, Modiolus modiolus was carried out. The Maxent model was tested and validated using 75%/25% training/test occurrence records and validated against two sampling biases (the whole study area and a 20km buffer). The model was compared to the envelope analysis and the area under the receiver operating characteristic curve (Area Under the curve; AUC) was evaluated. The performance of the Maxent model was rated as 'good' to 'excellent' on all replicated runs and low variation in the runs was recorded from the AUC values. The extent of "most suitable", "less suitable" and "unsuitable" habitat was calculated for the baseline year (2009) and the projected increased ocean temperature scenarios (2030, 2050, 2080 and 2100). A loss of 100% of "most suitable" habitat was reported by 2080. Maintaining a suitable level of protection of marine habitats/species of conservation importance may require management of the decline and migration rather than maintenance of present extent. Methods applied in this study provide the initial application of a plausible "conservation management tool".
Directory of Open Access Journals (Sweden)
Kate S G Gormley
Full Text Available The aims of this study were to determine the extent and distribution of an OSPAR priority habitat under current baseline ocean temperatures; to illustrate the prospect for habitat loss under a changing ocean temperature scenario; and to demonstrate the potential application of predictive habitat mapping in "future-proofing" conservation and biodiversity management. Maxent modelling and GIS environmental envelope analysis of the biogenic bed forming species, Modiolus modiolus was carried out. The Maxent model was tested and validated using 75%/25% training/test occurrence records and validated against two sampling biases (the whole study area and a 20km buffer. The model was compared to the envelope analysis and the area under the receiver operating characteristic curve (Area Under the curve; AUC was evaluated. The performance of the Maxent model was rated as 'good' to 'excellent' on all replicated runs and low variation in the runs was recorded from the AUC values. The extent of "most suitable", "less suitable" and "unsuitable" habitat was calculated for the baseline year (2009 and the projected increased ocean temperature scenarios (2030, 2050, 2080 and 2100. A loss of 100% of "most suitable" habitat was reported by 2080. Maintaining a suitable level of protection of marine habitats/species of conservation importance may require management of the decline and migration rather than maintenance of present extent. Methods applied in this study provide the initial application of a plausible "conservation management tool".
An autocatalytic network model for stock markets
Caetano, Marco Antonio Leonel; Yoneyama, Takashi
2015-02-01
The stock prices of companies with businesses that are closely related within a specific sector of economy might exhibit movement patterns and correlations in their dynamics. The idea in this work is to use the concept of autocatalytic network to model such correlations and patterns in the trends exhibited by the expected returns. The trends are expressed in terms of positive or negative returns within each fixed time interval. The time series derived from these trends is then used to represent the movement patterns by a probabilistic boolean network with transitions modeled as an autocatalytic network. The proposed method might be of value in short term forecasting and identification of dependencies. The method is illustrated with a case study based on four stocks of companies in the field of natural resource and technology.
Directory of Open Access Journals (Sweden)
Ali Khazaei
2014-07-01
Full Text Available In this work, artificial neural network (ANN has been employed to propose a practical model for predicting the surface tension of multi-component mixtures. In order to develop a reliable model based on the ANN, a comprehensive experimental data set including 15 ternary liquid mixtures at different temperatures was employed. These systems consist of 777 data points generally containing hydrocarbon components. The ANN model has been developed as a function of temperature, critical properties, and acentric factor of the mixture according to conventional corresponding-state models. 80% of the data points were employed for training ANN and the remaining data were utilized for testing the generated model. The average absolute relative deviations (AARD% of the model for the training set, the testing set, and the total data points were obtained 1.69, 1.86, and 1.72 respectively. Comparing the results with Flory theory, Brok-Bird equation, and group contribution theory has proved the high prediction capability of the attained model.
Keystone Business Models for Network Security Processors
Directory of Open Access Journals (Sweden)
Arthur Low
2013-07-01
Full Text Available Network security processors are critical components of high-performance systems built for cybersecurity. Development of a network security processor requires multi-domain experience in semiconductors and complex software security applications, and multiple iterations of both software and hardware implementations. Limited by the business models in use today, such an arduous task can be undertaken only by large incumbent companies and government organizations. Neither the “fabless semiconductor” models nor the silicon intellectual-property licensing (“IP-licensing” models allow small technology companies to successfully compete. This article describes an alternative approach that produces an ongoing stream of novel network security processors for niche markets through continuous innovation by both large and small companies. This approach, referred to here as the "business ecosystem model for network security processors", includes a flexible and reconfigurable technology platform, a “keystone” business model for the company that maintains the platform architecture, and an extended ecosystem of companies that both contribute and share in the value created by innovation. New opportunities for business model innovation by participating companies are made possible by the ecosystem model. This ecosystem model builds on: i the lessons learned from the experience of the first author as a senior integrated circuit architect for providers of public-key cryptography solutions and as the owner of a semiconductor startup, and ii the latest scholarly research on technology entrepreneurship, business models, platforms, and business ecosystems. This article will be of interest to all technology entrepreneurs, but it will be of particular interest to owners of small companies that provide security solutions and to specialized security professionals seeking to launch their own companies.
Modeling and Simulation Network Data Standards
2011-09-30
approaches . 2.3. JNAT. JNAT is a Web application that provides connectivity and network analysis capability. JNAT uses propagation models and low-fidelity...COMBATXXI Movement Logger Data Output Dictionary. Field # Geocentric Coordinates (GCC) Heading Geodetic Coordinates (GDC) Heading Universal...B-8 Field # Geocentric Coordinates (GCC) Heading Geodetic Coordinates (GDC) Heading Universal Transverse Mercator (UTM) Heading
The Kuramoto model in complex networks
Rodrigues, Francisco A.; Peron, Thomas K. DM.; Ji, Peng; Kurths, Jürgen
2016-01-01
Synchronization of an ensemble of oscillators is an emergent phenomenon present in several complex systems, ranging from social and physical to biological and technological systems. The most successful approach to describe how coherent behavior emerges in these complex systems is given by the paradigmatic Kuramoto model. This model has been traditionally studied in complete graphs. However, besides being intrinsically dynamical, complex systems present very heterogeneous structure, which can be represented as complex networks. This report is dedicated to review main contributions in the field of synchronization in networks of Kuramoto oscillators. In particular, we provide an overview of the impact of network patterns on the local and global dynamics of coupled phase oscillators. We cover many relevant topics, which encompass a description of the most used analytical approaches and the analysis of several numerical results. Furthermore, we discuss recent developments on variations of the Kuramoto model in networks, including the presence of noise and inertia. The rich potential for applications is discussed for special fields in engineering, neuroscience, physics and Earth science. Finally, we conclude by discussing problems that remain open after the last decade of intensive research on the Kuramoto model and point out some promising directions for future research.
An architectural model for network interconnection
van Sinderen, Marten J.; Vissers, C.A.; Kalin, T.
1983-01-01
This paper presents a technique of successive decomposition of a common users' activity to illustrate the problems of network interconnection. The criteria derived from this approach offer a structuring principle which is used to develop an architectural model that embeds heterogeneous subnetworks
Computational Modeling of Complex Protein Activity Networks
Schivo, Stefano; Leijten, Jeroen; Karperien, Marcel; Post, Janine N.; Prignet, Claude
2017-01-01
Because of the numerous entities interacting, the complexity of the networks that regulate cell fate makes it impossible to analyze and understand them using the human brain alone. Computational modeling is a powerful method to unravel complex systems. We recently described the development of a
A Model of Mental State Transition Network
Xiang, Hua; Jiang, Peilin; Xiao, Shuang; Ren, Fuji; Kuroiwa, Shingo
Emotion is one of the most essential and basic attributes of human intelligence. Current AI (Artificial Intelligence) research is concentrating on physical components of emotion, rarely is it carried out from the view of psychology directly(1). Study on the model of artificial psychology is the first step in the development of human-computer interaction. As affective computing remains unpredictable, creating a reasonable mental model becomes the primary task for building a hybrid system. A pragmatic mental model is also the fundament of some key topics such as recognition and synthesis of emotions. In this paper a Mental State Transition Network Model(2) is proposed to detect human emotions. By a series of psychological experiments, we present a new way to predict coming human's emotions depending on the various current emotional states under various stimuli. Besides, people in different genders and characters are taken into consideration in our investigation. According to the psychological experiments data derived from 200 questionnaires, a Mental State Transition Network Model for describing the transitions in distribution among the emotions and relationships between internal mental situations and external are concluded. Further more the coefficients of the mental transition network model were achieved. Comparing seven relative evaluating experiments, an average precision rate of 0.843 is achieved using a set of samples for the proposed model.
UAV Trajectory Modeling Using Neural Networks
Xue, Min
2017-01-01
Massive small unmanned aerial vehicles are envisioned to operate in the near future. While there are lots of research problems need to be addressed before dense operations can happen, trajectory modeling remains as one of the keys to understand and develop policies, regulations, and requirements for safe and efficient unmanned aerial vehicle operations. The fidelity requirement of a small unmanned vehicle trajectory model is high because these vehicles are sensitive to winds due to their small size and low operational altitude. Both vehicle control systems and dynamic models are needed for trajectory modeling, which makes the modeling a great challenge, especially considering the fact that manufactures are not willing to share their control systems. This work proposed to use a neural network approach for modelling small unmanned vehicle's trajectory without knowing its control system and bypassing exhaustive efforts for aerodynamic parameter identification. As a proof of concept, instead of collecting data from flight tests, this work used the trajectory data generated by a mathematical vehicle model for training and testing the neural network. The results showed great promise because the trained neural network can predict 4D trajectories accurately, and prediction errors were less than 2:0 meters in both temporal and spatial dimensions.
Modeling quantum fluid dynamics at nonzero temperatures
Berloff, Natalia G.; Brachet, Marc; Proukakis, Nick P.
2014-01-01
The detailed understanding of the intricate dynamics of quantum fluids, in particular in the rapidly growing subfield of quantum turbulence which elucidates the evolution of a vortex tangle in a superfluid, requires an in-depth understanding of the role of finite temperature in such systems. The Landau two-fluid model is the most successful hydrodynamical theory of superfluid helium, but by the nature of the scale separations it cannot give an adequate description of the processes involving vortex dynamics and interactions. In our contribution we introduce a framework based on a nonlinear classical-field equation that is mathematically identical to the Landau model and provides a mechanism for severing and coalescence of vortex lines, so that the questions related to the behavior of quantized vortices can be addressed self-consistently. The correct equation of state as well as nonlocality of interactions that leads to the existence of the roton minimum can also be introduced in such description. We review and apply the ideas developed for finite-temperature description of weakly interacting Bose gases as possible extensions and numerical refinements of the proposed method. We apply this method to elucidate the behavior of the vortices during expansion and contraction following the change in applied pressure. We show that at low temperatures, during the contraction of the vortex core as the negative pressure grows back to positive values, the vortex line density grows through a mechanism of vortex multiplication. This mechanism is suppressed at high temperatures. PMID:24704874
Modeling Insurgent Network Structure and Dynamics
Gabbay, Michael; Thirkill-Mackelprang, Ashley
2010-03-01
We present a methodology for mapping insurgent network structure based on their public rhetoric. Indicators of cooperative links between insurgent groups at both the leadership and rank-and-file levels are used, such as joint policy statements or joint operations claims. In addition, a targeting policy measure is constructed on the basis of insurgent targeting claims. Network diagrams which integrate these measures of insurgent cooperation and ideology are generated for different periods of the Iraqi and Afghan insurgencies. The network diagrams exhibit meaningful changes which track the evolution of the strategic environment faced by insurgent groups. Correlations between targeting policy and network structure indicate that insurgent targeting claims are aimed at establishing a group identity among the spectrum of rank-and-file insurgency supporters. A dynamical systems model of insurgent alliance formation and factionalism is presented which evolves the relationship between insurgent group dyads as a function of their ideological differences and their current relationships. The ability of the model to qualitatively and quantitatively capture insurgent network dynamics observed in the data is discussed.
Hybrid simulation models of production networks
Kouikoglou, Vassilis S
2001-01-01
This book is concerned with a most important area of industrial production, that of analysis and optimization of production lines and networks using discrete-event models and simulation. The book introduces a novel approach that combines analytic models and discrete-event simulation. Unlike conventional piece-by-piece simulation, this method observes a reduced number of events between which the evolution of the system is tracked analytically. Using this hybrid approach, several models are developed for the analysis of production lines and networks. The hybrid approach combines speed and accuracy for exceptional analysis of most practical situations. A number of optimization problems, involving buffer design, workforce planning, and production control, are solved through the use of hybrid models.
Propagating semantic information in biochemical network models
Directory of Open Access Journals (Sweden)
Schulz Marvin
2012-01-01
Full Text Available Abstract Background To enable automatic searches, alignments, and model combination, the elements of systems biology models need to be compared and matched across models. Elements can be identified by machine-readable biological annotations, but assigning such annotations and matching non-annotated elements is tedious work and calls for automation. Results A new method called "semantic propagation" allows the comparison of model elements based not only on their own annotations, but also on annotations of surrounding elements in the network. One may either propagate feature vectors, describing the annotations of individual elements, or quantitative similarities between elements from different models. Based on semantic propagation, we align partially annotated models and find annotations for non-annotated model elements. Conclusions Semantic propagation and model alignment are included in the open-source library semanticSBML, available on sourceforge. Online services for model alignment and for annotation prediction can be used at http://www.semanticsbml.org.
Model Predictive Control of Sewer Networks
DEFF Research Database (Denmark)
Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik
2016-01-01
The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....
Modeling Multistandard Wireless Networks in OPNET
DEFF Research Database (Denmark)
Zakrzewska, Anna; Berger, Michael Stübert; Ruepp, Sarah Renée
2011-01-01
Future wireless communication is emerging towards one heterogeneous platform. In this new environment wireless access will be provided by multiple radio technologies that are cooperating and complementing one another. The paper investigates the possibilities of developing such a multistandard sys...... system using OPNET Modeler. A network model consisting of LTE interworking with WLAN and WiMAX is considered from the radio resource management perspective. In particular, implementing a joint packet scheduler across multiple systems is discussed more in detail....
Modelling dendritic ecological networks in space: anintegrated network perspective
Peterson, Erin E.; Ver Hoef, Jay M.; Isaak, Dan J.; Falke, Jeffrey A.; Fortin, Marie-Josée; Jordon, Chris E.; McNyset, Kristina; Monestiez, Pascal; Ruesch, Aaron S.; Sengupta, Aritra; Som, Nicholas; Steel, E. Ashley; Theobald, David M.; Torgersen, Christian E.; Wenger, Seth J.
2013-01-01
Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of ecological networks, or in 2-D space, may be inadequate for studying the influence of structure and connectivity on ecological processes within DENs. We propose a conceptual taxonomy of network analysis methods that account for DEN characteristics to varying degrees and provide a synthesis of the different approaches within
Unified Model for Generation Complex Networks with Utility Preferential Attachment
International Nuclear Information System (INIS)
Wu Jianjun; Gao Ziyou; Sun Huijun
2006-01-01
In this paper, based on the utility preferential attachment, we propose a new unified model to generate different network topologies such as scale-free, small-world and random networks. Moreover, a new network structure named super scale network is found, which has monopoly characteristic in our simulation experiments. Finally, the characteristics of this new network are given.
Wireless sensor networks for canopy temperature sensing and irrigation management
For researchers, canopy temperature measurements have proven useful in characterizing crop water stress and developing protocols for irrigation management. Today, there is heightened interest in using remote canopy temperature measurements for real-time irrigation scheduling. However, without the us...
Global Historical Climatology Network - Monthly Temperature, Version 4 (BETA)
National Oceanic and Atmospheric Administration, Department of Commerce — Only available as BETA release. The GHCN-Monthly Temperature Version 4 dataset consists of monthly mean temperature - both raw and bias corrected data. A full...
Functional model of biological neural networks.
Lo, James Ting-Ho
2010-12-01
A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.
On traffic modelling in GPRS networks
DEFF Research Database (Denmark)
Madsen, Tatiana Kozlova; Schwefel, Hans-Peter; Prasad, Ramjee
2005-01-01
Optimal design and dimensioning of wireless data networks, such as GPRS, requires the knowledge of traffic characteristics of different data services. This paper presents an in-detail analysis of an IP-level traffic measurements taken in an operational GPRS network. The data measurements reported...... here are done at the Gi interface. The aim of this paper is to reveal some key statistics of GPRS data applications and to validate if the existing traffic models can adequately describe traffic volume and inter-arrival time distribution for different services. Additionally, we present a method of user...
A sub-circuit MOSFET model with a wide temperature range including cryogenic temperature
Energy Technology Data Exchange (ETDEWEB)
Jia Kan; Sun Weifeng; Shi Longxing, E-mail: jiakan.01@gmail.com [National ASIC System Engineering Research Center, Southeast University, Nanjing 210096 (China)
2011-06-15
A sub-circuit SPICE model of a MOSFET for low temperature operation is presented. Two resistors are introduced for the freeze-out effect, and the explicit behavioral models are developed for them. The model can be used in a wide temperature range covering both cryogenic temperature and regular temperatures. (semiconductor devices)
Modeling forces in high-temperature superconductors
International Nuclear Information System (INIS)
Turner, L. R.; Foster, M. W.
1997-01-01
We have developed a simple model that uses computed shielding currents to determine the forces acting on a high-temperature superconductor (HTS). The model has been applied to measurements of the force between HTS and permanent magnets (PM). Results show the expected hysteretic variation of force as the HTS moves first toward and then away from a permanent magnet, including the reversal of the sign of the force. Optimization of the shielding currents is carried out through a simulated annealing algorithm in a C++ program that repeatedly calls a commercial electromagnetic software code. Agreement with measured forces is encouraging
Energy Technology Data Exchange (ETDEWEB)
NONE
2015-07-01
Numerous methods have been developed around the world to model the dynamic behavior and detect a faulty operating mode of a temperature sensor. In this context, we present in this study a new method based on the dependence between the fuel assembly temperature profile on control rods positions, and the coolant flow rate in a nuclear reactor. This seems to be possible since the insertion of control rods at different axial positions and variations in flow rate of the reactor coolant results in different produced thermal power in the reactor. This is closely linked to the instant fuel rod temperature profile. In a first step, we selected parameters to be used and confirmed the adequate correlation between the chosen parameters and those to be estimated by the proposed monitoring system. In the next step, we acquired and de-noised the data of corresponding parameters, the qualified data is then used to design and train the artificial neural network. The effective data denoising was done by using the wavelet transform to remove a various kind of artifacts such as inherent noise. With the suitable choice of wavelet level and smoothing method, it was possible for us to remove all the non-required artifacts with a view to verify and analyze the considered signal. In our work, several potential mother wavelet functions (Haar, Daubechies, Bi-orthogonal, Reverse Bi-orthogonal, Discrete Meyer and Symlets) were investigated to find the most similar function with the being processed signals. To implement the proposed monitoring system for the fuel rod temperature sensor (03 wire RTD sensor), we used the Bayesian artificial neural network 'BNN' technique to model the dynamic behavior of the considered sensor, the system correlate the estimated values with the measured for the concretization of the proposed system we propose an FPGA (field programmable gate array) implementation. The monitoring system use the correlation. (authors)
A stream temperature model for the Peace-Athabasca River basin
Morales-Marin, L. A.; Rokaya, P.; Wheater, H. S.; Lindenschmidt, K. E.
2017-12-01
Water temperature plays a fundamental role in water ecosystem functioning. Because it regulates flow energy and metabolic rates in organism productivity over a broad spectrum of space and time scales, water temperature constitutes an important indicator of aquatic ecosystems health. In cold region basins, stream water temperature modelling is also fundamental to predict ice freeze-up and break-up events in order to improve flood management. Multiple model approaches such as linear and multivariable regression methods, neural network and thermal energy budged models have been developed and implemented to simulate stream water temperature. Most of these models have been applied to specific stream reaches and trained using observed data, but very little has been done to simulate water temperature in large catchment river networks. We present the coupling of RBM model, a semi-Lagrangian water temperature model for advection-dominated river system, and MESH, a semi-distributed hydrological model, to simulate stream water temperature in river catchments. The coupled models are implemented in the Peace-Athabasca River basin in order to analyze the variation in stream temperature regimes under changing hydrological and meteorological conditions. Uncertainty of stream temperature simulations is also assessed in order to determine the degree of reliability of the estimates.
A Networks Approach to Modeling Enzymatic Reactions.
Imhof, P
2016-01-01
Modeling enzymatic reactions is a demanding task due to the complexity of the system, the many degrees of freedom involved and the complex, chemical, and conformational transitions associated with the reaction. Consequently, enzymatic reactions are not determined by precisely one reaction pathway. Hence, it is beneficial to obtain a comprehensive picture of possible reaction paths and competing mechanisms. By combining individually generated intermediate states and chemical transition steps a network of such pathways can be constructed. Transition networks are a discretized representation of a potential energy landscape consisting of a multitude of reaction pathways connecting the end states of the reaction. The graph structure of the network allows an easy identification of the energetically most favorable pathways as well as a number of alternative routes. © 2016 Elsevier Inc. All rights reserved.
A improved Network Security Situation Awareness Model
Directory of Open Access Journals (Sweden)
Li Fangwei
2015-08-01
Full Text Available In order to reflect the situation of network security assessment performance fully and accurately, a new network security situation awareness model based on information fusion was proposed. Network security situation is the result of fusion three aspects evaluation. In terms of attack, to improve the accuracy of evaluation, a situation assessment method of DDoS attack based on the information of data packet was proposed. In terms of vulnerability, a improved Common Vulnerability Scoring System (CVSS was raised and maked the assessment more comprehensive. In terms of node weights, the method of calculating the combined weights and optimizing the result by Sequence Quadratic Program (SQP algorithm which reduced the uncertainty of fusion was raised. To verify the validity and necessity of the method, a testing platform was built and used to test through evaluating 2000 DAPRA data sets. Experiments show that the method can improve the accuracy of evaluation results.
Spatial Models and Networks of Living Systems
DEFF Research Database (Denmark)
Juul, Jeppe Søgaard
When studying the dynamics of living systems, insight can often be gained by developing a mathematical model that can predict future behaviour of the system or help classify system characteristics. However, in living cells, organisms, and especially groups of interacting individuals, a large number...... variables of the system. However, this approach disregards any spatial structure of the system, which may potentially change the behaviour drastically. An alternative approach is to construct a cellular automaton with nearest neighbour interactions, or even to model the system as a complex network...... with interactions defined by network topology. In this thesis I first describe three different biological models of ageing and cancer, in which spatial structure is important for the system dynamics. I then turn to describe characteristics of ecosystems consisting of three cyclically interacting species...
Fractional virus epidemic model on financial networks
Directory of Open Access Journals (Sweden)
Balci Mehmet Ali
2016-01-01
Full Text Available In this study, we present an epidemic model that characterizes the behavior of a financial network of globally operating stock markets. Since the long time series have a global memory effect, we represent our model by using the fractional calculus. This model operates on a network, where vertices are the stock markets and edges are constructed by the correlation distances. Thereafter, we find an analytical solution to commensurate system and use the well-known differential transform method to obtain the solution of incommensurate system of fractional differential equations. Our findings are confirmed and complemented by the data set of the relevant stock markets between 2006 and 2016. Rather than the hypothetical values, we use the Hurst Exponent of each time series to approximate the fraction size and graph theoretical concepts to obtain the variables.
Entanglement effects in model polymer networks
Everaers, R.; Kremer, K.
The influence of topological constraints on the local dynamics in cross-linked polymer melts and their contribution to the elastic properties of rubber elastic systems are a long standing problem in statistical mechanics. Polymer networks with diamond lattice connectivity (Everaers and Kremer 1995, Everaers and Kremer 1996a) are idealized model systems which isolate the effect of topology conservation from other sources of quenched disorder. We study their behavior in molecular dynamics simulations under elongational strain. In our analysis we compare the measured, purely entropic shear moduli G to the predictions of statistical mechanical models of rubber elasticity, making extensive use of the microscopic structural and topological information available in computer simulations. We find (Everaers and Kremer 1995) that the classical models of rubber elasticity underestimate the true change in entropy in a deformed network significantly, because they neglect the tension along the contour of the strands which cannot relax due to entanglements (Everaers and Kremer (in preparation)). This contribution and the fluctuations in strained systems seem to be well described by the constrained mode model (Everaers 1998) which allows to treat the crossover from classical rubber elasticity to the tube model for polymer networks with increasing strand length within one transparant formalism. While this is important for the description of the effects we try to do a first quantitative step towards their explanation by topological considerations. We show (Everaers and Kremer 1996a) that for the comparatively short strand lengths of our diamond networks the topology contribution to the shear modulus is proportional to the density of entangled mesh pairs with non-zero Gauss linking number. Moreover, the prefactor can be estimated consistently within a rather simple model developed by Vologodskii et al. and by Graessley and Pearson, which is based on the definition of an entropic
Directory of Open Access Journals (Sweden)
M. Susmikanti
2015-12-01
Full Text Available In a nuclear industry area, high temperature treatment of materials is a factor which requires special attention. Assessment needs to be conducted on the properties of the materials used, including the strength of the materials. The measurement of material properties under thermal processes may reflect residual stresses. The use of Genetic Algorithm (GA to determine the optimal residual stress is one way to determine the strength of a material. In residual stress modeling with several parameters, it is sometimes difficult to solve for the optimal value through analytical or numerical calculations. Here, GA is an efficient algorithm which can generate the optimal values, both minima and maxima. The purposes of this research are to obtain the optimization of variable in residual stress models using GA and to predict the center of residual stress distribution, using fuzzy neural network (FNN while the artificial neural network (ANN used for modeling. In this work a single-material 316/316L stainless steel bar is modeled. The minimal residual stresses of the material at high temperatures were obtained with GA and analytical calculations. At a temperature of 6500C, the GA optimal residual stress estimation converged at –711.3689 MPa at adistance of 0.002934 mm from center point, whereas the analytical calculation result at that temperature and position is -975.556 MPa . At a temperature of 8500C, the GA result was -969.868 MPa at 0.002757 mm from the center point, while with analytical result was -1061.13 MPa. The difference in residual stress between GA and analytical results at a temperatureof6500C is about 27 %, while at 8500C it is 8.67 %. The distribution of residual stress showed a grouping concentrated around a coordinate of (-76; 76 MPa. The residuals stress model is a degree-two polynomial with coefficients of 50.33, -76.54, and -55.2, respectively, with a standard deviation of 7.874.
DEFF Research Database (Denmark)
Sindbæk, Søren Michael
2015-01-01
preserve patterns of thisinteraction. Formal network analysis and modelling holds the potential to identify anddemonstrate such patterns, where traditional methods often prove inadequate. Thearchaeological study of communication networks in the past, however, calls for radically different analytical...... this is not a problem of network analysis, but network synthesis: theclassic problem of cracking codes or reconstructing black-box circuits. It is proposedthat archaeological approaches to network synthesis must involve a contextualreading of network data: observations arising from individual contexts, morphologies...
Directory of Open Access Journals (Sweden)
Tatar Afshin
2016-03-01
Full Text Available Raw natural gases usually contain water. It is very important to remove the water from these gases through dehydration processes due to economic reasons and safety considerations. One of the most important methods for water removal from these gases is using dehydration units which use Triethylene glycol (TEG. The TEG concentration at which all water is removed and dew point characteristics of mixture are two important parameters, which should be taken into account in TEG dehydration system. Hence, developing a reliable and accurate model to predict the performance of such a system seems to be very important in gas engineering operations. This study highlights the use of intelligent modeling techniques such as Multilayer perceptron (MLP and Radial Basis Function Neural Network (RBF-ANN to predict the equilibrium water dew point in a stream of natural gas based on the TEG concentration of stream and contractor temperature. Literature data set used in this study covers temperatures from 10 °C to 80 °C and TEG concentrations from 90.000% to 99.999%. Results showed that both models are accurate in prediction of experimental data and the MLP model gives more accurate predictions compared to RBF model.
Performance modeling, loss networks, and statistical multiplexing
Mazumdar, Ravi
2009-01-01
This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of understanding the phenomenon of statistical multiplexing. The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the important ideas of Palm distributions associated with traffic models and their role in performance measures. Also presented are recent ideas of large buffer, and many sources asymptotics that play an important role in understanding statistical multiplexing. I
Alloy model for high temperature superconductors
International Nuclear Information System (INIS)
Weissmann, M.; Saul, A.
1991-07-01
An alloy model is proposed for the electronic structure of high temperature superconductors. It is based on the assumption that holes and extra electrons are localized in small copper oxygen clusters, that would be the components of such alloy. This model, when used together with quantum chemical calculations on small clusters, can explain the structure observed in the experimental densities of states of both hole and electron superconductors close to the Fermi energy. The main point is the strong dependence of the energy level distribution and composition on the number of electrons in a cluster. The alloy model also suggests a way to correlate Tc with the number of holes, or extra electrons, and the number of adequate clusters to locate them. (author). 21 refs, 4 figs, 1 tab
Artificial Neural Network Model for Predicting Compressive
Directory of Open Access Journals (Sweden)
Salim T. Yousif
2013-05-01
Full Text Available Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature. The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor affecting the output of the model. The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.
UAV Trajectory Modeling Using Neural Networks
Xue, Min
2017-01-01
Large amount of small Unmanned Aerial Vehicles (sUAVs) are projected to operate in the near future. Potential sUAV applications include, but not limited to, search and rescue, inspection and surveillance, aerial photography and video, precision agriculture, and parcel delivery. sUAVs are expected to operate in the uncontrolled Class G airspace, which is at or below 500 feet above ground level (AGL), where many static and dynamic constraints exist, such as ground properties and terrains, restricted areas, various winds, manned helicopters, and conflict avoidance among sUAVs. How to enable safe, efficient, and massive sUAV operations at the low altitude airspace remains a great challenge. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative works on establishing infrastructure and developing policies, requirement, and rules to enable safe and efficient sUAVs' operations. To achieve this goal, it is important to gain insights of future UTM traffic operations through simulations, where the accurate trajectory model plays an extremely important role. On the other hand, like what happens in current aviation development, trajectory modeling should also serve as the foundation for any advanced concepts and tools in UTM. Accurate models of sUAV dynamics and control systems are very important considering the requirement of the meter level precision in UTM operations. The vehicle dynamics are relatively easy to derive and model, however, vehicle control systems remain unknown as they are usually kept by manufactures as a part of intellectual properties. That brings challenges to trajectory modeling for sUAVs. How to model the vehicle's trajectories with unknown control system? This work proposes to use a neural network to model a vehicle's trajectory. The neural network is first trained to learn the vehicle's responses at numerous conditions. Once being fully trained, given current vehicle states, winds, and desired future trajectory, the neural
Mapping and modeling of physician collaboration network.
Uddin, Shahadat; Hamra, Jafar; Hossain, Liaquat
2013-09-10
Effective provisioning of healthcare services during patient hospitalization requires collaboration involving a set of interdependent complex tasks, which needs to be carried out in a synergistic manner. Improved patients' outcome during and after hospitalization has been attributed to how effective different health services provisioning groups carry out their tasks in a coordinated manner. Previous studies have documented the underlying relationships between collaboration among physicians on the effective outcome in delivering health services for improved patient outcomes. However, there are very few systematic empirical studies with a focus on the effect of collaboration networks among healthcare professionals and patients' medical condition. On the basis of the fact that collaboration evolves among physicians when they visit a common hospitalized patient, in this study, we first propose an approach to map collaboration network among physicians from their visiting information to patients. We termed this network as physician collaboration network (PCN). Then, we use exponential random graph (ERG) models to explore the microlevel network structures of PCNs and their impact on hospitalization cost and hospital readmission rate. ERG models are probabilistic models that are presented by locally determined explanatory variables and can effectively identify structural properties of networks such as PCN. It simplifies a complex structure down to a combination of basic parameters such as 2-star, 3-star, and triangle. By applying our proposed mapping approach and ERG modeling technique to the electronic health insurance claims dataset of a very large Australian health insurance organization, we construct and model PCNs. We notice that the 2-star (subset of 3 nodes in which 1 node is connected to each of the other 2 nodes) parameter of ERG has significant impact on hospitalization cost. Further, we identify that triangle (subset of 3 nodes in which each node is connected to
Energy Technology Data Exchange (ETDEWEB)
Mohagheghian, Erfan [Memorial University of Newfoundland, St. John' s (Canada); Zafarian-Rigaki, Habiballah; Motamedi-Ghahfarrokhi, Yaser; Hemmati-Sarapardeh, Abdolhossein [Amirkabir University of Technology, Tehran (Iran, Islamic Republic of)
2015-10-15
Carbon dioxide injection, which is widely used as an enhanced oil recovery (EOR) method, has the potential of being coupled with CO{sub 2} sequestration and reducing the emission of greenhouse gas. Hence, knowing the compressibility factor of carbon dioxide is of a vital significance. Compressibility factor (Z-factor) is traditionally measured through time consuming, expensive and cumbersome experiments. Hence, developing a fast, robust and accurate model for its estimation is necessary. In this study, a new reliable model on the basis of feed forward artificial neural networks is presented to predict CO{sub 2} compressibility factor. Reduced temperature and pressure were selected as the input parameters of the proposed model. To evaluate and compare the results of the developed model with pre-existing models, both statistical and graphical error analyses were employed. The results indicated that the proposed model is more reliable and accurate compared to pre-existing models in a wide range of temperature (up to 1,273.15 K) and pressure (up to 140MPa). Furthermore, by employing the relevancy factor, the effect of pressure and temprature on the Z-factor of CO{sub 2} was compared for below and above the critical pressure of CO{sub 2}, and the physcially expected trends were observed. Finally, to identify the probable outliers and applicability domain of the proposed ANN model, both numerical and graphical techniques based on Leverage approach were performed. The results illustrated that only 1.75% of the experimental data points were located out of the applicability domain of the proposed model. As a result, the developed model is reliable for the prediction of CO{sub 2} compressibility factor.
Modeling In-Network Aggregation in VANETs
Dietzel, Stefan; Kargl, Frank; Heijenk, Geert; Schaub, Florian
2011-01-01
The multitude of applications envisioned for vehicular ad hoc networks requires efficient communication and dissemination mechanisms to prevent network congestion. In-network data aggregation promises to reduce bandwidth requirements and enable scalability in large vehicular networks. However, most
Different Epidemic Models on Complex Networks
International Nuclear Information System (INIS)
Zhang Haifeng; Small, Michael; Fu Xinchu
2009-01-01
Models for diseases spreading are not just limited to SIS or SIR. For instance, for the spreading of AIDS/HIV, the susceptible individuals can be classified into different cases according to their immunity, and similarly, the infected individuals can be sorted into different classes according to their infectivity. Moreover, some diseases may develop through several stages. Many authors have shown that the individuals' relation can be viewed as a complex network. So in this paper, in order to better explain the dynamical behavior of epidemics, we consider different epidemic models on complex networks, and obtain the epidemic threshold for each case. Finally, we present numerical simulations for each case to verify our results.
Directory of Open Access Journals (Sweden)
Benjamin H. Letcher
2016-02-01
Full Text Available Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59°C, identified a clear warming trend (0.63 °C decade−1 and a widening of the synchronized period (29 d decade−1. We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (∼0.05% average drop in RMSE with 10% fewer days with data. Missing all data for a year decreased performance (∼0.6 °C jump in RMSE, but this decrease was moderated when data were available from other streams in the network.
Centralized Bayesian reliability modelling with sensor networks
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Sečkárová, Vladimíra
2013-01-01
Roč. 19, č. 5 (2013), s. 471-482 ISSN 1387-3954 R&D Projects: GA MŠk 7D12004 Grant - others:GA MŠk(CZ) SVV-265315 Keywords : Bayesian modelling * Sensor network * Reliability Subject RIV: BD - Theory of Information Impact factor: 0.984, year: 2013 http://library.utia.cas.cz/separaty/2013/AS/dedecius-0392551.pdf
Modelling Pollutant Dispersion in a Street Network
Salem, N. Ben; Garbero, V.; Salizzoni, P.; Lamaison, G.; Soulhac, L.
2015-04-01
This study constitutes a further step in the analysis of the performances of a street network model to simulate atmospheric pollutant dispersion in urban areas. The model, named SIRANE, is based on the decomposition of the urban atmosphere into two sub-domains: the urban boundary layer, whose dynamics is assumed to be well established, and the urban canopy, represented as a series of interconnected boxes. Parametric laws govern the mass exchanges between the boxes under the assumption that the pollutant dispersion within the canopy can be fully simulated by modelling three main bulk transfer phenomena: channelling along street axes, transfers at street intersections, and vertical exchange between street canyons and the overlying atmosphere. Here, we aim to evaluate the reliability of the parametrizations adopted to simulate these phenomena, by focusing on their possible dependence on the external wind direction. To this end, we test the model against concentration measurements within an idealized urban district whose geometrical layout closely matches the street network represented in SIRANE. The analysis is performed for an urban array with a fixed geometry and a varying wind incidence angle. The results show that the model provides generally good results with the reference parametrizations adopted in SIRANE and that its performances are quite robust for a wide range of the model parameters. This proves the reliability of the street network approach in simulating pollutant dispersion in densely built city districts. The results also show that the model performances may be improved by considering a dependence of the wind fluctuations at street intersections and of the vertical exchange velocity on the direction of the incident wind. This opens the way for further investigations to clarify the dependence of these parameters on wind direction and street aspect ratios.
The Channel Network model and field applications
International Nuclear Information System (INIS)
Khademi, B.; Moreno, L.; Neretnieks, I.
1999-01-01
The Channel Network model describes the fluid flow and solute transport in fractured media. The model is based on field observations, which indicate that flow and transport take place in a three-dimensional network of connected channels. The channels are generated in the model from observed stochastic distributions and solute transport is modeled taking into account advection and rock interactions, such as matrix diffusion and sorption within the rock. The most important site-specific data for the Channel Network model are the conductance distribution of the channels and the flow-wetted surface. The latter is the surface area of the rock in contact with the flowing water. These parameters may be estimated from hydraulic measurements. For the Aespoe site, several borehole data sets are available, where a packer distance of 3 meters was used. Numerical experiments were performed in order to study the uncertainties in the determination of the flow-wetted surface and conductance distribution. Synthetic data were generated along a borehole and hydraulic tests with different packer distances were simulated. The model has previously been used to study the Long-term Pumping and Tracer Test (LPT2) carried out in the Aespoe Hard Rock Laboratory (HRL) in Sweden, where the distance travelled by the tracers was of the order hundreds of meters. Recently, the model has been used to simulate the tracer tests performed in the TRUE experiment at HRL, with travel distance of the order of tens of meters. Several tracer tests with non-sorbing and sorbing species have been performed
Advances in dynamic network modeling in complex transportation systems
Ukkusuri, Satish V
2013-01-01
This book focuses on the latest in dynamic network modeling, including route guidance and traffic control in transportation systems and other complex infrastructure networks. Covers dynamic traffic assignment, flow modeling, mobile sensor deployment and more.
International Nuclear Information System (INIS)
Kusy, A.
1987-01-01
Two types of elementary resistances in thick resistive films have been considered: (i) constriction resistance R/sub C/ determined by the bulk properties of conducting material and by the geometry of constriction, and (ii) barrier resistance R/sub B/ determined by the parameters of a thermally activated type of tunneling process and by the geometry of the metal-insulator-metal unit. On this basis a resistance network composed of a large number of the two types of resistances has been defined. The network has been considered as being equivalent to thick resistive film (TRF) from the point of view of the resistance and temperature coefficient of resistance (TCR). The parameters of this network have been evaluated by the computer-aided approximation of the experimental data found for RuO 2 -based TRFs. On the basis of the equations derived for the network as well as the results of the approximation process, it can be concluded that the small values of the network TCR result from the superposition of the TCR of the conducting component β/sub C/ and of the temperature coefficient of barrier resistance α/sub B/. In this superposition β/sub C/ is attenuated (by 1--2 orders of magnitude), while α/sub B/ is attenuated by only few percentages. The network has been found to be strongly barrier dominated
Fast Rotational Diffusion of Water Molecules in a 2D Hydrogen Bond Network at Cryogenic Temperatures
Prisk, T. R.; Hoffmann, C.; Kolesnikov, A. I.; Mamontov, E.; Podlesnyak, A. A.; Wang, X.; Kent, P. R. C.; Anovitz, L. M.
2018-05-01
Individual water molecules or small clusters of water molecules contained within microporous minerals present an extreme case of confinement where the local structure of hydrogen bond networks are dramatically altered from bulk water. In the zinc silicate hemimorphite, the water molecules form a two-dimensional hydrogen bond network with hydroxyl groups in the crystal framework. Here, we present a combined experimental and theoretical study of the structure and dynamics of water molecules within this network. The water molecules undergo a continuous phase transition in their orientational configuration analogous to a two-dimensional Ising model. The incoherent dynamic structure factor reveals two thermally activated relaxation processes, one on a subpicosecond timescale and another on a 10-100 ps timescale, between 70 and 130 K. The slow process is an in-plane reorientation of the water molecule involving the breaking of hydrogen bonds with a framework that, despite the low temperatures involved, is analogous to rotational diffusion of water molecules in the bulk liquid. The fast process is a localized motion of the water molecule with no apparent analogs among known bulk or confined phases of water.
Temperature dependence of the partially localized state in a 2D molecular nanoporous network
Energy Technology Data Exchange (ETDEWEB)
Piquero-Zulaica, Ignacio, E-mail: ipiquerozulaica@gmail.com [Centro de Física de Materiales (CSIC/UPV-EHU)—Materials Physics Center, Manuel Lardizabal 5, 20018 San Sebastián (Spain); Nowakowska, Sylwia [Department of Physics, University of Basel, Klingelbergstrasse 82, 4056 Basel (Switzerland); Ortega, J. Enrique [Centro de Física de Materiales (CSIC/UPV-EHU)—Materials Physics Center, Manuel Lardizabal 5, 20018 San Sebastián (Spain); Donostia International Physics Center (DIPC), Manuel Lardizabal 4, 20018 San Sebastián (Spain); Departamento Física Aplicada I, Universidad del País Vasco, 20018 San Sebastián (Spain); Stöhr, Meike [Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 4, 9747 AG Groningen (Netherlands); Gade, Lutz H. [Anorganisch-Chemisches Institut, Universität Heidelberg, Im Neuenheimer Feld 270, 69120 Heidelberg (Germany); Jung, Thomas A. [Laboratory for Micro- and Nanotechnology, Paul Scherrer Institute, 5232 Villigen (Switzerland); Lobo-Checa, Jorge, E-mail: jorge.lobo@csic.es [Instituto de Ciencia de Materiales de Aragón (ICMA), CSIC-Universidad de Zaragoza, E-50009 Zaragoza (Spain); Departamento de Física de la Materia Condensada, Universidad de Zaragoza, E-50009 Zaragoza (Spain)
2017-01-01
Highlights: • A state of a 2D porous network is demonstrated to originate from the Shockley state. • The temperature evolution of both states is followed by means of ARPES. • Identical energy shifts are observed for both states, proving their common origin. - Abstract: Two-dimensional organic and metal-organic nanoporous networks can scatter surface electrons, leading to their partial localization. Such quantum states are related to intrinsic surface states of the substrate material. We further corroborate this relation by studying the thermally induced energy shifts of the electronic band stemming from coupled quantum states hosted in a metal-organic array formed by a perylene derivative on Cu(111). We observe by angle-resolved photoemission spectroscopy (ARPES), that both, the Shockley and the partially localized states, shift by the same amount to higher binding energies upon decreasing the sample temperature, providing evidence of their common origin. Our experimental approach and results further support the use of surface states for modelling these systems, which are expected to provide new insight into the physics concerning partially confined electronic states: scattering processes, potential barrier strengths, excited state lifetimes or the influence of guest molecules.
Ferreira, Pedro M.; Gomes, João M.; Martins, Igor A. C.; Ruano, António E.
2012-01-01
Accurate measurements of global solar radiation and atmospheric temperature, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature. PMID:23202230
Temperature Buffer Test. Final THM modelling
International Nuclear Information System (INIS)
Aakesson, Mattias; Malmberg, Daniel; Boergesson, Lennart; Hernelind, Jan; Ledesma, Alberto; Jacinto, Abel
2012-01-01
The Temperature Buffer Test (TBT) is a joint project between SKB/ANDRA and supported by ENRESA (modelling) and DBE (instrumentation), which aims at improving the understanding and to model the thermo-hydro-mechanical behavior of buffers made of swelling clay submitted to high temperatures (over 100 deg C) during the water saturation process. The test has been carried out in a KBS-3 deposition hole at Aespoe HRL. It was installed during the spring of 2003. Two heaters (3 m long, 0.6 m diameter) and two buffer arrangements have been investigated: the lower heater was surrounded by bentonite only, whereas the upper heater was surrounded by a composite barrier, with a sand shield between the heater and the bentonite. The test was dismantled and sampled during the winter of 2009/2010. This report presents the final THM modelling which was resumed subsequent to the dismantling operation. The main part of this work has been numerical modelling of the field test. Three different modelling teams have presented several model cases for different geometries and different degree of process complexity. Two different numerical codes, Code B right and Abaqus, have been used. The modelling performed by UPC-Cimne using Code B right, has been divided in three subtasks: i) analysis of the response observed in the lower part of the test, by inclusion of a number of considerations: (a) the use of the Barcelona Expansive Model for MX-80 bentonite; (b) updated parameters in the vapour diffusive flow term; (c) the use of a non-conventional water retention curve for MX-80 at high temperature; ii) assessment of a possible relation between the cracks observed in the bentonite blocks in the upper part of TBT, and the cycles of suction and stresses registered in that zone at the start of the experiment; and iii) analysis of the performance, observations and interpretation of the entire test. It was however not possible to carry out a full THM analysis until the end of the test due to
Temperature Buffer Test. Final THM modelling
Energy Technology Data Exchange (ETDEWEB)
Aakesson, Mattias; Malmberg, Daniel; Boergesson, Lennart; Hernelind, Jan [Clay Technology AB, Lund (Sweden); Ledesma, Alberto; Jacinto, Abel [UPC, Universitat Politecnica de Catalunya, Barcelona (Spain)
2012-01-15
The Temperature Buffer Test (TBT) is a joint project between SKB/ANDRA and supported by ENRESA (modelling) and DBE (instrumentation), which aims at improving the understanding and to model the thermo-hydro-mechanical behavior of buffers made of swelling clay submitted to high temperatures (over 100 deg C) during the water saturation process. The test has been carried out in a KBS-3 deposition hole at Aespoe HRL. It was installed during the spring of 2003. Two heaters (3 m long, 0.6 m diameter) and two buffer arrangements have been investigated: the lower heater was surrounded by bentonite only, whereas the upper heater was surrounded by a composite barrier, with a sand shield between the heater and the bentonite. The test was dismantled and sampled during the winter of 2009/2010. This report presents the final THM modelling which was resumed subsequent to the dismantling operation. The main part of this work has been numerical modelling of the field test. Three different modelling teams have presented several model cases for different geometries and different degree of process complexity. Two different numerical codes, Code{sub B}right and Abaqus, have been used. The modelling performed by UPC-Cimne using Code{sub B}right, has been divided in three subtasks: i) analysis of the response observed in the lower part of the test, by inclusion of a number of considerations: (a) the use of the Barcelona Expansive Model for MX-80 bentonite; (b) updated parameters in the vapour diffusive flow term; (c) the use of a non-conventional water retention curve for MX-80 at high temperature; ii) assessment of a possible relation between the cracks observed in the bentonite blocks in the upper part of TBT, and the cycles of suction and stresses registered in that zone at the start of the experiment; and iii) analysis of the performance, observations and interpretation of the entire test. It was however not possible to carry out a full THM analysis until the end of the test due to
Distributed Bayesian Networks for User Modeling
DEFF Research Database (Denmark)
Tedesco, Roberto; Dolog, Peter; Nejdl, Wolfgang
2006-01-01
The World Wide Web is a popular platform for providing eLearning applications to a wide spectrum of users. However – as users differ in their preferences, background, requirements, and goals – applications should provide personalization mechanisms. In the Web context, user models used by such ada......The World Wide Web is a popular platform for providing eLearning applications to a wide spectrum of users. However – as users differ in their preferences, background, requirements, and goals – applications should provide personalization mechanisms. In the Web context, user models used...... by such adaptive applications are often partial fragments of an overall user model. The fragments have then to be collected and merged into a global user profile. In this paper we investigate and present algorithms able to cope with distributed, fragmented user models – based on Bayesian Networks – in the context...... of Web-based eLearning platforms. The scenario we are tackling assumes learners who use several systems over time, which are able to create partial Bayesian Networks for user models based on the local system context. In particular, we focus on how to merge these partial user models. Our merge mechanism...
Prediction of fracture toughness temperature dependence applying neural network
Czech Academy of Sciences Publication Activity Database
Dlouhý, Ivo; Hadraba, Hynek; Chlup, Zdeněk; Šmída, T.
2011-01-01
Roč. 11, č. 1 (2011), s. 9-14 ISSN 1451-3749 R&D Projects: GA ČR(CZ) GAP108/10/0466 Institutional research plan: CEZ:AV0Z20410507 Keywords : brittle to ductile transition * fracture toughness * artificial neural network * steels Subject RIV: JL - Materials Fatigue, Friction Mechanics
Numerical Modeling of Conjugate Heat Transfer in Fluid Network
Majumdar, Alok
2004-01-01
Fluid network modeling with conjugate heat transfer has many applications in Aerospace engineering. In modeling unsteady flow with heat transfer, it is important to know the variation of wall temperature in time and space to calculate heat transfer between solid to fluid. Since wall temperature is a function of flow, a coupled analysis of temperature of solid and fluid is necessary. In cryogenic applications, modeling of conjugate heat transfer is of great importance to correctly predict boil-off rate in propellant tanks and chill down of transfer lines. In TFAWS 2003, the present author delivered a paper to describe a general-purpose computer program, GFSSP (Generalized Fluid System Simulation Program). GFSSP calculates flow distribution in complex flow circuit for compressible/incompressible, with or without heat transfer or phase change in all real fluids or mixtures. The flow circuit constitutes of fluid nodes and branches. The mass, energy and specie conservation equations are solved at the nodes where as momentum conservation equations are solved at the branches. The proposed paper describes the extension of GFSSP to model conjugate heat transfer. The network also includes solid nodes and conductors in addition to fluid nodes and branches. The energy conservation equations for solid nodes solves to determine the temperatures of the solid nodes simultaneously with all conservation equations governing fluid flow. The numerical scheme accounts for conduction, convection and radiation heat transfer. The paper will also describe the applications of the code to predict chill down of cryogenic transfer line and boil-off rate of cryogenic propellant storage tank.
Artificial neural network modeling of DDGS flowability with varying process and storage parameters
Neural Network (NN) modeling techniques were used to predict flowability behavior in distillers dried grains with solubles (DDGS) prepared with varying CDS (10, 15, and 20%, wb), drying temperature (100, 200, and 300°C), cooling temperature (-12, 0, and 35°C) and cooling time (0 and 1 month) levels....
Analytic Models of High-Temperature Hohlraums
International Nuclear Information System (INIS)
Stygar, W.A.; Olson, R.E.; Spielman, R.B.; Leeper, R.J.
2000-01-01
A unified set of high-temperature-hohlraum models has been developed. For a simple hohlraum, P s = (A s +(1minusα W )A W +A H )σT R 4 + (4Vσ/c)(dT R r /dt) where P S is the total power radiated by the source, A s is the source area, A W is the area of the cavity wall excluding the source and holes in the wall, A H is the area of the holes, σ is the Stefan-Boltzmann constant, T R is the radiation brightness temperature, V is the hohlraum volume, and c is the speed of light. The wall albedo α W triple b ond (T W /T R ) 4 where T W is the brightness temperature of area A W . The net power radiated by the source P N = P S -A S σT R 4 , which suggests that for laser-driven hohlraums the conversion efficiency η CE be defined as P N /P LASER . The characteristic time required to change T R 4 in response to a change in P N is 4V/C((lminusα W )A W +A H ). Using this model, T R , α W , and η CE can be expressed in terms of quantities directly measurable in a hohlraum experiment. For a steady-state hohlraum that encloses a convex capsule, P N = {(1minusα W )A W +A H +((1minusα C )(A S +A W α W )A C /A T = )}σT RC 4 where α C is the capsule albedo, A C is the capsule area, A T triple b ond (A S +A W +A H ), and T RC is the brightness temperature of the radiation that drives the capsule. According to this relation, the capsule-coupling efficiency of the baseline National-Ignition-Facility (NIF) hohlraum is 15% higher than predicted by previous analytic expressions. A model of a hohlraum that encloses a z pinch is also presented
Alexandrov, Natalia (Technical Monitor); Kuby, Michael; Tierney, Sean; Roberts, Tyler; Upchurch, Christopher
2005-01-01
This report reviews six classes of models that are used for studying transportation network topologies. The report is motivated by two main questions. First, what can the "new science" of complex networks (scale-free, small-world networks) contribute to our understanding of transport network structure, compared to more traditional methods? Second, how can geographic information systems (GIS) contribute to studying transport networks? The report defines terms that can be used to classify different kinds of models by their function, composition, mechanism, spatial and temporal dimensions, certainty, linearity, and resolution. Six broad classes of models for analyzing transport network topologies are then explored: GIS; static graph theory; complex networks; mathematical programming; simulation; and agent-based modeling. Each class of models is defined and classified according to the attributes introduced earlier. The paper identifies some typical types of research questions about network structure that have been addressed by each class of model in the literature.
DEFF Research Database (Denmark)
Tunzi, Michele; Boukhanouf, Rabah; Li, Hongwei
2018-01-01
This paper presents results of a research study into improving energy performance of small-scale district heat network through water supply and return temperature optimization technique. The case study involves establishing the baseline heat demand of the estate’s buildings, benchmarking...... the existing heat network operating parameters, and defining the optimum supply and return temperature. A stepwise temperature optimization technique of plate radiators heat emitters was applied to control the buildings indoor thermal comfort using night set back temperature strategy of 21/18 °C....... It was established that the heat network return temperature could be lowered from the current measured average of 55 °C to 35.6 °C, resulting in overall reduction of heat distribution losses and fuel consumption of 10% and 9% respectively. Hence, the study demonstrates the potential of operating existing heat...
International Nuclear Information System (INIS)
Boskovic, Bojan O.; Stolojan, Vlad; Zeze, Dagou A.; Forrest, Roy D.; Silva, S. Ravi P.; Haq, Sajad
2004-01-01
Carbon nanofibers have been grown at room temperature using a combination of radio frequency and microwave assisted plasma-enhanced chemical vapor deposition. The nanofibers were grown, using Ni powder catalyst, onto substrates kept at room temperature by using a purposely designed water-cooled sample holder. Branched carbon nanofiber growth was obtained without using a template resulting in interconnected carbon nanofiber network formation on substrates held at room temperature. This method would allow room-temperature direct synthesized nanofiber networks over relatively large areas, for a range of temperature sensitive substrates, such as organic materials, plastics, and other polymers of interest for nanoelectronic two-dimensional networks, nanoelectromechanical devices, nanoactuators, and composite materials
A network model for Ebola spreading.
Rizzo, Alessandro; Pedalino, Biagio; Porfiri, Maurizio
2016-04-07
The availability of accurate models for the spreading of infectious diseases has opened a new era in management and containment of epidemics. Models are extensively used to plan for and execute vaccination campaigns, to evaluate the risk of international spreadings and the feasibility of travel bans, and to inform prophylaxis campaigns. Even when no specific therapeutical protocol is available, as for the Ebola Virus Disease (EVD), models of epidemic spreading can provide useful insight to steer interventions in the field and to forecast the trend of the epidemic. Here, we propose a novel mathematical model to describe EVD spreading based on activity driven networks (ADNs). Our approach overcomes the simplifying assumption of homogeneous mixing, which is central to most of the mathematically tractable models of EVD spreading. In our ADN-based model, each individual is not bound to contact every other, and its network of contacts varies in time as a function of an activity potential. Our model contemplates the possibility of non-ideal and time-varying intervention policies, which are critical to accurately describe EVD spreading in afflicted countries. The model is calibrated from field data of the 2014 April-to-December spreading in Liberia. We use the model as a predictive tool, to emulate the dynamics of EVD in Liberia and offer a one-year projection, until December 2015. Our predictions agree with the current vision expressed by professionals in the field, who consider EVD in Liberia at its final stage. The model is also used to perform a what-if analysis to assess the efficacy of timely intervention policies. In particular, we show that an earlier application of the same intervention policy would have greatly reduced the number of EVD cases, the duration of the outbreak, and the infrastructures needed for the implementation of the intervention. Copyright © 2016 Elsevier Ltd. All rights reserved.
Modeling Network Transition Constraints with Hypergraphs
DEFF Research Database (Denmark)
Harrod, Steven
2011-01-01
Discrete time dynamic graphs are frequently used to model multicommodity flows or activity paths through constrained resources, but simple graphs fail to capture the interaction effects of resource transitions. The resulting schedules are not operationally feasible, and return inflated objective...... values. A directed hypergraph formulation is derived to address railway network sequencing constraints, and an experimental problem sample solved to estimate the magnitude of objective inflation when interaction effects are ignored. The model is used to demonstrate the value of advance scheduling...... of train paths on a busy North American railway....
Mathematical model for spreading dynamics of social network worms
International Nuclear Information System (INIS)
Sun, Xin; Liu, Yan-Heng; Han, Jia-Wei; Liu, Xue-Jie; Li, Bin; Li, Jin
2012-01-01
In this paper, a mathematical model for social network worm spreading is presented from the viewpoint of social engineering. This model consists of two submodels. Firstly, a human behavior model based on game theory is suggested for modeling and predicting the expected behaviors of a network user encountering malicious messages. The game situation models the actions of a user under the condition that the system may be infected at the time of opening a malicious message. Secondly, a social network accessing model is proposed to characterize the dynamics of network users, by which the number of online susceptible users can be determined at each time step. Several simulation experiments are carried out on artificial social networks. The results show that (1) the proposed mathematical model can well describe the spreading dynamics of social network worms; (2) weighted network topology greatly affects the spread of worms; (3) worms spread even faster on hybrid social networks
Model parameter updating using Bayesian networks
International Nuclear Information System (INIS)
Treml, C.A.; Ross, Timothy J.
2004-01-01
This paper outlines a model parameter updating technique for a new method of model validation using a modified model reference adaptive control (MRAC) framework with Bayesian Networks (BNs). The model parameter updating within this method is generic in the sense that the model/simulation to be validated is treated as a black box. It must have updateable parameters to which its outputs are sensitive, and those outputs must have metrics that can be compared to that of the model reference, i.e., experimental data. Furthermore, no assumptions are made about the statistics of the model parameter uncertainty, only upper and lower bounds need to be specified. This method is designed for situations where a model is not intended to predict a complete point-by-point time domain description of the item/system behavior; rather, there are specific points, features, or events of interest that need to be predicted. These specific points are compared to the model reference derived from actual experimental data. The logic for updating the model parameters to match the model reference is formed via a BN. The nodes of this BN consist of updateable model input parameters and the specific output values or features of interest. Each time the model is executed, the input/output pairs are used to adapt the conditional probabilities of the BN. Each iteration further refines the inferred model parameters to produce the desired model output. After parameter updating is complete and model inputs are inferred, reliabilities for the model output are supplied. Finally, this method is applied to a simulation of a resonance control cooling system for a prototype coupled cavity linac. The results are compared to experimental data.
Modelling of solar energy potential in Nigeria using an artificial neural network model
International Nuclear Information System (INIS)
Fadare, D.A.
2009-01-01
In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Nigeria (lat. 4-14 o N, log. 2-15 o E) was developed. Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using neural toolbox for MATLAB. Geographical and meteorological data of 195 cities in Nigeria for period of 10 years (1983-1993) from the NASA geo-satellite database were used for the training and testing the network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, mean temperature, and relative humidity) were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation intensities for training and testing datasets were higher than 90%, thus suggesting a high reliability of the model for evaluation of solar radiation in locations where solar radiation data are not available. The predicted solar radiation values from the model were given in form of monthly maps. The monthly mean solar radiation potential in northern and southern regions ranged from 7.01-5.62 to 5.43-3.54 kW h/m 2 day, respectively. A graphical user interface (GUI) was developed for the application of the model. The model can be used easily for estimation of solar radiation for preliminary design of solar applications.
Complex networks-based energy-efficient evolution model for wireless sensor networks
Energy Technology Data Exchange (ETDEWEB)
Zhu Hailin [Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, P.O. Box 106, Beijing 100876 (China)], E-mail: zhuhailin19@gmail.com; Luo Hong [Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, P.O. Box 106, Beijing 100876 (China); Peng Haipeng; Li Lixiang; Luo Qun [Information Secure Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, P.O. Box 145, Beijing 100876 (China)
2009-08-30
Based on complex networks theory, we present two self-organized energy-efficient models for wireless sensor networks in this paper. The first model constructs the wireless sensor networks according to the connectivity and remaining energy of each sensor node, thus it can produce scale-free networks which have a performance of random error tolerance. In the second model, we not only consider the remaining energy, but also introduce the constraint of links to each node. This model can make the energy consumption of the whole network more balanced. Finally, we present the numerical experiments of the two models.
Complex networks-based energy-efficient evolution model for wireless sensor networks
International Nuclear Information System (INIS)
Zhu Hailin; Luo Hong; Peng Haipeng; Li Lixiang; Luo Qun
2009-01-01
Based on complex networks theory, we present two self-organized energy-efficient models for wireless sensor networks in this paper. The first model constructs the wireless sensor networks according to the connectivity and remaining energy of each sensor node, thus it can produce scale-free networks which have a performance of random error tolerance. In the second model, we not only consider the remaining energy, but also introduce the constraint of links to each node. This model can make the energy consumption of the whole network more balanced. Finally, we present the numerical experiments of the two models.
Modeling online social networks based on preferential linking
International Nuclear Information System (INIS)
Hu Hai-Bo; Chen Jun; Guo Jin-Li
2012-01-01
We study the phenomena of preferential linking in a large-scale evolving online social network and find that the linear preference holds for preferential creation, preferential acceptance, and preferential attachment. Based on the linear preference, we propose an analyzable model, which illustrates the mechanism of network growth and reproduces the process of network evolution. Our simulations demonstrate that the degree distribution of the network produced by the model is in good agreement with that of the real network. This work provides a possible bridge between the micro-mechanisms of network growth and the macrostructures of online social networks
Modeling the Effect of Bandwidth Allocation on Network Performance
African Journals Online (AJOL)
... The proposed model showed improved performance for CDMA networks, but further increase in the bandwidth did not benefit the network; (iii) A reliability measure such as the spectral efficiency is therefore useful to redeem the limitation in (ii). Keywords: Coverage Capacity, CDMA, Mobile Network, Network Throughput ...
Aeronautical telecommunications network advances, challenges, and modeling
Musa, Sarhan M
2015-01-01
Addresses the Challenges of Modern-Day Air Traffic Air traffic control (ATC) directs aircraft in the sky and on the ground to safety, while the Aeronautical Telecommunications Network (ATN) comprises all systems and phases that assist in aircraft departure and landing. The Aeronautical Telecommunications Network: Advances, Challenges, and Modeling focuses on the development of ATN and examines the role of the various systems that link aircraft with the ground. The book places special emphasis on ATC-introducing the modern ATC system from the perspective of the user and the developer-and provides a thorough understanding of the operating mechanism of the ATC system. It discusses the evolution of ATC, explaining its structure and how it works; includes design examples; and describes all subsystems of the ATC system. In addition, the book covers relevant tools, techniques, protocols, and architectures in ATN, including MIPv6, air traffic control (ATC), security of air traffic management (ATM), very-high-frequenc...
Modelling dependable systems using hybrid Bayesian networks
International Nuclear Information System (INIS)
Neil, Martin; Tailor, Manesh; Marquez, David; Fenton, Norman; Hearty, Peter
2008-01-01
A hybrid Bayesian network (BN) is one that incorporates both discrete and continuous nodes. In our extensive applications of BNs for system dependability assessment, the models are invariably hybrid and the need for efficient and accurate computation is paramount. We apply a new iterative algorithm that efficiently combines dynamic discretisation with robust propagation algorithms on junction tree structures to perform inference in hybrid BNs. We illustrate its use in the field of dependability with two example of reliability estimation. Firstly we estimate the reliability of a simple single system and next we implement a hierarchical Bayesian model. In the hierarchical model we compute the reliability of two unknown subsystems from data collected on historically similar subsystems and then input the result into a reliability block model to compute system level reliability. We conclude that dynamic discretisation can be used as an alternative to analytical or Monte Carlo methods with high precision and can be applied to a wide range of dependability problems
Logic integer programming models for signaling networks.
Haus, Utz-Uwe; Niermann, Kathrin; Truemper, Klaus; Weismantel, Robert
2009-05-01
We propose a static and a dynamic approach to model biological signaling networks, and show how each can be used to answer relevant biological questions. For this, we use the two different mathematical tools of Propositional Logic and Integer Programming. The power of discrete mathematics for handling qualitative as well as quantitative data has so far not been exploited in molecular biology, which is mostly driven by experimental research, relying on first-order or statistical models. The arising logic statements and integer programs are analyzed and can be solved with standard software. For a restricted class of problems the logic models reduce to a polynomial-time solvable satisfiability algorithm. Additionally, a more dynamic model enables enumeration of possible time resolutions in poly-logarithmic time. Computational experiments are included.
Integrated flow and temperature modeling at the catchment scale
DEFF Research Database (Denmark)
Loinaz, Maria Christina; Davidsen, Hasse Kampp; Butts, Michael
2013-01-01
–groundwater dynamics affect stream temperature. A coupled surface water–groundwater and temperature model has therefore been developed to quantify the impacts of land management and water use on stream flow and temperatures. The model is applied to the simulation of stream temperature levels in a spring-fed stream...
DEFF Research Database (Denmark)
Ommen, Torben Schmidt; Elmegaard, Brian
2012-01-01
In order to minimise losses in a district heating network, one approach is to lower the temperature difference between working media and soil. Considering only direct heat exchange, the minimum forward temperature level is determined by the demand side, as energy services are required at a certai...
Bayesian Recurrent Neural Network for Language Modeling.
Chien, Jen-Tzung; Ku, Yuan-Chu
2016-02-01
A language model (LM) is calculated as the probability of a word sequence that provides the solution to word prediction for a variety of information systems. A recurrent neural network (RNN) is powerful to learn the large-span dynamics of a word sequence in the continuous space. However, the training of the RNN-LM is an ill-posed problem because of too many parameters from a large dictionary size and a high-dimensional hidden layer. This paper presents a Bayesian approach to regularize the RNN-LM and apply it for continuous speech recognition. We aim to penalize the too complicated RNN-LM by compensating for the uncertainty of the estimated model parameters, which is represented by a Gaussian prior. The objective function in a Bayesian classification network is formed as the regularized cross-entropy error function. The regularized model is constructed not only by calculating the regularized parameters according to the maximum a posteriori criterion but also by estimating the Gaussian hyperparameter by maximizing the marginal likelihood. A rapid approximation to a Hessian matrix is developed to implement the Bayesian RNN-LM (BRNN-LM) by selecting a small set of salient outer-products. The proposed BRNN-LM achieves a sparser model than the RNN-LM. Experiments on different corpora show the robustness of system performance by applying the rapid BRNN-LM under different conditions.
Foreground removal from WMAP 5 yr temperature maps using an MLP neural network
DEFF Research Database (Denmark)
Nørgaard-Nielsen, Hans Ulrik
2010-01-01
CMB signal makes it essential to minimize the systematic errors in the CMB temperature determinations. Methods. The feasibility of using simple neural networks to extract the CMB signal from detailed simulated data has already been demonstrated. Here, simple neural networks are applied to the WMAP 5...... yr temperature data without using any auxiliary data. Results. A simple multilayer perceptron neural network with two hidden layers provides temperature estimates over more than 75 per cent of the sky with random errors significantly below those previously extracted from these data. Also......, the systematic errors, i.e. errors correlated with the Galactic foregrounds, are very small. Conclusions. With these results the neural network method is well prepared for dealing with the high-quality CMB data from the ESA Planck Surveyor satellite. © ESO, 2010....
Research on network information security model and system construction
Wang Haijun
2016-01-01
It briefly describes the impact of large data era on China’s network policy, but also brings more opportunities and challenges to the network information security. This paper reviews for the internationally accepted basic model and characteristics of network information security, and analyses the characteristics of network information security and their relationship. On the basis of the NIST security model, this paper describes three security control schemes in safety management model and the...
A Complex Network Approach to Distributional Semantic Models.
Directory of Open Access Journals (Sweden)
Akira Utsumi
Full Text Available A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models.
Two stage neural network modelling for robust model predictive control.
Patan, Krzysztof
2018-01-01
The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Foreground removal from CMB temperature maps using an MLP neural network
DEFF Research Database (Denmark)
Nørgaard-Nielsen, Hans Ulrik; Jørgensen, H.E.
2008-01-01
the CMB temperature signal from the combined signal CMB and the foregrounds has been investigated. As a specific example, we have analysed simulated data, as expected from the ESA Planck CMB mission. A simple multilayer perceptron neural network with 2 hidden layers can provide temperature estimates over...... CMB signal it is essential to minimize the systematic errors in the CMB temperature determinations. Following the available knowledge of the spectral behavior of the Galactic foregrounds simple power law-like spectra have been assumed. The feasibility of using a simple neural network for extracting...
A highly crystalline single Au wire network as a high temperature transparent heater
Rao, K. D. M.; Kulkarni, Giridhar U.
2014-05-01
A transparent conductor which can generate high temperatures finds important applications in optoelectronics. In this article, a wire network made of Au on quartz is shown to serve as an effective high temperature transparent heater. The heater has been fabricated by depositing Au onto a cracked sacrificial template. The highly interconnected Au wire network thus formed exhibited a transmittance of ~87% in a wide spectral range with a sheet resistance of 5.4 Ω □-1. By passing current through the network, it could be joule heated to ~600 °C within a few seconds. The extraordinary thermal performance and stability owe much to the seamless junctions present in the wire network. Furthermore, the wire network gets self-annealed through joule heating as seen from its increased crystallinity. Interestingly, both transmittance and sheet resistance improved following annealing to 92% and 3.2 Ω □-1, respectively. A transparent conductor which can generate high temperatures finds important applications in optoelectronics. In this article, a wire network made of Au on quartz is shown to serve as an effective high temperature transparent heater. The heater has been fabricated by depositing Au onto a cracked sacrificial template. The highly interconnected Au wire network thus formed exhibited a transmittance of ~87% in a wide spectral range with a sheet resistance of 5.4 Ω □-1. By passing current through the network, it could be joule heated to ~600 °C within a few seconds. The extraordinary thermal performance and stability owe much to the seamless junctions present in the wire network. Furthermore, the wire network gets self-annealed through joule heating as seen from its increased crystallinity. Interestingly, both transmittance and sheet resistance improved following annealing to 92% and 3.2 Ω □-1, respectively. Electronic supplementary information (ESI) available: Optical micrographs, EDAX, XRD, SEM and TEM images of Au metal wires. See DOI: 10.1039/c4nr00869c
United States Historical Climatology Network Daily Temperature and Precipitation Data (1871-1997)
Energy Technology Data Exchange (ETDEWEB)
Easterling, D.R.
2002-10-28
This document describes a database containing daily observations of maximum and minimum temperature, precipitation amount, snowfall amount, and snow depth from 1062 observing stations across the contiguous US. This database is an expansion and update of the original 138-station database previously released by the Carbon Dioxide Information Analysis Center (CDIAC) as CDIAC numeric data package NDP-042. These 1062 stations are a subset of the 1221-station US Historical Climatology Network (HCN), a monthly database compiled by the National Climatic Data Center (Asheville, North Carolina) that has been widely used in analyzing US climate. Data from 1050 of these daily records extend into the 1990s, while 990 of these extend through 1997. Most station records are essentially complete for at least 40 years; the latest beginning year of record is 1948. Records from 158 stations begin prior to 1900, with that of Charleston, South Carolina beginning the earliest (1871). The daily resolution of these data makes them extremely valuable for studies attempting to detect and monitor long-term climatic changes on a regional scale. Studies using daily data may be able to detect changes in regional climate that would not be apparent from analysis of monthly temperature and precipitation data. Such studies may include analyses of trends in maximum and minimum temperatures, temperature extremes, daily temperature range, precipitation ''event size'' frequency, and the magnitude and duration of wet and dry periods. The data are also valuable in areas such as regional climate model validation and climate change impact assessment. This database is available free of charge from CDIAC as a numeric data package (NDP).
Exact model reduction of combinatorial reaction networks
Directory of Open Access Journals (Sweden)
Fey Dirk
2008-08-01
Full Text Available Abstract Background Receptors and scaffold proteins usually possess a high number of distinct binding domains inducing the formation of large multiprotein signaling complexes. Due to combinatorial reasons the number of distinguishable species grows exponentially with the number of binding domains and can easily reach several millions. Even by including only a limited number of components and binding domains the resulting models are very large and hardly manageable. A novel model reduction technique allows the significant reduction and modularization of these models. Results We introduce methods that extend and complete the already introduced approach. For instance, we provide techniques to handle the formation of multi-scaffold complexes as well as receptor dimerization. Furthermore, we discuss a new modeling approach that allows the direct generation of exactly reduced model structures. The developed methods are used to reduce a model of EGF and insulin receptor crosstalk comprising 5,182 ordinary differential equations (ODEs to a model with 87 ODEs. Conclusion The methods, presented in this contribution, significantly enhance the available methods to exactly reduce models of combinatorial reaction networks.
Neural Networks For Electrohydrodynamic Effect Modelling
Directory of Open Access Journals (Sweden)
Wiesław Wajs
2004-01-01
Full Text Available This paper presents currently achieved results concerning methods of electrohydrodynamiceffect used in geophysics simulated with feedforward networks trained with backpropagation algorithm, radial basis function networks and generalized regression networks.
Directory of Open Access Journals (Sweden)
E. Pisoni
2008-02-01
Full Text Available Satellite data can be very useful in applications where extensive spatial information is needed, but sometimes missing data due to presence of clouds can affect data quality. In this study a methodology for pre-processing sea surface temperature (SST data is proposed. The methodology, that processes measures in the visible wavelength, is based on an Artificial Neural Network (ANN system. The effectiveness of the procedure has been also evaluated comparing results obtained using an interpolation method. After the methodology has been identified, a validation is performed on 3 different episodes representative of SST variability in the Mediterranean sea. The proposed technique can process SST NOAA/AVHRR data to simulate severe storm episodes by means of prognostic meteorological models.
Social network models predict movement and connectivity in ecological landscapes
Fletcher, Robert J.; Acevedo, M.A.; Reichert, Brian E.; Pias, Kyle E.; Kitchens, Wiley M.
2011-01-01
Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.
Social network models predict movement and connectivity in ecological landscapes.
Fletcher, Robert J; Acevedo, Miguel A; Reichert, Brian E; Pias, Kyle E; Kitchens, Wiley M
2011-11-29
Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.
Modeling the wafer temperature profile in a multiwafer LPCVD furnace
Energy Technology Data Exchange (ETDEWEB)
Badgwell, T.A. [Rice Univ., Houston, TX (United States). Dept. of Chemical Engineering; Trachtenberg, I.; Edgar, T.F. [Univ. of Texas, Austin, TX (United States). Dept. of Chemical Engineering
1994-01-01
A mathematical model has been developed to predict wafer temperatures within a hot-wall multiwafer low pressure chemical vapor deposition (LPCVD) reactor. The model predicts both axial (wafer-to-wafer) and radial (across-wafer) temperature profiles. Model predictions compare favorably with in situ wafer temperature measurements described in an earlier paper. Measured axial and radial temperature nonuniformities are explained in terms of radiative heat-transfer effects. A simulation study demonstrates how changes in the outer tube temperature profile and reactor geometry affect wafer temperatures. Reactor design changes which could improve the wafer temperature profile are discussed.
Boskovic, BO; Stolojan, V; Zeze, DA; Forrest, RD; Silva, SRP; Haq, S
2004-01-01
Carbon nanofibers have been grown at room temperature using a combination of radio frequency and microwave assisted plasma-enhanced chemical vapor deposition. The nanofibers were grown, using Ni powder catalyst, onto substrates kept at room temperature by using a purposely designed water-cooled sample holder. Branched carbon nanofiber growth was obtained without using a template resulting in interconnected carbon nanofiber network formation on substrates held at room temperatur...
A model for evaluating stream temperature response to climate change in Wisconsin
Stewart, Jana S.; Westenbroek, Stephen M.; Mitro, Matthew G.; Lyons, John D.; Kammel, Leah E.; Buchwald, Cheryl A.
2015-01-01
Expected climatic changes in air temperature and precipitation patterns across the State of Wisconsin may alter future stream temperature and flow regimes. As a consequence of flow and temperature changes, the composition and distribution of fish species assemblages are expected to change. In an effort to gain a better understanding of how climatic changes may affect stream temperature, an approach was developed to predict and project daily summertime stream temperature under current and future climate conditions for 94,341 stream kilometers across Wisconsin. The approach uses a combination of static landscape characteristics and dynamic time-series climatic variables as input for an Artificial Neural Network (ANN) Model integrated with a Soil-Water-Balance (SWB) Model. Future climate scenarios are based on output from downscaled General Circulation Models (GCMs). The SWB model provided a means to estimate the temporal variability in groundwater recharge and provided a mechanism to evaluate the effect of changing air temperature and precipitation on groundwater recharge and soil moisture. The Integrated Soil-Water-Balance and Artificial Neural Network version 1 (SWB-ANNv1) Model was used to simulate daily summertime stream temperature under current (1990–2008) climate and explained 76 percent of the variation in the daily mean based on validation at 67 independent sites. Results were summarized as July mean water temperature, and individual stream segments were classified by thermal class (cold, cold transition, warm transition, and warm) for comparison of current (1990–2008) with future climate conditions.
Neural network models of categorical perception.
Damper, R I; Harnad, S R
2000-05-01
Studies of the categorical perception (CP) of sensory continua have a long and rich history in psychophysics. In 1977, Macmillan, Kaplan, and Creelman introduced the use of signal detection theory to CP studies. Anderson and colleagues simultaneously proposed the first neural model for CP, yet this line of research has been less well explored. In this paper, we assess the ability of neural-network models of CP to predict the psychophysical performance of real observers with speech sounds and artificial/novel stimuli. We show that a variety of neural mechanisms are capable of generating the characteristics of CP. Hence, CP may not be a special model of perception but an emergent property of any sufficiently powerful general learning system.
Combination of Bayesian Network and Overlay Model in User Modeling
Directory of Open Access Journals (Sweden)
Loc Nguyen
2009-12-01
Full Text Available The core of adaptive system is user model containing personal information such as knowledge, learning styles, goals… which is requisite for learning personalized process. There are many modeling approaches, for example: stereotype, overlay, plan recognition… but they don’t bring out the solid method for reasoning from user model. This paper introduces the statistical method that combines Bayesian network and overlay modeling so that it is able to infer user’s knowledge from evidences collected during user’s learning process.
Networks model of the East Turkistan terrorism
Li, Ben-xian; Zhu, Jun-fang; Wang, Shun-guo
2015-02-01
The presence of the East Turkistan terrorist network in China can be traced back to the rebellions on the BAREN region in Xinjiang in April 1990. This article intends to research the East Turkistan networks in China and offer a panoramic view. The events, terrorists and their relationship are described using matrices. Then social network analysis is adopted to reveal the network type and the network structure characteristics. We also find the crucial terrorist leader. Ultimately, some results show that the East Turkistan network has big hub nodes and small shortest path, and that the network follows a pattern of small world network with hierarchical structure.
Pruning Boltzmann networks and hidden Markov models
DEFF Research Database (Denmark)
Pedersen, Morten With; Stork, D.
1996-01-01
We present sensitivity-based pruning algorithms for general Boltzmann networks. Central to our methods is the efficient calculation of a second-order approximation to the true weight saliencies in a cross-entropy error. Building upon previous work which shows a formal correspondence between linear...... Boltzmann chains and hidden Markov models (HMMs), we argue that our method can be applied to HMMs as well. We illustrate pruning on Boltzmann zippers, which are equivalent to two HMMs with cross-connection links. We verify that our second-order approximation preserves the rank ordering of weight saliencies...
Compartmentalization analysis using discrete fracture network models
Energy Technology Data Exchange (ETDEWEB)
La Pointe, P.R.; Eiben, T.; Dershowitz, W. [Golder Associates, Redmond, VA (United States); Wadleigh, E. [Marathon Oil Co., Midland, TX (United States)
1997-08-01
This paper illustrates how Discrete Fracture Network (DFN) technology can serve as a basis for the calculation of reservoir engineering parameters for the development of fractured reservoirs. It describes the development of quantitative techniques for defining the geometry and volume of structurally controlled compartments. These techniques are based on a combination of stochastic geometry, computational geometry, and graph the theory. The parameters addressed are compartment size, matrix block size and tributary drainage volume. The concept of DFN models is explained and methodologies to compute these parameters are demonstrated.
Analysis and Comparison of Typical Models within Distribution Network Design
DEFF Research Database (Denmark)
Jørgensen, Hans Jacob; Larsen, Allan; Madsen, Oli B.G.
This paper investigates the characteristics of typical optimisation models within Distribution Network Design. During the paper fourteen models known from the literature will be thoroughly analysed. Through this analysis a schematic approach to categorisation of distribution network design models...... for educational purposes. Furthermore, the paper can be seen as a practical introduction to network design modelling as well as a being an art manual or recipe when constructing such a model....
Fundamentals of complex networks models, structures and dynamics
Chen, Guanrong; Li, Xiang
2014-01-01
Complex networks such as the Internet, WWW, transportationnetworks, power grids, biological neural networks, and scientificcooperation networks of all kinds provide challenges for futuretechnological development. In particular, advanced societies havebecome dependent on large infrastructural networks to an extentbeyond our capability to plan (modeling) and to operate (control).The recent spate of collapses in power grids and ongoing virusattacks on the Internet illustrate the need for knowledge aboutmodeling, analysis of behaviors, optimized planning and performancecontrol in such networks. F
A Search Model with a Quasi-Network
DEFF Research Database (Denmark)
Ejarque, Joao Miguel
This paper adds a quasi-network to a search model of the labor market. Fitting the model to an average unemployment rate and to other moments in the data implies the presence of the network is not noticeable in the basic properties of the unemployment and job finding rates. However, the network...
Joint Modelling of Structural and Functional Brain Networks
DEFF Research Database (Denmark)
Andersen, Kasper Winther; Herlau, Tue; Mørup, Morten
-parametric Bayesian network model which allows for joint modelling and integration of multiple networks. We demonstrate the model’s ability to detect vertices that share structure across networks jointly in functional MRI (fMRI) and diffusion MRI (dMRI) data. Using two fMRI and dMRI scans per subject, we establish...
Artificial Neural Network Modeling of an Inverse Fluidized Bed ...
African Journals Online (AJOL)
A Radial Basis Function neural network has been successfully employed for the modeling of the inverse fluidized bed reactor. In the proposed model, the trained neural network represents the kinetics of biological decomposition of pollutants in the reactor. The neural network has been trained with experimental data ...
Degree distribution of a new model for evolving networks
Indian Academy of Sciences (India)
on intuitive but realistic consideration that nodes are added to the network with both preferential and random attachments. The degree distribution of the model is between a power-law and an exponential decay. Motivated by the features of network evolution, we introduce a new model of evolving networks, incorporating the ...
Neural Network Based Models for Fusion Applications
Meneghini, Orso; Tema Biwole, Arsene; Luda, Teobaldo; Zywicki, Bailey; Rea, Cristina; Smith, Sterling; Snyder, Phil; Belli, Emily; Staebler, Gary; Canty, Jeff
2017-10-01
Whole device modeling, engineering design, experimental planning and control applications demand models that are simultaneously physically accurate and fast. This poster reports on the ongoing effort towards the development and validation of a series of models that leverage neural-Ânetwork (NN) multidimensional regression techniques to accelerate some of the most mission critical first principle models for the fusion community, such as: the EPED workflow for prediction of the H-Mode and Super H-Mode pedestal structure the TGLF and NEO models for the prediction of the turbulent and neoclassical particle, energy and momentum fluxes; and the NEO model for the drift-kinetic solution of the bootstrap current. We also applied NNs on DIII-D experimental data for disruption prediction and quantifying the effect of RMPs on the pedestal and ELMs. All of these projects were supported by the infrastructure provided by the OMFIT integrated modeling framework. Work supported by US DOE under DE-SC0012656, DE-FG02-95ER54309, DE-FC02-04ER54698.
Performance of a Predictive Model for Calculating Ascent Time to a Target Temperature
Directory of Open Access Journals (Sweden)
Jin Woo Moon
2016-12-01
Full Text Available The aim of this study was to develop an artificial neural network (ANN prediction model for controlling building heating systems. This model was used to calculate the ascent time of indoor temperature from the setback period (when a building was not occupied to a target setpoint temperature (when a building was occupied. The calculated ascent time was applied to determine the proper moment to start increasing the temperature from the setback temperature to reach the target temperature at an appropriate time. Three major steps were conducted: (1 model development; (2 model optimization; and (3 performance evaluation. Two software programs—Matrix Laboratory (MATLAB and Transient Systems Simulation (TRNSYS—were used for model development, performance tests, and numerical simulation methods. Correlation analysis between input variables and the output variable of the ANN model revealed that two input variables (current indoor air temperature and temperature difference from the target setpoint temperature, presented relatively strong relationships with the ascent time to the target setpoint temperature. These two variables were used as input neurons. Analyzing the difference between the simulated and predicted values from the ANN model provided the optimal number of hidden neurons (9, hidden layers (3, moment (0.9, and learning rate (0.9. At the study’s conclusion, the optimized model proved its prediction accuracy with acceptable errors.
Temperature estimation of induction machines based on wireless sensor networks
Directory of Open Access Journals (Sweden)
Y. Huang
2018-04-01
Full Text Available In this paper, a fourth-order Kalman filter (KF algorithm is implemented in the wireless sensor node to estimate the temperatures of the stator winding, the rotor cage and the stator core in the induction machine. Three separate wireless sensor nodes are used as the data acquisition systems for different input signals. Six Hall sensors are used to acquire the three-phase stator currents and voltages of the induction machine. All of them are processed to root mean square (rms in ampere and volt. A rotary encoder is mounted for the rotor speed and Pt-1000 is used for the temperature of the coolant air. The processed signals in the physical unit are transmitted wirelessly to the host wireless sensor node, where the KF is implemented with fixed-point arithmetic in Contiki OS. Time-division multiple access (TDMA is used to make the wireless transmission more stable. Compared to the floating-point implementation, the fixed-point implementation has the same estimation accuracy at only about one-fifth of the computation time. The temperature estimation system can work under any work condition as long as there are currents through the machine. It can also be rebooted for estimation even when wireless transmission has collapsed or packages are missing.
Subsurface temperature of the onshore Netherlands: new temperature dataset and modelling
Bonté, D.; Wees, J.-D. van; Verweij, J.M.
2012-01-01
Subsurface temperature is a key parameter for geothermal energy prospection in sedimentary basins. Here, we present the results of a 3D temperature modelling using a thermal-tectonic forward modelling method, calibrated with subsurface temperature measurements in the Netherlands. The first step
Ferrari, F. A. S.; Viana, R. L.; Reis, A. S.; Iarosz, K. C.; Caldas, I. L.; Batista, A. M.
2018-04-01
The cerebral cortex plays a key role in complex cortical functions. It can be divided into areas according to their function (motor, sensory and association areas). In this paper, the cerebral cortex is described as a network of networks (cortex network), we consider that each cortical area is composed of a network with small-world property (cortical network). The neurons are assumed to have bursting properties with the dynamics described by the Rulkov model. We study the phase synchronization of the cortex network and the cortical networks. In our simulations, we verify that synchronization in cortex network is not homogeneous. Besides, we focus on the suppression of neural phase synchronization. Synchronization can be related to undesired and pathological abnormal rhythms in the brain. For this reason, we consider the delayed feedback control to suppress the synchronization. We show that delayed feedback control is efficient to suppress synchronous behavior in our network model when an appropriate signal intensity and time delay are defined.
Modelling global fresh surface water temperature
Beek, L.P.H. van; Eikelboom, T.; Vliet, M.T.H. van; Bierkens, M.F.P.
2011-01-01
Temperature directly determines a range of water physical properties including vapour pressure, surface tension, density and viscosity, and the solubility of oxygen and other gases. Indirectly water temperature acts as a strong control on fresh water biogeochemistry, influencing sediment
QSAR modelling using combined simple competitive learning networks and RBF neural networks.
Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E
2018-04-01
The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.
Linear approximation model network and its formation via ...
Indian Academy of Sciences (India)
niques, an alternative `linear approximation model' (LAM) network approach is .... network is LPV, existing LTI theory is difficult to apply (Kailath 1980). ..... Beck J V, Arnold K J 1977 Parameter estimation in engineering and science (New York: ...
Equity venture capital platform model based on complex network
Guo, Dongwei; Zhang, Lanshu; Liu, Miao
2018-05-01
This paper uses the small-world network and the random-network to simulate the relationship among the investors, construct the network model of the equity venture capital platform to explore the impact of the fraud rate and the bankruptcy rate on the robustness of the network model while observing the impact of the average path length and the average agglomeration coefficient of the investor relationship network on the income of the network model. The study found that the fraud rate and bankruptcy rate exceeded a certain threshold will lead to network collapse; The bankruptcy rate has a great influence on the income of the platform; The risk premium exists, and the average return is better under a certain range of bankruptcy risk; The structure of the investor relationship network has no effect on the income of the investment model.
Directory of Open Access Journals (Sweden)
Jianli Li
2014-01-01
Full Text Available The position and orientation system (POS is a key equipment for airborne remote sensing systems, which provides high-precision position, velocity, and attitude information for various imaging payloads. Temperature error is the main source that affects the precision of POS. Traditional temperature error model is single temperature parameter linear function, which is not sufficient for the higher accuracy requirement of POS. The traditional compensation method based on neural network faces great problem in the repeatability error under different temperature conditions. In order to improve the precision and generalization ability of the temperature error compensation for POS, a nonlinear multiparameters temperature error modeling and compensation method based on Bayesian regularization neural network was proposed. The temperature error of POS was analyzed and a nonlinear multiparameters model was established. Bayesian regularization method was used as the evaluation criterion, which further optimized the coefficients of the temperature error. The experimental results show that the proposed method can improve temperature environmental adaptability and precision. The developed POS had been successfully applied in airborne TSMFTIS remote sensing system for the first time, which improved the accuracy of the reconstructed spectrum by 47.99%.
Frank, Laurence Emmanuelle
2006-01-01
Feature Network Models (FNM) are graphical structures that represent proximity data in a discrete space with the use of features. A statistical inference theory is introduced, based on the additivity properties of networks and the linear regression framework. Considering features as predictor
Related work on reference modeling for collaborative networks
Afsarmanesh, H.; Camarinha-Matos, L.M.; Camarinha-Matos, L.M.; Afsarmanesh, H.
2008-01-01
Several international research and development initiatives have led to development of models for organizations and organization interactions. These models and their approaches constitute a background for development of reference models for collaborative networks. A brief survey of work on modeling
A random spatial network model based on elementary postulates
Karlinger, Michael R.; Troutman, Brent M.
1989-01-01
A model for generating random spatial networks that is based on elementary postulates comparable to those of the random topology model is proposed. In contrast to the random topology model, this model ascribes a unique spatial specification to generated drainage networks, a distinguishing property of some network growth models. The simplicity of the postulates creates an opportunity for potential analytic investigations of the probabilistic structure of the drainage networks, while the spatial specification enables analyses of spatially dependent network properties. In the random topology model all drainage networks, conditioned on magnitude (number of first-order streams), are equally likely, whereas in this model all spanning trees of a grid, conditioned on area and drainage density, are equally likely. As a result, link lengths in the generated networks are not independent, as usually assumed in the random topology model. For a preliminary model evaluation, scale-dependent network characteristics, such as geometric diameter and link length properties, and topologic characteristics, such as bifurcation ratio, are computed for sets of drainage networks generated on square and rectangular grids. Statistics of the bifurcation and length ratios fall within the range of values reported for natural drainage networks, but geometric diameters tend to be relatively longer than those for natural networks.
PageRank model of opinion formation on Ulam networks
Chakhmakhchyan, L.; Shepelyansky, D.
2013-12-01
We consider a PageRank model of opinion formation on Ulam networks, generated by the intermittency map and the typical Chirikov map. The Ulam networks generated by these maps have certain similarities with such scale-free networks as the World Wide Web (WWW), showing an algebraic decay of the PageRank probability. We find that the opinion formation process on Ulam networks has certain similarities but also distinct features comparing to the WWW. We attribute these distinctions to internal differences in network structure of the Ulam and WWW networks. We also analyze the process of opinion formation in the frame of generalized Sznajd model which protects opinion of small communities.
An Improved Car-Following Model in Vehicle Networking Based on Network Control
Directory of Open Access Journals (Sweden)
D. Y. Kong
2014-01-01
Full Text Available Vehicle networking is a system to realize information interoperability between vehicles and people, vehicles and roads, vehicles and vehicles, and cars and transport facilities, through the network information exchange, in order to achieve the effective monitoring of the vehicle and traffic flow. Realizing information interoperability between vehicles and vehicles, which can affect the traffic flow, is an important application of network control system (NCS. In this paper, a car-following model using vehicle networking theory is established, based on network control principle. The car-following model, which is an improvement of the traditional traffic model, describes the traffic in vehicle networking condition. The impact that vehicle networking has on the traffic flow is quantitatively assessed in a particular scene of one-way, no lane changing highway. The examples show that the capacity of the road is effectively enhanced by using vehicle networking.
Lu, Thomas; Pham, Timothy; Liao, Jason
2011-01-01
This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.
Modeling management of research and education networks
Galagan, D.V.
2004-01-01
Computer networks and their services have become an essential part of research and education. Nowadays every modern R&E institution must have a computer network and provide network services to its students and staff. In addition to its internal computer network, every R&E institution must have a
Marketing communications model for innovation networks
Directory of Open Access Journals (Sweden)
Tiago João Freitas Correia
2015-10-01
Full Text Available Innovation is an increasingly relevant concept for the success of any organization, but it also represents a set of internal and external considerations, barriers and challenges to overcome. Along the concept of innovation, new paradigms emerge such as open innovation and co-creation that are simultaneously innovation modifiers and intensifiers in organizations, promoting organizational openness and stakeholder integration within the value creation process. Innovation networks composed by a multiplicity of agents in co-creative work perform as innovation mechanisms to face the increasingly complexity of products, services and markets. Technology, especially the Internet, is an enabler of all process among organizations supported by co-creative platforms for innovation. The definition of marketing communication strategies that promote motivation and involvement of all stakeholders in synergic creation and external promotion is the central aspect of this research. The implementation of the projects is performed by participative workshops with stakeholders from Madan Parque through IDEAS(REVOLUTION methodology and the operational model LinkUp parameterized for the project. The project is divided into the first part, the theoretical framework, and the second part where a model is developed for the marketing communication strategies that appeal to the Madan Parque case study. Keywords: Marketing Communication; Open Innovation, Technology; Innovation Networks; Incubator; Co-Creation.
A graph model for opportunistic network coding
Sorour, Sameh
2015-08-12
© 2015 IEEE. Recent advancements in graph-based analysis and solutions of instantly decodable network coding (IDNC) trigger the interest to extend them to more complicated opportunistic network coding (ONC) scenarios, with limited increase in complexity. In this paper, we design a simple IDNC-like graph model for a specific subclass of ONC, by introducing a more generalized definition of its vertices and the notion of vertex aggregation in order to represent the storage of non-instantly-decodable packets in ONC. Based on this representation, we determine the set of pairwise vertex adjacency conditions that can populate this graph with edges so as to guarantee decodability or aggregation for the vertices of each clique in this graph. We then develop the algorithmic procedures that can be applied on the designed graph model to optimize any performance metric for this ONC subclass. A case study on reducing the completion time shows that the proposed framework improves on the performance of IDNC and gets very close to the optimal performance.
An adaptive wavelet-network model for forecasting daily total solar-radiation
International Nuclear Information System (INIS)
Mellit, A.; Benghanem, M.; Kalogirou, S.A.
2006-01-01
The combination of wavelet theory and neural networks has lead to the development of wavelet networks. Wavelet-networks are feed-forward networks using wavelets as activation functions. Wavelet-networks have been used successfully in various engineering applications such as classification, identification and control problems. In this paper, the use of adaptive wavelet-network architecture in finding a suitable forecasting model for predicting the daily total solar-radiation is investigated. Total solar-radiation is considered as the most important parameter in the performance prediction of renewable energy systems, particularly in sizing photovoltaic (PV) power systems. For this purpose, daily total solar-radiation data have been recorded during the period extending from 1981 to 2001, by a meteorological station in Algeria. The wavelet-network model has been trained by using either the 19 years of data or one year of the data. In both cases the total solar radiation data corresponding to year 2001 was used for testing the model. The network was trained to accept and handle a number of unusual cases. Results indicate that the model predicts daily total solar-radiation values with a good accuracy of approximately 97% and the mean absolute percentage error is not more than 6%. In addition, the performance of the model was compared with different neural network structures and classical models. Training algorithms for wavelet-networks require smaller numbers of iterations when compared with other neural networks. The model can be used to fill missing data in weather databases. Additionally, the proposed model can be generalized and used in different locations and for other weather data, such as sunshine duration and ambient temperature. Finally, an application using the model for sizing a PV-power system is presented in order to confirm the validity of this model
Efficient Bayesian network modeling of systems
International Nuclear Information System (INIS)
Bensi, Michelle; Kiureghian, Armen Der; Straub, Daniel
2013-01-01
The Bayesian network (BN) is a convenient tool for probabilistic modeling of system performance, particularly when it is of interest to update the reliability of the system or its components in light of observed information. In this paper, BN structures for modeling the performance of systems that are defined in terms of their minimum link or cut sets are investigated. Standard BN structures that define the system node as a child of its constituent components or its minimum link/cut sets lead to converging structures, which are computationally disadvantageous and could severely hamper application of the BN to real systems. A systematic approach to defining an alternative formulation is developed that creates chain-like BN structures that are orders of magnitude more efficient, particularly in terms of computational memory demand. The formulation uses an integer optimization algorithm to identify the most efficient BN structure. Example applications demonstrate the proposed methodology and quantify the gained computational advantage
Modeling stochasticity in biochemical reaction networks
International Nuclear Information System (INIS)
Constantino, P H; Vlysidis, M; Smadbeck, P; Kaznessis, Y N
2016-01-01
Small biomolecular systems are inherently stochastic. Indeed, fluctuations of molecular species are substantial in living organisms and may result in significant variation in cellular phenotypes. The chemical master equation (CME) is the most detailed mathematical model that can describe stochastic behaviors. However, because of its complexity the CME has been solved for only few, very small reaction networks. As a result, the contribution of CME-based approaches to biology has been very limited. In this review we discuss the approach of solving CME by a set of differential equations of probability moments, called moment equations. We present different approaches to produce and to solve these equations, emphasizing the use of factorial moments and the zero information entropy closure scheme. We also provide information on the stability analysis of stochastic systems. Finally, we speculate on the utility of CME-based modeling formalisms, especially in the context of synthetic biology efforts. (topical review)
SUSTAIN: a network model of category learning.
Love, Bradley C; Medin, Douglas L; Gureckis, Todd M
2004-04-01
SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes-attractors-rules. SUSTAIN's discovery of category substructure is affected not only by the structure of the world but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts in which identification learning is faster than classification learning.
Time response prediction of Brazilian Nuclear Power Plant temperature sensors using neural networks
Energy Technology Data Exchange (ETDEWEB)
Santos, Roberto Carlos dos; Pereira, Iraci Martinez, E-mail: rcsantos@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)
2011-07-01
This work presents the results of the time constants values predicted from ANN using Angra I Brazilian nuclear power plant data. The signals obtained from LCSR loop current step response test sensors installed in the process presents noise end fluctuations that are inherent of operational conditions. Angra I nuclear power plant has 20 RTDs as part of the protection reactor system. The results were compared with those obtained from traditional way. Primary coolant RTDs (Resistance Temperature Detector) typically feed the plant's control and safety systems and must, therefore, be very accurate and have good dynamic performance. An in-situ test method called LCSR - loop current step response test was developed to measure remotely the response time of RTDs. In the LCSR method, the response time of the sensor is identified by means of the LCSR transformation that involves the dynamic response modal time constants determination using a nodal heat transfer model. For this reason, this calculation is not simple and requires specialized personnel. This work combines the two methodologies, Plunge test and LCSR test, using neural networks. With the use of neural networks it will not be necessary to use the LCSR transformation to determine sensor's time constant and this leads to more robust results. (author)
Time response prediction of Brazilian Nuclear Power Plant temperature sensors using neural networks
International Nuclear Information System (INIS)
Santos, Roberto Carlos dos; Pereira, Iraci Martinez
2011-01-01
This work presents the results of the time constants values predicted from ANN using Angra I Brazilian nuclear power plant data. The signals obtained from LCSR loop current step response test sensors installed in the process presents noise end fluctuations that are inherent of operational conditions. Angra I nuclear power plant has 20 RTDs as part of the protection reactor system. The results were compared with those obtained from traditional way. Primary coolant RTDs (Resistance Temperature Detector) typically feed the plant's control and safety systems and must, therefore, be very accurate and have good dynamic performance. An in-situ test method called LCSR - loop current step response test was developed to measure remotely the response time of RTDs. In the LCSR method, the response time of the sensor is identified by means of the LCSR transformation that involves the dynamic response modal time constants determination using a nodal heat transfer model. For this reason, this calculation is not simple and requires specialized personnel. This work combines the two methodologies, Plunge test and LCSR test, using neural networks. With the use of neural networks it will not be necessary to use the LCSR transformation to determine sensor's time constant and this leads to more robust results. (author)
Multiplicative Attribute Graph Model of Real-World Networks
Energy Technology Data Exchange (ETDEWEB)
Kim, Myunghwan [Stanford Univ., CA (United States); Leskovec, Jure [Stanford Univ., CA (United States)
2010-10-20
Large scale real-world network data, such as social networks, Internet andWeb graphs, is ubiquitous in a variety of scientific domains. The study of such social and information networks commonly finds patterns and explain their emergence through tractable models. In most networks, especially in social networks, nodes also have a rich set of attributes (e.g., age, gender) associatedwith them. However, most of the existing network models focus only on modeling the network structure while ignoring the features of nodes in the network. Here we present a class of network models that we refer to as the Multiplicative Attribute Graphs (MAG), which naturally captures the interactions between the network structure and node attributes. We consider a model where each node has a vector of categorical features associated with it. The probability of an edge between a pair of nodes then depends on the product of individual attributeattribute similarities. The model yields itself to mathematical analysis as well as fit to real data. We derive thresholds for the connectivity, the emergence of the giant connected component, and show that the model gives rise to graphs with a constant diameter. Moreover, we analyze the degree distribution to show that the model can produce networks with either lognormal or power-law degree distribution depending on certain conditions.
International Nuclear Information System (INIS)
Saito, H; Jansen, K.; Cichy, K.; Frankfurt Univ.; Poznan Univ.
2014-12-01
We present our recent results for the tensor network (TN) approach to lattice gauge theories. TN methods provide an efficient approximation for quantum many-body states. We employ TN for one dimensional systems, Matrix Product States, to investigate the 1-flavour Schwinger model. In this study, we compute the chiral condensate at finite temperature. From the continuum extrapolation, we obtain the chiral condensate in the high temperature region consistent with the analytical calculation by Sachs and Wipf.
Multilevel method for modeling large-scale networks.
Energy Technology Data Exchange (ETDEWEB)
Safro, I. M. (Mathematics and Computer Science)
2012-02-24
Understanding the behavior of real complex networks is of great theoretical and practical significance. It includes developing accurate artificial models whose topological properties are similar to the real networks, generating the artificial networks at different scales under special conditions, investigating a network dynamics, reconstructing missing data, predicting network response, detecting anomalies and other tasks. Network generation, reconstruction, and prediction of its future topology are central issues of this field. In this project, we address the questions related to the understanding of the network modeling, investigating its structure and properties, and generating artificial networks. Most of the modern network generation methods are based either on various random graph models (reinforced by a set of properties such as power law distribution of node degrees, graph diameter, and number of triangles) or on the principle of replicating an existing model with elements of randomization such as R-MAT generator and Kronecker product modeling. Hierarchical models operate at different levels of network hierarchy but with the same finest elements of the network. However, in many cases the methods that include randomization and replication elements on the finest relationships between network nodes and modeling that addresses the problem of preserving a set of simplified properties do not fit accurately enough the real networks. Among the unsatisfactory features are numerically inadequate results, non-stability of algorithms on real (artificial) data, that have been tested on artificial (real) data, and incorrect behavior at different scales. One reason is that randomization and replication of existing structures can create conflicts between fine and coarse scales of the real network geometry. Moreover, the randomization and satisfying of some attribute at the same time can abolish those topological attributes that have been undefined or hidden from
A comprehensive probabilistic analysis model of oil pipelines network based on Bayesian network
Zhang, C.; Qin, T. X.; Jiang, B.; Huang, C.
2018-02-01
Oil pipelines network is one of the most important facilities of energy transportation. But oil pipelines network accident may result in serious disasters. Some analysis models for these accidents have been established mainly based on three methods, including event-tree, accident simulation and Bayesian network. Among these methods, Bayesian network is suitable for probabilistic analysis. But not all the important influencing factors are considered and the deployment rule of the factors has not been established. This paper proposed a probabilistic analysis model of oil pipelines network based on Bayesian network. Most of the important influencing factors, including the key environment condition and emergency response are considered in this model. Moreover, the paper also introduces a deployment rule for these factors. The model can be used in probabilistic analysis and sensitive analysis of oil pipelines network accident.
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.
Automata network models of galaxy evolution
Chappell, David; Scalo, John
1993-01-01
Two ideas appear frequently in theories of star formation and galaxy evolution: (1) star formation is nonlocally excitatory, stimulating star formation in neighboring regions by propagation of a dense fragmenting shell or the compression of preexisting clouds; and (2) star formation is nonlocally inhibitory, making H2 regions and explosions which can create low-density and/or high temperature regions and increase the macroscopic velocity dispersion of the cloudy gas. Since it is not possible, given the present state of hydrodynamic modeling, to estimate whether one of these effects greatly dominates the other, it is of interest to investigate the predicted spatial pattern of star formation and its temporal behavior in simple models which incorporate both effects in a controlled manner. The present work presents preliminary results of such a study which is based on lattice galaxy models with various types of nonlocal inhibitory and excitatory couplings of the local SFR to the gas density, temperature, and velocity field meant to model a number of theoretical suggestions.
Appplication of statistical mechanical methods to the modeling of social networks
Strathman, Anthony Robert
With the recent availability of large-scale social data sets, social networks have become open to quantitative analysis via the methods of statistical physics. We examine the statistical properties of a real large-scale social network, generated from cellular phone call-trace logs. We find this network, like many other social networks to be assortative (r = 0.31) and clustered (i.e., strongly transitive, C = 0.21). We measure fluctuation scaling to identify the presence of internal structure in the network and find that structural inhomogeneity effectively disappears at the scale of a few hundred nodes, though there is no sharp cutoff. We introduce an agent-based model of social behavior, designed to model the formation and dissolution of social ties. The model is a modified Metropolis algorithm containing agents operating under the basic sociological constraints of reciprocity, communication need and transitivity. The model introduces the concept of a social temperature. We go on to show that this simple model reproduces the global statistical network features (incl. assortativity, connected fraction, mean degree, clustering, and mean shortest path length) of the real network data and undergoes two phase transitions, one being from a "gas" to a "liquid" state and the second from a liquid to a glassy state as function of this social temperature.
International Nuclear Information System (INIS)
Thomas, J.R. Jr.; Adams, J.T.
1994-01-01
A neural network was trained with data for the frequency response function between in-core neutron noise and core-exit thermocouple noise in a pressurized water reactor, with the moderator temperature coefficient (MTC) as target. The trained network was subsequently used to predict the MTC at other points in the same fuel cycle. Results support use of the method for operating pressurized water reactors provided noise data can be accumulated for several fuel cycles to provide a training base
Road network safety evaluation using Bayesian hierarchical joint model.
Wang, Jie; Huang, Helai
2016-05-01
Safety and efficiency are commonly regarded as two significant performance indicators of transportation systems. In practice, road network planning has focused on road capacity and transport efficiency whereas the safety level of a road network has received little attention in the planning stage. This study develops a Bayesian hierarchical joint model for road network safety evaluation to help planners take traffic safety into account when planning a road network. The proposed model establishes relationships between road network risk and micro-level variables related to road entities and traffic volume, as well as socioeconomic, trip generation and network density variables at macro level which are generally used for long term transportation plans. In addition, network spatial correlation between intersections and their connected road segments is also considered in the model. A road network is elaborately selected in order to compare the proposed hierarchical joint model with a previous joint model and a negative binomial model. According to the results of the model comparison, the hierarchical joint model outperforms the joint model and negative binomial model in terms of the goodness-of-fit and predictive performance, which indicates the reasonableness of considering the hierarchical data structure in crash prediction and analysis. Moreover, both random effects at the TAZ level and the spatial correlation between intersections and their adjacent segments are found to be significant, supporting the employment of the hierarchical joint model as an alternative in road-network-level safety modeling as well. Copyright © 2016 Elsevier Ltd. All rights reserved.
Models as Tools of Analysis of a Network Organisation
Directory of Open Access Journals (Sweden)
Wojciech Pająk
2013-06-01
Full Text Available The paper presents models which may be applied as tools of analysis of a network organisation. The starting point of the discussion is defining the following terms: supply chain and network organisation. Further parts of the paper present basic assumptions analysis of a network organisation. Then the study characterises the best known models utilised in analysis of a network organisation. The purpose of the article is to define the notion and the essence of network organizations and to present the models used for their analysis.
Model for the evolution of network dislocation density in irradiated metals
International Nuclear Information System (INIS)
Garner, F.A.; Wolfer, W.G.
1982-01-01
It is a well-known fact that the total dislocation density that evolves in irradiated metals is a strong function of irradiation temperature. The dislocation density comprises two components, however, and only one of these (Frank loops) retains its temperature dependence at high fluence. The network dislocation density approaches a saturation level which is relatively insensitive to starting microstructure, stress, irradiation temperature, displacement rate and helium level. The latter statement is supported in this paper by a review of published microstructural data. A model has been developed to explain the insensitivity to many variables of the saturation network dislocation density in irradiated metals. This model also explains how the rate of approach to saturation can be sensitive to displacement rate and temperature while the saturation level itself is not dependent on temperature
A temperature dependent slip factor based thermal model for friction
Indian Academy of Sciences (India)
This paper proposes a new slip factor based three-dimensional thermal model to predict the temperature distribution during friction stir welding of 304L stainless steel plates. The proposed model employs temperature and radius dependent heat source to study the thermal cycle, temperature distribution, power required, the ...
Modeling of AlMg Sheet Forming at Elevated Temperatures
van den Boogaard, Antonius H.; Bolt, P.; Werkhoven, R.
2001-01-01
The process limits of aluminum sheet forming processes can be improved by control-ling local flow behavior by means of elevated temperatures and temperature gradients. In order to accurately model the deep drawing or stretching of aluminum sheet at elevated temperatures, a model is required that
Resolving structural variability in network models and the brain.
Directory of Open Access Journals (Sweden)
Florian Klimm
2014-03-01
Full Text Available Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling--in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity do not in general simultaneously display a second (e.g., hierarchy. This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful
Modeling shoot-tip temperature in the greenhouse environment
International Nuclear Information System (INIS)
Faust, J.E.; Heins, R.D.
1998-01-01
An energy-balance model is described that predicts vinca (Catharanthus roseus L.) shoot-tip temperature using four environmental measurements: solar radiation and dry bulb, wet bulb, and glazing material temperature. The time and magnitude of the differences between shoot-tip and air temperature were determined in greenhouses maintained at air temperatures of 15, 20, 25, 30, or 35 °C. At night, shoot-tip temperature was always below air temperature. Shoot-tip temperature decreased from 0.5 to 5 °C below air temperature as greenhouse glass temperature decreased from 2 to 15 °C below air temperature. During the photoperiod under low vapor-pressure deficit (VPD) and low air temperature, shoot-tip temperature increased ≈4 °C as solar radiation increased from 0 to 600 W·m -2 . Under high VPD and high air temperature, shoot-tip temperature initially decreased 1 to 2 °C at sunrise, then increased later in the morning as solar radiation increased. The model predicted shoot-tip temperatures within ±1 °C of 81% of the observed 1-hour average shoot-tip temperatures. The model was used to simulate shoot-tip temperatures under different VPD, solar radiation, and air temperatures. Since the rate of leaf and flower development are influenced by the temperature of the meristematic tissues, a model of shoot-tip temperature will be a valuable tool to predict plant development in greenhouses and to control the greenhouse environment based on a plant temperature setpoint. (author)
DEFF Research Database (Denmark)
Tol, Hakan; Svendsen, Svend
2015-01-01
This paper presents a method for the dimensioning of the low-energy District Heating (DH) piping networks operating with a control philosophy of supplying heat in low-temperature such as 55 °C in supply and 25°C in return regularly while the supply temperature levels are being boosted in cold...... winter periods. The performance of the existing radiators that were formerly sized with over-dimensions was analyzed, its results being used as input data for the performance evaluation of the piping network of the low-energy DH system operating with the control philosophy in question. The optimization...
Dynamic Model of the High Temperature Proton Exchange Membrane Fuel Cell Stack Temperature
DEFF Research Database (Denmark)
Andreasen, Søren Juhl; Kær, Søren Knudsen
2009-01-01
The present work involves the development of a model for predicting the dynamic temperature of a high temperature proton exchange membrane (HTPEM) fuel cell stack. The model is developed to test different thermal control strategies before implementing them in the actual system. The test system co...... elements for start-up, heat conduction through stack insulation, cathode air convection, and heating of the inlet gases in the manifold. Various measurements are presented to validate the model predictions of the stack temperatures....
Modelling of temperature distribution and temperature pulsations in elements of fast breeder reactor
International Nuclear Information System (INIS)
Sorokin, A.P.; Bogoslovskaia, G.P.; Ushakov, P.A.; Zhukov, A.V.; Ivanov, Eu.F.; Matjukhin, N.M.
2004-01-01
From thermophysical point of view, integrated configuration of liquid metal cooled reactor has some limitations. Large volume of mixing chamber causes a complex behavior of thermal hydraulic characteristics in such facilities. Also, this volume is responsible for large-scale eddies in the coolant, existence of stagnant areas and flow stratification, occurrence of temperature non-uniformity and pulsation of coolant and structure temperatures. Temperature non-uniformities and temperature pulsations depend heavily even on small variations in reactor core design. The paper presents some results on modeling of thermal hydraulic processes occurring in liquid metal cooled reactor. The behavior of following parameters are discussed: temperature non-uniformities at the core output and related temperature pulsations; temperature pulsations due to mixing of sodium jets at different temperatures; temperature pulsations arising if a part of loop (circuit) is shut off; temperature non-uniformities and pulsation at the core output and related temperature pulsation; temperature pulsations due to mixing of sodium jets at different temperatures; temperature pulsations arising if a part of loop (circuit) is shut off; temperature non-uniformities and pulsation of temperature during transients and during transition to natural convection cooling. Also, the issue of modeling of temperature behavior in compact arrangement of fast reactor fuel pins using water as modeling liquid is considered in the paper. One more discussion is concerned with experimental method of modeling of liquid metal mixing with the use of air. The method is based on freon tracer technique. The results of simulation of the thermal hydraulic processes mentioned above have been analyzed, that will allow the main lines of the study to be determined and conclusion to be drawn regarding the temperature behavior in fast reactor units. (author)
Women’s Social Networks and Birth Attendant Decisions: Application of the Network-Episode Model
Edmonds, Joyce K.; Hruschka, Daniel; Bernard, H. Russell; Sibley, Lynn
2011-01-01
This paper examines the association of women's social networks with the use of skilled birth attendants in uncomplicated pregnancy and childbirth in Matlab, Bangladesh. The Network-Episode Model was applied to determine if network structure variables (density / kinship homogeneity / strength of ties) together with network content (endorsement for or against a particular type of birth attendant) explain the type of birth attendant used by women above and beyond the variance explained by women'...
A last updating evolution model for online social networks
Bu, Zhan; Xia, Zhengyou; Wang, Jiandong; Zhang, Chengcui
2013-05-01
As information technology has advanced, people are turning to electronic media more frequently for communication, and social relationships are increasingly found on online channels. However, there is very limited knowledge about the actual evolution of the online social networks. In this paper, we propose and study a novel evolution network model with the new concept of “last updating time”, which exists in many real-life online social networks. The last updating evolution network model can maintain the robustness of scale-free networks and can improve the network reliance against intentional attacks. What is more, we also found that it has the “small-world effect”, which is the inherent property of most social networks. Simulation experiment based on this model show that the results and the real-life data are consistent, which means that our model is valid.
Low-temperature plasma modelling and simulation
Dijk, van J.
2011-01-01
Since its inception in the beginning of the twentieth century, low-temperature plasma science has become a major ¿eld of science. Low-temperature plasma sources and gas discharges are found in domestic, industrial, atmospheric and extra-terrestrial settings. Examples of domestic discharges are those
Adaptive Networks Theory, Models and Applications
Gross, Thilo
2009-01-01
With adaptive, complex networks, the evolution of the network topology and the dynamical processes on the network are equally important and often fundamentally entangled. Recent research has shown that such networks can exhibit a plethora of new phenomena which are ultimately required to describe many real-world networks. Some of those phenomena include robust self-organization towards dynamical criticality, formation of complex global topologies based on simple, local rules, and the spontaneous division of "labor" in which an initially homogenous population of network nodes self-organizes into functionally distinct classes. These are just a few. This book is a state-of-the-art survey of those unique networks. In it, leading researchers set out to define the future scope and direction of some of the most advanced developments in the vast field of complex network science and its applications.
A graph model for opportunistic network coding
Sorour, Sameh; Aboutoraby, Neda; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim
2015-01-01
© 2015 IEEE. Recent advancements in graph-based analysis and solutions of instantly decodable network coding (IDNC) trigger the interest to extend them to more complicated opportunistic network coding (ONC) scenarios, with limited increase
Modeling and control of magnetorheological fluid dampers using neural networks
Wang, D. H.; Liao, W. H.
2005-02-01
Due to the inherent nonlinear nature of magnetorheological (MR) fluid dampers, one of the challenging aspects for utilizing these devices to achieve high system performance is the development of accurate models and control algorithms that can take advantage of their unique characteristics. In this paper, the direct identification and inverse dynamic modeling for MR fluid dampers using feedforward and recurrent neural networks are studied. The trained direct identification neural network model can be used to predict the damping force of the MR fluid damper on line, on the basis of the dynamic responses across the MR fluid damper and the command voltage, and the inverse dynamic neural network model can be used to generate the command voltage according to the desired damping force through supervised learning. The architectures and the learning methods of the dynamic neural network models and inverse neural network models for MR fluid dampers are presented, and some simulation results are discussed. Finally, the trained neural network models are applied to predict and control the damping force of the MR fluid damper. Moreover, validation methods for the neural network models developed are proposed and used to evaluate their performance. Validation results with different data sets indicate that the proposed direct identification dynamic model using the recurrent neural network can be used to predict the damping force accurately and the inverse identification dynamic model using the recurrent neural network can act as a damper controller to generate the command voltage when the MR fluid damper is used in a semi-active mode.
Structural equation models from paths to networks
Westland, J Christopher
2015-01-01
This compact reference surveys the full range of available structural equation modeling (SEM) methodologies. It reviews applications in a broad range of disciplines, particularly in the social sciences where many key concepts are not directly observable. This is the first book to present SEM’s development in its proper historical context–essential to understanding the application, strengths and weaknesses of each particular method. This book also surveys the emerging path and network approaches that complement and enhance SEM, and that will grow in importance in the near future. SEM’s ability to accommodate unobservable theory constructs through latent variables is of significant importance to social scientists. Latent variable theory and application are comprehensively explained, and methods are presented for extending their power, including guidelines for data preparation, sample size calculation, and the special treatment of Likert scale data. Tables of software, methodologies and fit st...
DEFF Research Database (Denmark)
Park, Byung Sik; Imran, Muhammad; Hoon, Im-Yong
2017-01-01
. The corresponding heat loss from secondary network, pumping power and area of domestic hot water heat exchanger unit for each supply temperature and temperature difference for required heating load of the apartment complex are calculated. Results indicate that when supply temperature is decreased from 65 °C to 45...... apartment. The Apartment complex has 15 floors, 4 apartments on each floor and each apartment has heating surface area of 85 m2. The supply temperature of the hot water is reduced from 65 °C to 45 °C and the temperature difference between supply and return line is varied from 18 °C to 27 °C...... °C, area of heat exchanger is increased by 68.2%, pumping power is also increased by 9.8% and heat loss is reduced by 15.6%. These results correspond to a temperature difference of 20 °C, the standard temperature difference in South Korea residential heating system. Economic assessment...
Network formation under heterogeneous costs: The multiple group model
Kamphorst, J.J.A.; van der Laan, G.
2007-01-01
It is widely recognized that the shape of networks influences both individual and aggregate behavior. This raises the question which types of networks are likely to arise. In this paper we investigate a model of network formation, where players are divided into groups and the costs of a link between
Neural networks in economic modelling : An empirical study
Verkooijen, W.J.H.
1996-01-01
This dissertation addresses the statistical aspects of neural networks and their usability for solving problems in economics and finance. Neural networks are discussed in a framework of modelling which is generally accepted in econometrics. Within this framework a neural network is regarded as a
Multiple Social Networks, Data Models and Measures for
DEFF Research Database (Denmark)
Magnani, Matteo; Rossi, Luca
2017-01-01
Multiple Social Network Analysis is a discipline defining models, measures, methodologies, and algorithms to study multiple social networks together as a single social system. It is particularly valuable when the networks are interconnected, e.g., the same actors are present in more than one...
Temperature bounds in a model of laminar flames
International Nuclear Information System (INIS)
Kirane, M.; Badraoui, S.
1994-06-01
We consider reaction-diffusion equations coupling temperature and mass fraction in one-step-reaction model of combustion in R N . Uniform temperature bounds are derived when the Lewis number is less than one. This result completes the case of Lewis number greater than one studied by J.D. Avrin and M. Kirane ''Temperature growth and temperature bounds in special cases of combustion models'' (to appear in Applicable Analysis). (author). 5 refs
Agent Based Modeling on Organizational Dynamics of Terrorist Network
Bo Li; Duoyong Sun; Renqi Zhu; Ze Li
2015-01-01
Modeling organizational dynamics of terrorist network is a critical issue in computational analysis of terrorism research. The first step for effective counterterrorism and strategic intervention is to investigate how the terrorists operate with the relational network and what affects the performance. In this paper, we investigate the organizational dynamics by employing a computational experimentation methodology. The hierarchical cellular network model and the organizational dynamics model ...
Learning Analytics for Networked Learning Models
Joksimovic, Srecko; Hatala, Marek; Gaševic, Dragan
2014-01-01
Teaching and learning in networked settings has attracted significant attention recently. The central topic of networked learning research is human-human and human-information interactions occurring within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach to…
Network model for fine coal dewatering. Part I. The model
Energy Technology Data Exchange (ETDEWEB)
Qamar, I.; Tierney, J.W.; Chiang, S.H.
1985-08-01
There is a body of well established research in filtration and related subjects, but much of it has been empirical - based on correlations from experimental data. This approach has the disadvantage that it lacks generality, and it is difficult to predict the behavior of new or different systems. A more general method for studying dewatering is needed-one which will include the microscopic characteristics of the filter cake, which, like other porous media, contains a complicated network of interconnected pores through which the fluid must flow. These pores play an important role in dewatering because they give rise to capillary forces when one fluid is displacing another. In this report, we describe a network model which we believe satisfies these requirements. In the main body of this report, the model is described in detail. Background information is given where appropriate, and a brief description is given of the experimental work being done in our laboratories to verify the model. A detailed description of the experimental procedures and results is given in other DOE reports. The computer programs which are needed to solve the model are described in detail in the Appendices and are accompanied by flow charts, sample problems, and sample outputs. Sufficient detail is given in order to use the model programs on other computer systems. 32 refs., 7 figs., 5 tabs.
Directory of Open Access Journals (Sweden)
Lan Liu
2017-01-01
Full Text Available As the adoption of Software Defined Networks (SDNs grows, the security of SDN still has several unaddressed limitations. A key network security research area is in the study of malware propagation across the SDN-enabled networks. To analyze the spreading processes of network malware (e.g., viruses in SDN, we propose a dynamic model with a time-varying community network, inspired by research models on the spread of epidemics in complex networks across communities. We assume subnets of the network as communities and links that are dense in subnets but sparse between subnets. Using numerical simulation and theoretical analysis, we find that the efficiency of network malware propagation in this model depends on the mobility rate q of the nodes between subnets. We also find that there exists a mobility rate threshold qc. The network malware will spread in the SDN when the mobility rate q>qc. The malware will survive when q>qc and perish when q
2014-01-29
DISTRIBUTION A: Approved for public release; distribution is unlimited. Thermosetting Polymers Have a TG Envelope – Not Just a TG 4 • The glass transition...glass transition temperature of a thermosetting polymer can vary over a wide range of temperatures depending on how the polymer is processed • A... thermosetting polymer with only one kind of network formation and negligible side reactions, the conversion may be determined at every point in the scan. • By
Stochastic actor-oriented models for network change
Snijders, T.A.B.
1996-01-01
A class of models is proposed for longitudinal network data. These models are along the lines of methodological individualism: actors use heuristics to try to achieve their individual goals, subject to constraints. The current network structure is among these constraints. The models are continuous
Modeling the reemergence of information diffusion in social network
Yang, Dingda; Liao, Xiangwen; Shen, Huawei; Cheng, Xueqi; Chen, Guolong
2018-01-01
Information diffusion in networks is an important research topic in various fields. Existing studies either focus on modeling the process of information diffusion, e.g., independent cascade model and linear threshold model, or investigate information diffusion in networks with certain structural characteristics such as scale-free networks and small world networks. However, there are still several phenomena that have not been captured by existing information diffusion models. One of the prominent phenomena is the reemergence of information diffusion, i.e., a piece of information reemerges after the completion of its initial diffusion process. In this paper, we propose an optimized information diffusion model by introducing a new informed state into traditional susceptible-infected-removed model. We verify the proposed model via simulations in real-world social networks, and the results indicate that the model can reproduce the reemergence of information during the diffusion process.
An Improved Walk Model for Train Movement on Railway Network
International Nuclear Information System (INIS)
Li Keping; Mao Bohua; Gao Ziyou
2009-01-01
In this paper, we propose an improved walk model for simulating the train movement on railway network. In the proposed method, walkers represent trains. The improved walk model is a kind of the network-based simulation analysis model. Using some management rules for walker movement, walker can dynamically determine its departure and arrival times at stations. In order to test the proposed method, we simulate the train movement on a part of railway network. The numerical simulation and analytical results demonstrate that the improved model is an effective tool for simulating the train movement on railway network. Moreover, it can well capture the characteristic behaviors of train scheduling in railway traffic. (general)
Infinite Multiple Membership Relational Modeling for Complex Networks
DEFF Research Database (Denmark)
Mørup, Morten; Schmidt, Mikkel Nørgaard; Hansen, Lars Kai
Learning latent structure in complex networks has become an important problem fueled by many types of networked data originating from practically all fields of science. In this paper, we propose a new non-parametric Bayesian multiplemembership latent feature model for networks. Contrary to existing...... multiplemembership models that scale quadratically in the number of vertices the proposedmodel scales linearly in the number of links admittingmultiple-membership analysis in large scale networks. We demonstrate a connection between the single membership relational model and multiple membership models and show...
Stabilization of model-based networked control systems
Energy Technology Data Exchange (ETDEWEB)
Miranda, Francisco [CIDMA, Universidade de Aveiro, Aveiro (Portugal); Instituto Politécnico de Viana do Castelo, Viana do Castelo (Portugal); Abreu, Carlos [Instituto Politécnico de Viana do Castelo, Viana do Castelo (Portugal); CMEMS-UMINHO, Universidade do Minho, Braga (Portugal); Mendes, Paulo M. [CMEMS-UMINHO, Universidade do Minho, Braga (Portugal)
2016-06-08
A class of networked control systems called Model-Based Networked Control Systems (MB-NCSs) is considered. Stabilization of MB-NCSs is studied using feedback controls and simulation of stabilization for different feedbacks is made with the purpose to reduce the network trafic. The feedback control input is applied in a compensated model of the plant that approximates the plant dynamics and stabilizes the plant even under slow network conditions. Conditions for global exponential stabilizability and for the choosing of a feedback control input for a given constant time between the information moments of the network are derived. An optimal control problem to obtain an optimal feedback control is also presented.
NLP model and stochastic multi-start optimization approach for heat exchanger networks
International Nuclear Information System (INIS)
Núñez-Serna, Rosa I.; Zamora, Juan M.
2016-01-01
Highlights: • An NLP model for the optimal design of heat exchanger networks is proposed. • The NLP model is developed from a stage-wise grid diagram representation. • A two-phase stochastic multi-start optimization methodology is utilized. • Improved network designs are obtained with different heat load distributions. • Structural changes and reductions in the number of heat exchangers are produced. - Abstract: Heat exchanger network synthesis methodologies frequently identify good network structures, which nevertheless, might be accompanied by suboptimal values of design variables. The objective of this work is to develop a nonlinear programming (NLP) model and an optimization approach that aim at identifying the best values for intermediate temperatures, sub-stream flow rate fractions, heat loads and areas for a given heat exchanger network topology. The NLP model that minimizes the total annual cost of the network is constructed based on a stage-wise grid diagram representation. To improve the possibilities of obtaining global optimal designs, a two-phase stochastic multi-start optimization algorithm is utilized for the solution of the developed model. The effectiveness of the proposed optimization approach is illustrated with the optimization of two network designs proposed in the literature for two well-known benchmark problems. Results show that from the addressed base network topologies it is possible to achieve improved network designs, with redistributions in exchanger heat loads that lead to reductions in total annual costs. The results also show that the optimization of a given network design sometimes leads to structural simplifications and reductions in the total number of heat exchangers of the network, thereby exposing alternative viable network topologies initially not anticipated.
Modelling temperature dynamics of a district heating system in Naestved, Denmark-A case study
International Nuclear Information System (INIS)
Gabrielaitiene, Irina; Bohm, Benny; Sunden, Bengt
2007-01-01
Modelling the temperature dynamics of a district heating system is typically validated for a single pipe or a system with limited information about dynamic consumer behaviour. In the present work, time dependent consumer data from the Naestved district heating system was used to investigate the ability of modelling tools to represent the temperature profile distortion throughout an entire heating system network. The Naestved district heating subsystem was modelled by two approaches (the node method developed at the Technical University of Denmark and the software TERMIS), and these modelling results were compared with measured data. The results indicate that the discrepancies between the predicted and measured temperatures are pronounced for consumers located in pipelines at distant pipelines containing numerous bends and fittings. Additionally, it was found that representing the consumer behaviour on an annual average basis introduced a deviation between the predicted and the measured return temperatures at the heat source
Mixture models with entropy regularization for community detection in networks
Chang, Zhenhai; Yin, Xianjun; Jia, Caiyan; Wang, Xiaoyang
2018-04-01
Community detection is a key exploratory tool in network analysis and has received much attention in recent years. NMM (Newman's mixture model) is one of the best models for exploring a range of network structures including community structure, bipartite and core-periphery structures, etc. However, NMM needs to know the number of communities in advance. Therefore, in this study, we have proposed an entropy regularized mixture model (called EMM), which is capable of inferring the number of communities and identifying network structure contained in a network, simultaneously. In the model, by minimizing the entropy of mixing coefficients of NMM using EM (expectation-maximization) solution, the small clusters contained little information can be discarded step by step. The empirical study on both synthetic networks and real networks has shown that the proposed model EMM is superior to the state-of-the-art methods.
Conceptual and methodological biases in network models.
Lamm, Ehud
2009-10-01
Many natural and biological phenomena can be depicted as networks. Theoretical and empirical analyses of networks have become prevalent. I discuss theoretical biases involved in the delineation of biological networks. The network perspective is shown to dissolve the distinction between regulatory architecture and regulatory state, consistent with the theoretical impossibility of distinguishing a priori between "program" and "data." The evolutionary significance of the dynamics of trans-generational and interorganism regulatory networks is explored and implications are presented for understanding the evolution of the biological categories development-heredity, plasticity-evolvability, and epigenetic-genetic.
Directory of Open Access Journals (Sweden)
Mohammad Taghi Ameli
2012-01-01
Full Text Available Transmission Network Expansion Planning (TNEP is a basic part of power network planning that determines where, when and how many new transmission lines should be added to the network. So, the TNEP is an optimization problem in which the expansion purposes are optimized. Artificial Intelligence (AI tools such as Genetic Algorithm (GA, Simulated Annealing (SA, Tabu Search (TS and Artificial Neural Networks (ANNs are methods used for solving the TNEP problem. Today, by using the hybridization models of AI tools, we can solve the TNEP problem for large-scale systems, which shows the effectiveness of utilizing such models. In this paper, a new approach to the hybridization model of Probabilistic Neural Networks (PNNs and Harmony Search Algorithm (HSA was used to solve the TNEP problem. Finally, by considering the uncertain role of the load based on a scenario technique, this proposed model was tested on the Garver’s 6-bus network.
International Nuclear Information System (INIS)
Xia, Dunzhu; Kong, Lun; Hu, Yiwei; Ni, Peizhen
2015-01-01
We present a novel silicon microgyroscope (SMG) temperature prediction and control system in a narrow space. As the temperature of SMG is closely related to its drive mode frequency and driving voltage, a temperature prediction model can be established based on the BP neural network. The simulation results demonstrate that the established temperature prediction model can estimate the temperature in the range of −40 to 60 °C with an error of less than ±0.05 °C. Then, a temperature control system based on the combination of fuzzy logic controller and the increment PID control method is proposed. The simulation results prove that the Fuzzy-PID controller has a smaller steady state error, less rise time and better robustness than the PID controller. This is validated by experimental results that show the Fuzzy-PID control method can achieve high precision in keeping the SMG temperature stable at 55 °C with an error of less than 0.2 °C. The scale factor can be stabilized at 8.7 mV/°/s with a temperature coefficient of 33 ppm °C −1 . ZRO (zero rate output) instability is decreased from 1.10°/s (9.5 mV) to 0.08°/s (0.7 mV) when the temperature control system is implemented over an ambient temperature range of −40 to 60 °C. (paper)
Switching performance of OBS network model under prefetched real traffic
Huang, Zhenhua; Xu, Du; Lei, Wen
2005-11-01
Optical Burst Switching (OBS) [1] is now widely considered as an efficient switching technique in building the next generation optical Internet .So it's very important to precisely evaluate the performance of the OBS network model. The performance of the OBS network model is variable in different condition, but the most important thing is that how it works under real traffic load. In the traditional simulation models, uniform traffics are usually generated by simulation software to imitate the data source of the edge node in the OBS network model, and through which the performance of the OBS network is evaluated. Unfortunately, without being simulated by real traffic, the traditional simulation models have several problems and their results are doubtable. To deal with this problem, we present a new simulation model for analysis and performance evaluation of the OBS network, which uses prefetched IP traffic to be data source of the OBS network model. The prefetched IP traffic can be considered as real IP source of the OBS edge node and the OBS network model has the same clock rate with a real OBS system. So it's easy to conclude that this model is closer to the real OBS system than the traditional ones. The simulation results also indicate that this model is more accurate to evaluate the performance of the OBS network system and the results of this model are closer to the actual situation.
Modeling MAC layer for powerline communications networks
Hrasnica, Halid; Haidine, Abdelfatteh
2001-02-01
The usage of electrical power distribution networks for voice and data transmission, called Powerline Communications, becomes nowadays more and more attractive, particularly in the telecommunication access area. The most important reasons for that are the deregulation of the telecommunication market and a fact that the access networks are still property of former monopolistic companies. In this work, first we analyze a PLC network and system structure as well as a disturbance scenario in powerline networks. After that, we define a logical structure of the powerline MAC layer and propose the reservation MAC protocols for the usage in the PLC network which provides collision free data transmission. This makes possible better network utilization and realization of QoS guarantees which can make PLC networks competitive to other access technologies.
A material model for aluminium sheet forming at elevated temperatures
van den Boogaard, Antonius H.; Werkhoven, R.J.; Bolt, P.J.
2001-01-01
In order to accurately simulate the deep drawing or stretching of aluminum sheet at elevated temperatures, a model is required that incorporates the temperature and strain-rate dependency of the material. In this paper two models are compared: a phenomenological material model in which the
Intelligent Data Transfer for Multiple Sensor Networks over a Broad Temperature Range
Krasowski, Michael (Inventor)
2018-01-01
A sensor network may be configured to operate in extreme temperature environments. A sensor may be configured to generate a frequency carrier, and transmit the frequency carrier to a node. The node may be configured to amplitude modulate the frequency carrier, and transmit the amplitude modulated frequency carrier to a receiver.
He, L.; Arvidson, R. E.; O'Sullivan, J. A.
2018-04-01
We use a neural network (NN) approach to simultaneously retrieve surface single scattering albedos and temperature maps for CRISM data from 1.40 to 3.85 µm. It approximates the inverse of DISORT which simulates solar and emission radiative streams.
Design of a low temperature district heating network with supply recirculation
DEFF Research Database (Denmark)
Li, Hongwei; Dalla Rosa, Alessandro; Svendsen, Svend
2010-01-01
The focus on continuing improving building energy efficiency and reducing building energy consumption brings the key impetus for the development of the new generation district heating (DH) system. In the new generation DH network, the supply and return temperature are designed low in order to sig...... calculates the heat loss in the twin pipe as that in the single pipe. The influence of this simplification on the supply/return water temperature prediction was analyzed by solving the coupled differential energy equations.......-pass system starts to function. The aim of this paper is to investigate the influence of by-pass water on the network return temperature and introduce the concept of supply water recirculation into the network design so that the traditional by-pass system can be avoided. Instead of mixing the by-pass water......The focus on continuing improving building energy efficiency and reducing building energy consumption brings the key impetus for the development of the new generation district heating (DH) system. In the new generation DH network, the supply and return temperature are designed low in order...
Ripple-Spreading Network Model Optimization by Genetic Algorithm
Directory of Open Access Journals (Sweden)
Xiao-Bing Hu
2013-01-01
Full Text Available Small-world and scale-free properties are widely acknowledged in many real-world complex network systems, and many network models have been developed to capture these network properties. The ripple-spreading network model (RSNM is a newly reported complex network model, which is inspired by the natural ripple-spreading phenomenon on clam water surface. The RSNM exhibits good potential for describing both spatial and temporal features in the development of many real-world networks where the influence of a few local events spreads out through nodes and then largely determines the final network topology. However, the relationships between ripple-spreading related parameters (RSRPs of RSNM and small-world and scale-free topologies are not as obvious or straightforward as in many other network models. This paper attempts to apply genetic algorithm (GA to tune the values of RSRPs, so that the RSNM may generate these two most important network topologies. The study demonstrates that, once RSRPs are properly tuned by GA, the RSNM is capable of generating both network topologies and therefore has a great flexibility to study many real-world complex network systems.
A Temperature-Dependent Hysteresis Model for Relaxor Ferroelectric Compounds
National Research Council Canada - National Science Library
Raye, Julie K; Smith, Ralph C
2004-01-01
This paper summarizes the development of a homogenized free energy model which characterizes the temperature-dependent hysteresis and constitutive nonlinearities inherent to relaxor ferroelectric materials...
Modelling the impact of social network on energy savings
International Nuclear Information System (INIS)
Du, Feng; Zhang, Jiangfeng; Li, Hailong; Yan, Jinyue; Galloway, Stuart; Lo, Kwok L.
2016-01-01
Highlights: • Energy saving propagation along a social network is modelled. • This model consists of a time evolving weighted directed network. • Network weights and information decay are applied in savings calculation. - Abstract: It is noted that human behaviour changes can have a significant impact on energy consumption, however, qualitative study on such an impact is still very limited, and it is necessary to develop the corresponding mathematical models to describe how much energy savings can be achieved through human engagement. In this paper a mathematical model of human behavioural dynamic interactions on a social network is derived to calculate energy savings. This model consists of a weighted directed network with time evolving information on each node. Energy savings from the whole network is expressed as mathematical expectation from probability theory. This expected energy savings model includes both direct and indirect energy savings of individuals in the network. The savings model is obtained by network weights and modified by the decay of information. Expected energy savings are calculated for cases where individuals in the social network are treated as a single information source or multiple sources. This model is tested on a social network consisting of 40 people. The results show that the strength of relations between individuals is more important to information diffusion than the number of connections individuals have. The expected energy savings of optimally chosen node can be 25.32% more than randomly chosen nodes at the end of the second month for the case of single information source in the network, and 16.96% more than random nodes for the case of multiple information sources. This illustrates that the model presented in this paper can be used to determine which individuals will have the most influence on the social network, which in turn provides a useful guide to identify targeted customers in energy efficiency technology rollout
Petropoulos, George P; McCalmont, Jon P
2017-06-23
This paper describes a soil moisture dataset that has been collecting ground measurements of soil moisture, soil temperature and related parameters for west Wales, United Kingdom. Already acquired in situ data have been archived to the autonomous Wales Soil Moisture Network (WSMN) since its foundation in July 2011. The sites from which measurements are being collected represent a range of conditions typical of the Welsh environment, with climate ranging from oceanic to temperate and a range of the most typical land use/cover types found in Wales. At present, WSMN consists of a total of nine monitoring sites across the area with a concentration of sites in three sub-areas around the region of Aberystwyth located in Mid-Wales. The dataset of composed of 0-5 (or 0-10) cm soil moisture, soil temperature, precipitation, and other ancillary data. WSMN data are provided openly to the public via the International Soil Moisture Network (ISMN) platform. At present, WSMN is also rapidly expanding thanks to funding obtained recently which allows more monitoring sites to be added to the network to the wider community interested in using its data.
Hybrid neural network bushing model for vehicle dynamics simulation
International Nuclear Information System (INIS)
Sohn, Jeong Hyun; Lee, Seung Kyu; Yoo, Wan Suk
2008-01-01
Although the linear model was widely used for the bushing model in vehicle suspension systems, it could not express the nonlinear characteristics of bushing in terms of the amplitude and the frequency. An artificial neural network model was suggested to consider the hysteretic responses of bushings. This model, however, often diverges due to the uncertainties of the neural network under the unexpected excitation inputs. In this paper, a hybrid neural network bushing model combining linear and neural network is suggested. A linear model was employed to represent linear stiffness and damping effects, and the artificial neural network algorithm was adopted to take into account the hysteretic responses. A rubber test was performed to capture bushing characteristics, where sine excitation with different frequencies and amplitudes is applied. Random test results were used to update the weighting factors of the neural network model. It is proven that the proposed model has more robust characteristics than a simple neural network model under step excitation input. A full car simulation was carried out to verify the proposed bushing models. It was shown that the hybrid model results are almost identical to the linear model under several maneuvers
An economic model of friendship and enmity for measuring social balance in networks
Lee, Kyu-Min; Shin, Euncheol; You, Seungil
2017-12-01
We propose a dynamic economic model of networks where agents can be friends or enemies with one another. This is a decentralized relationship model in that agents decide whether to change their relationships so as to minimize their imbalanced triads. In this model, there is a single parameter, which we call social temperature, that captures the degree to which agents care about social balance in their relationships. We show that the global structure of relationship configuration converges to a unique stationary distribution. Using this stationary distribution, we characterize the maximum likelihood estimator of the social temperature parameter. Since the estimator is computationally challenging to calculate from real social network datasets, we provide a simple simulation algorithm and verify its performance with real social network datasets.
Model for the growth of the world airline network
Verma, T.; Araújo, N. A. M.; Nagler, J.; Andrade, J. S.; Herrmann, H. J.
2016-06-01
We propose a probabilistic growth model for transport networks which employs a balance between popularity of nodes and the physical distance between nodes. By comparing the degree of each node in the model network and the World Airline Network (WAN), we observe that the difference between the two is minimized for α≈2. Interestingly, this is the value obtained for the node-node correlation function in the WAN. This suggests that our model explains quite well the growth of airline networks.
Modelling the dependability in Network Function Virtualisation
Lin, Wenqi
2017-01-01
Network Function Virtualization has been brought up to allow the TSPs to have more possibilities and flexibilities to provision services with better load optimizing, energy utilizing and dynamic scaling. Network functions will be decoupled from the underlying dedicated hardware into software instances that run on commercial off-the-shelf servers. However, the development is still at an early stage and the dependability concerns raise by the virtualization of the network functions are touched ...
Mode Choice Modeling Using Artificial Neural Networks
Edara, Praveen Kumar
2003-01-01
Artificial intelligence techniques have produced excellent results in many diverse fields of engineering. Techniques such as neural networks and fuzzy systems have found their way into transportation engineering. In recent years, neural networks are being used instead of regression techniques for travel demand forecasting purposes. The basic reason lies in the fact that neural networks are able to capture complex relationships and learn from examples and also able to adapt when new data becom...
Bayesian Networks for Modeling Dredging Decisions
2011-10-01
years, that algorithms have been developed to solve these problems efficiently. Most modern Bayesian network software uses junction tree (a.k.a. join... software was used to develop the network . This is by no means an exhaustive list of Bayesian network applications, but it is representative of recent...characteristic node (SCN), state- defining node ( SDN ), effect node (EFN), or value node. The five types of nodes can be described as follows: ERDC/EL TR-11
A genetic algorithm for solving supply chain network design model
Firoozi, Z.; Ismail, N.; Ariafar, S. H.; Tang, S. H.; Ariffin, M. K. M. A.
2013-09-01
Network design is by nature costly and optimization models play significant role in reducing the unnecessary cost components of a distribution network. This study proposes a genetic algorithm to solve a distribution network design model. The structure of the chromosome in the proposed algorithm is defined in a novel way that in addition to producing feasible solutions, it also reduces the computational complexity of the algorithm. Computational results are presented to show the algorithm performance.
A soil moisture and temperature network for SMOS validation in Western Denmark
DEFF Research Database (Denmark)
Bircher, Simone; Skou, Niels; Jensen, K. H.
2011-01-01
The Soil Moisture and Ocean Salinity Mission (SMOS) acquires surface soil moisture data globally, and thus product validation for a range of climate and environmental conditions across continents is a crucial step. For this purpose, a soil moisture and temperature network of Decagon ECH2O 5TE...... SMOS pixel (44 × 44 km), which is representative of the land surface conditions of the catchment and with minimal impact from open water (2) arrangement of three network clusters along the precipitation gradient, and (3) distribution of the stations according to respective fractions of classes...... representing the prevailing environmental conditions. Overall, measured moisture and temperature patterns could be related to the respective land cover and soil conditions. Texture-dependency of the 0–5 cm soil moisture measurements was demonstrated. Regional differences in 0–5 cm soil moisture, temperature...
Runoff Modelling in Urban Storm Drainage by Neural Networks
DEFF Research Database (Denmark)
Rasmussen, Michael R.; Brorsen, Michael; Schaarup-Jensen, Kjeld
1995-01-01
A neural network is used to simulate folw and water levels in a sewer system. The calibration of th neural network is based on a few measured events and the network is validated against measureed events as well as flow simulated with the MOUSE model (Lindberg and Joergensen, 1986). The neural...... network is used to compute flow or water level at selected points in the sewer system, and to forecast the flow from a small residential area. The main advantages of the neural network are the build-in self calibration procedure and high speed performance, but the neural network cannot be used to extract...... knowledge of the runoff process. The neural network was found to simulate 150 times faster than e.g. the MOUSE model....
Multi-temperature state-dependent equivalent circuit discharge model for lithium-sulfur batteries
DEFF Research Database (Denmark)
Propp, Karsten; Marinescu, Monica; Auger, Daniel J.
2016-01-01
Lithium-sulfur (Li-S) batteries are described extensively in the literature, but existing computational models aimed at scientific understanding are too complex for use in applications such as battery management. Computationally simple models are vital for exploitation. This paper proposes a non......-linear state-of-charge dependent Li-S equivalent circuit network (ECN) model for a Li-S cell under discharge. Li-S batteries are fundamentally different to Li-ion batteries, and require chemistry-specific models. A new Li-S model is obtained using a ‘behavioural’ interpretation of the ECN model; as Li...... pulse profile at four temperatures from 10 °C to 50 °C, giving linearized ECN parameters for a range of states-of-charge, currents and temperatures. These are used to create a nonlinear polynomial-based battery model suitable for use in a battery management system. When the model is used to predict...
MODELS OF HOURLY DRY BULB TEMPERATURE AND ...
African Journals Online (AJOL)
Hourly meteorological data of both dry bulb temperature and relative humidity for 18 locations in Nigeria for the period 1995 to 2009 were analysed to obtain the mean monthly average and monthly hourly average of each of the two meteorological variables for each month for each location. The difference between the ...
Modelling of tandem cell temperature coefficients
Energy Technology Data Exchange (ETDEWEB)
Friedman, D.J. [National Renewable Energy Lab., Golden, CO (United States)
1996-05-01
This paper discusses the temperature dependence of the basic solar-cell operating parameters for a GaInP/GaAs series-connected two-terminal tandem cell. The effects of series resistance and of different incident solar spectra are also discussed.
Model of community emergence in weighted social networks
Kumpula, J. M.; Onnela, J.-P.; Saramäki, J.; Kertész, J.; Kaski, K.
2009-04-01
Over the years network theory has proven to be rapidly expanding methodology to investigate various complex systems and it has turned out to give quite unparalleled insight to their structure, function, and response through data analysis, modeling, and simulation. For social systems in particular the network approach has empirically revealed a modular structure due to interplay between the network topology and link weights between network nodes or individuals. This inspired us to develop a simple network model that could catch some salient features of mesoscopic community and macroscopic topology formation during network evolution. Our model is based on two fundamental mechanisms of network sociology for individuals to find new friends, namely cyclic closure and focal closure, which are mimicked by local search-link-reinforcement and random global attachment mechanisms, respectively. In addition we included to the model a node deletion mechanism by removing all its links simultaneously, which corresponds for an individual to depart from the network. Here we describe in detail the implementation of our model algorithm, which was found to be computationally efficient and produce many empirically observed features of large-scale social networks. Thus this model opens a new perspective for studying such collective social phenomena as spreading, structure formation, and evolutionary processes.
The influence of cold temperature on cellular excitability of hippocampal networks.
de la Peña, Elvira; Mälkiä, Annika; Vara, Hugo; Caires, Rebeca; Ballesta, Juan J; Belmonte, Carlos; Viana, Felix
2012-01-01
The hippocampus plays an important role in short term memory, learning and spatial navigation. A characteristic feature of the hippocampal region is its expression of different electrical population rhythms and activities during different brain states. Physiological fluctuations in brain temperature affect the activity patterns in hippocampus, but the underlying cellular mechanisms are poorly understood. In this work, we investigated the thermal modulation of hippocampal activity at the cellular network level. Primary cell cultures of mouse E17 hippocampus displayed robust network activation upon light cooling of the extracellular solution from baseline physiological temperatures. The activity generated was dependent on action potential firing and excitatory glutamatergic synaptic transmission. Involvement of thermosensitive channels from the transient receptor potential (TRP) family in network activation by temperature changes was ruled out, whereas pharmacological and immunochemical experiments strongly pointed towards the involvement of temperature-sensitive two-pore-domain potassium channels (K(2P)), TREK/TRAAK family. In hippocampal slices we could show an increase in evoked and spontaneous synaptic activity produced by mild cooling in the physiological range that was prevented by chloroform, a K(2P) channel opener. We propose that cold-induced closure of background TREK/TRAAK family channels increases the excitability of some hippocampal neurons, acting as a temperature-sensitive gate of network activation. Our findings in the hippocampus open the possibility that small temperature variations in the brain in vivo, associated with metabolism or blood flow oscillations, act as a switch mechanism of neuronal activity and determination of firing patterns through regulation of thermosensitive background potassium channel activity.
A control model for district heating networks with storage
Scholten, Tjeert; De Persis, Claudio; Tesi, Pietro
2014-01-01
In [1] pressure control of hydraulic networks is investigated. We extend this work to district heating systems with storage capabilities and derive a model taking the topology of the network into account. The goal for the derived model is that it should allow for control of the storage level and
Travel Time Reliability for Urban Networks : Modelling and Empirics
Zheng, F.; Liu, Xiaobo; van Zuylen, H.J.; Li, Jie; Lu, Chao
2017-01-01
The importance of travel time reliability in traffic management, control, and network design has received a lot of attention in the past decade. In this paper, a network travel time distribution model based on the Johnson curve system is proposed. The model is applied to field travel time data
Deterministic ripple-spreading model for complex networks.
Hu, Xiao-Bing; Wang, Ming; Leeson, Mark S; Hines, Evor L; Di Paolo, Ezequiel
2011-04-01
This paper proposes a deterministic complex network model, which is inspired by the natural ripple-spreading phenomenon. The motivations and main advantages of the model are the following: (i) The establishment of many real-world networks is a dynamic process, where it is often observed that the influence of a few local events spreads out through nodes, and then largely determines the final network topology. Obviously, this dynamic process involves many spatial and temporal factors. By simulating the natural ripple-spreading process, this paper reports a very natural way to set up a spatial and temporal model for such complex networks. (ii) Existing relevant network models are all stochastic models, i.e., with a given input, they cannot output a unique topology. Differently, the proposed ripple-spreading model can uniquely determine the final network topology, and at the same time, the stochastic feature of complex networks is captured by randomly initializing ripple-spreading related parameters. (iii) The proposed model can use an easily manageable number of ripple-spreading related parameters to precisely describe a network topology, which is more memory efficient when compared with traditional adjacency matrix or similar memory-expensive data structures. (iv) The ripple-spreading model has a very good potential for both extensions and applications.
Real time thermal hydraulic model for high temperature gas-cooled reactor core
International Nuclear Information System (INIS)
Sui Zhe; Sun Jun; Ma Yuanle; Zhang Ruipeng
2013-01-01
A real-time thermal hydraulic model of the reactor core was described and integrated into the simulation system for the high temperature gas-cooled pebble bed reactor nuclear power plant, which was developed in the vPower platform, a new simulation environment for nuclear and fossil power plants. In the thermal hydraulic model, the helium flow paths were established by the flow network tools in order to obtain the flow rates and pressure distributions. Meanwhile, the heat structures, representing all the solid heat transfer elements in the pebble bed, graphite reflectors and carbon bricks, were connected by the heat transfer network in order to solve the temperature distributions in the reactor core. The flow network and heat transfer network were coupled and calculated in real time. Two steady states (100% and 50% full power) and two transients (inlet temperature step and flow step) were tested that the quantitative comparisons of the steady results with design data and qualitative analysis of the transients showed the good applicability of the present thermal hydraulic model. (authors)
A concise wall temperature model for DI Diesel engines
Energy Technology Data Exchange (ETDEWEB)
Torregrosa, A.; Olmeda, P.; Degraeuwe, B. [CMT-Motores Termicos, Universidad Politecnica de Valencia (Spain); Reyes, M. [Centro de Mecanica de Fluidos y Aplicaciones, Universidad Simon Bolivar (Venezuela)
2006-08-15
A concise resistor model for wall temperature prediction in diesel engines with piston cooling is presented here. The model uses the instantaneous in-cylinder pressure and some usually measured operational parameters to predict the temperature of the structural elements of the engine. The resistor model was adjusted by means of temperature measurements in the cylinder head, the liner and the piston. For each model parameter, an expression as a function of the engine geometry, operational parameters and material properties was derived to make the model applicable to other similar engines. The model predicts well the cylinder head, liner and piston temperature and is sensitive to variations of operational parameters such as the start of injection, coolant and oil temperature and engine speed and load. (author)
Mathematical modelling of complex contagion on clustered networks
O'sullivan, David J.; O'Keeffe, Gary; Fennell, Peter; Gleeson, James
2015-09-01
The spreading of behavior, such as the adoption of a new innovation, is influenced bythe structure of social networks that interconnect the population. In the experiments of Centola (Science, 2010), adoption of new behavior was shown to spread further and faster across clustered-lattice networks than across corresponding random networks. This implies that the “complex contagion” effects of social reinforcement are important in such diffusion, in contrast to “simple” contagion models of disease-spread which predict that epidemics would grow more efficiently on random networks than on clustered networks. To accurately model complex contagion on clustered networks remains a challenge because the usual assumptions (e.g. of mean-field theory) regarding tree-like networks are invalidated by the presence of triangles in the network; the triangles are, however, crucial to the social reinforcement mechanism, which posits an increased probability of a person adopting behavior that has been adopted by two or more neighbors. In this paper we modify the analytical approach that was introduced by Hebert-Dufresne et al. (Phys. Rev. E, 2010), to study disease-spread on clustered networks. We show how the approximation method can be adapted to a complex contagion model, and confirm the accuracy of the method with numerical simulations. The analytical results of the model enable us to quantify the level of social reinforcement that is required to observe—as in Centola’s experiments—faster diffusion on clustered topologies than on random networks.
Mathematical modelling of complex contagion on clustered networks
Directory of Open Access Journals (Sweden)
David J. P. O'Sullivan
2015-09-01
Full Text Available The spreading of behavior, such as the adoption of a new innovation, is influenced bythe structure of social networks that interconnect the population. In the experiments of Centola (Science, 2010, adoption of new behavior was shown to spread further and faster across clustered-lattice networks than across corresponding random networks. This implies that the complex contagion effects of social reinforcement are important in such diffusion, in contrast to simple contagion models of disease-spread which predict that epidemics would grow more efficiently on random networks than on clustered networks. To accurately model complex contagion on clustered networks remains a challenge because the usual assumptions (e.g. of mean-field theory regarding tree-like networks are invalidated by the presence of triangles in the network; the triangles are, however, crucial to the social reinforcement mechanism, which posits an increased probability of a person adopting behavior that has been adopted by two or more neighbors. In this paper we modify the analytical approach that was introduced by Hebert-Dufresne et al. (Phys. Rev. E, 2010, to study disease-spread on clustered networks. We show how the approximation method can be adapted to a complex contagion model, and confirm the accuracy of the method with numerical simulations. The analytical results of the model enable us to quantify the level of social reinforcement that is required to observe—as in Centola’s experiments—faster diffusion on clustered topologies than on random networks.
A small-world network model of facial emotion recognition.
Takehara, Takuma; Ochiai, Fumio; Suzuki, Naoto
2016-01-01
Various models have been proposed to increase understanding of the cognitive basis of facial emotions. Despite those efforts, interactions between facial emotions have received minimal attention. If collective behaviours relating to each facial emotion in the comprehensive cognitive system could be assumed, specific facial emotion relationship patterns might emerge. In this study, we demonstrate that the frameworks of complex networks can effectively capture those patterns. We generate 81 facial emotion images (6 prototypes and 75 morphs) and then ask participants to rate degrees of similarity in 3240 facial emotion pairs in a paired comparison task. A facial emotion network constructed on the basis of similarity clearly forms a small-world network, which features an extremely short average network distance and close connectivity. Further, even if two facial emotions have opposing valences, they are connected within only two steps. In addition, we show that intermediary morphs are crucial for maintaining full network integration, whereas prototypes are not at all important. These results suggest the existence of collective behaviours in the cognitive systems of facial emotions and also describe why people can efficiently recognize facial emotions in terms of information transmission and propagation. For comparison, we construct three simulated networks--one based on the categorical model, one based on the dimensional model, and one random network. The results reveal that small-world connectivity in facial emotion networks is apparently different from those networks, suggesting that a small-world network is the most suitable model for capturing the cognitive basis of facial emotions.
A general evolving model for growing bipartite networks
International Nuclear Information System (INIS)
Tian, Lixin; He, Yinghuan; Liu, Haijun; Du, Ruijin
2012-01-01
In this Letter, we propose and study an inner evolving bipartite network model. Significantly, we prove that the degree distribution of two different kinds of nodes both obey power-law form with adjustable exponents. Furthermore, the joint degree distribution of any two nodes for bipartite networks model is calculated analytically by the mean-field method. The result displays that such bipartite networks are nearly uncorrelated networks, which is different from one-mode networks. Numerical simulations and empirical results are given to verify the theoretical results. -- Highlights: ► We proposed a general evolving bipartite network model which was based on priority connection, reconnection and breaking edges. ► We prove that the degree distribution of two different kinds of nodes both obey power-law form with adjustable exponents. ► The joint degree distribution of any two nodes for bipartite networks model is calculated analytically by the mean-field method. ► The result displays that such bipartite networks are nearly uncorrelated networks, which is different from one-mode networks.
Modeling geomagnetic induced currents in Australian power networks
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.
Systems and methods for modeling and analyzing networks
Hill, Colin C; Church, Bruce W; McDonagh, Paul D; Khalil, Iya G; Neyarapally, Thomas A; Pitluk, Zachary W
2013-10-29
The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.
Modeling of Temperature Effect on Modal Frequency of Concrete Beam Based on Field Monitoring Data
Directory of Open Access Journals (Sweden)
Wenchen Shan
2018-01-01
Full Text Available Temperature variation has been widely demonstrated to produce significant effect on modal frequencies that even exceed the effect of actual damage. In order to eliminate the temperature effect on modal frequency, an effective method is to construct quantitative models which accurately predict the modal frequency corresponding to temperature variation. In this paper, principal component analysis (PCA is conducted on the temperatures taken from all embedded thermocouples for extracting input parameters of regression models. Three regression-based numerical models using multiple linear regression (MLR, back-propagation neural network (BPNN, and support vector regression (SVR techniques are constructed to capture the relationships between modal frequencies and temperature distributions from measurements of a concrete beam during a period of forty days of monitoring. A comparison with respect to the performance of various optimally configured regression models has been performed on measurement data. Results indicate that the SVR exhibits a better reproduction and prediction capability than BPNN and MLR models for predicting the modal frequencies with respect to nonuniformly distributed temperatures. It is succeeded that temperature effects on modal frequencies can be effectively eliminated based on the optimally formulated SVR model.
An equivalent circuit model of supercapacitors for applications in wireless sensor networks
Yang, Hengzhao; Zhang, Ying
2011-04-01
Energy harvesting technologies have been extensively researched to develop long-lived wireless sensor networks. To better utilize the harvested energy, various energy storage systems are proposed. A simple circuit model is developed to describe supercapacitor behavior, which uses two resistor-capacitor branches with different time constants to characterize the charging and redistribution processes, and a variable leakage resistance (VLR) to characterize the self-discharge process. The voltage and temperature dependence of the VLR values is also discussed. Results show that the VLR model is more accurate than the energy recursive equation (ERE) models for short term wireless sensor network applications.
A model for phosphate glass topology considering the modifying ion sub-network
DEFF Research Database (Denmark)
Hermansen, Christian; Mauro, J.C.; Yue, Yuanzheng
2014-01-01
In the present paper we establish a temperature dependent constraint model of alkali phosphate glasses considering the structural and topological role of the modifying ion sub-network constituted by alkali ions and their non-bonding oxygen coordination spheres. The model is consistent with availa......In the present paper we establish a temperature dependent constraint model of alkali phosphate glasses considering the structural and topological role of the modifying ion sub-network constituted by alkali ions and their non-bonding oxygen coordination spheres. The model is consistent...... with available structural data by NMR and molecular dynamics simulation and dynamic data such glass transition temperature (Tg) and liquid fragility (m). Alkali phosphate glasses are exemplary systems for developing constraint model since the modifying cation network plays an important role besides the primary...... phosphate network. The proposed topological model predicts the changing trend of the Tg and m with increasing alkali oxide content for alkali phosphate glasses, including an anomalous minimum at around 20 mol% alkali oxide content. We find that the minimum in Tg and m is caused by increased connectivity...
Ocean wave prediction using numerical and neural network models
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Prabaharan, N.
This paper presents an overview of the development of the numerical wave prediction models and recently used neural networks for ocean wave hindcasting and forecasting. The numerical wave models express the physical concepts of the phenomena...
Dynamic thermo-hydraulic model of district cooling networks
International Nuclear Information System (INIS)
Oppelt, Thomas; Urbaneck, Thorsten; Gross, Ulrich; Platzer, Bernd
2016-01-01
Highlights: • A dynamic thermo-hydraulic model for district cooling networks is presented. • The thermal modelling is based on water segment tracking (Lagrangian approach). • Thus, numerical errors and balance inaccuracies are avoided. • Verification and validation studies proved the reliability of the model. - Abstract: In the present paper, the dynamic thermo-hydraulic model ISENA is presented which can be applied for answering different questions occurring in design and operation of district cooling networks—e.g. related to economic and energy efficiency. The network model consists of a quasistatic hydraulic model and a transient thermal model based on tracking water segments through the whole network (Lagrangian method). Applying this approach, numerical errors and balance inaccuracies can be avoided which leads to a higher quality of results compared to other network models. Verification and validation calculations are presented in order to show that ISENA provides reliable results and is suitable for practical application.
Directory of Open Access Journals (Sweden)
S. I Saedi
2018-03-01
Full Text Available Introduction Global solar radiation is the sum of direct, diffuse, and reflected solar radiation. Weather forecasts, agricultural practices, and solar equipment development are three major fields that need proper information about solar radiation. Furthermore, sun in regarded as a huge source of renewable and clean energy which can be used in numerous applications to get rid of environmental impacts of non-renewable fossil fuels. Therefore, easy and fast estimation of daily global solar radiation would play an effective role is these affairs. Materials and Methods This study aimed at predicting the daily global solar radiation by means of artificial neural network (ANN method, based on easy-to-gain weather data i.e. daily mean, minimum and maximum temperatures. Having a variety of climates with long-term valid weather data, Washington State, located at the northwestern part of USA was chosen for this purpose. It has a total number of 19 weather stations to cover all the State climates. First, a station with the largest number of valid historical weather data (Lind was chosen to develop, validate, and test different ANN models. Three training algorithms i.e. Levenberg – Marquardt (LM, Scaled Conjugate Gradient (SCG, and Bayesian regularization (BR were tested in one and two hidden layer networks each with up to 20 neurons to derive six best architectures. R, RMSE, MAPE, and scatter plots were considered to evaluate each network in all steps. In order to investigate the generalizability of the best six models, they were tested in other Washington State weather stations. The most accurate and general models was evaluated in an Iran sample weather station which was chosen to be Mashhad. Results and Discussion The variation of MSE for the three training functions in one hidden layer models for Lind station indicated that SCG converged weights and biases in shorter time than LM, and LM did that faster than BR. It means that SCG provided the fastest
Dynamic Pathloss Model for Future Mobile Communication Networks
DEFF Research Database (Denmark)
Kumar, Ambuj; Mihovska, Albena Dimitrova; Prasad, Ramjee
2016-01-01
that are essentially static. Therefore, once the signal level drops beyond the predicted values due to any variance in the environmental conditions, very crowded areas may not be catered well enough by the deployed network that had been designed with the static path loss model. This paper proposes an approach......— Future mobile communication networks (MCNs) are expected to be more intelligent and proactive based on new capabilities that increase agility and performance. However, for any successful mobile network service, the dexterity in network deployment is a key factor. The efficiency of the network...... planning depends on how congruent the chosen path loss model and real propagation are. Various path loss models have been developed that predict the signal propagation in various morphological and climatic environments; however they consider only those physical parameters of the network environment...
Dynamic Pathloss Model for Place and Time Itinerant Networks
DEFF Research Database (Denmark)
Kumar, Ambuj; Mihovska, Albena; Prasad, Ramjee
2018-01-01
that are essentially static. Therefore, once the signal level drops beyond the predicted values due to any variance in the environmental conditions, very crowded areas may not be catered well enough by the deployed network that had been designed with the static path loss model. This paper proposes an approach......t Future mobile communication networks are expected to be more intelligent and proactive based on new capabilities that increase agility and performance. However, for any successful mobile network service, the dexterity in network deployment is a key factor. The efficiency of the network planning...... depends on how congruent the chosen path loss model and real propagation are. Various path loss models have been developed that predict the signal propagation in various morphological and climatic environments; however they consider only those physical parameters of the network environment...
An information spreading model based on online social networks
Wang, Tao; He, Juanjuan; Wang, Xiaoxia
2018-01-01
Online social platforms are very popular in recent years. In addition to spreading information, users could review or collect information on online social platforms. According to the information spreading rules of online social network, a new information spreading model, namely IRCSS model, is proposed in this paper. It includes sharing mechanism, reviewing mechanism, collecting mechanism and stifling mechanism. Mean-field equations are derived to describe the dynamics of the IRCSS model. Moreover, the steady states of reviewers, collectors and stiflers and the effects of parameters on the peak values of reviewers, collectors and sharers are analyzed. Finally, numerical simulations are performed on different networks. Results show that collecting mechanism and reviewing mechanism, as well as the connectivity of the network, make information travel wider and faster, and compared to WS network and ER network, the speed of reviewing, sharing and collecting information is fastest on BA network.
Modeling of the Temperature Field Recovery in the Oil Pool
Khabibullin, I. L.; Davtetbaev, A. Ya.; Mar'in, D. F.; Khisamov, A. A.
2018-05-01
This paper considers the problem on mathematical modeling of the temperature field recovery in the oil pool upon termination of injection of water into the pool. The problem is broken down into two stages: injection of water and temperature and pressure recovery upon termination of injection. A review of the existing mathematical models is presented, analytical solutions for a number of cases have been constructed, and a comparison of the analytical solutions of different models has been made. In the general form, the expression has been obtained that permits determining the temperature change in the oil pool upon termination of injection of water (recovery of the temperature field).
A Mathematical Model to Improve the Performance of Logistics Network
Directory of Open Access Journals (Sweden)
Muhammad Izman Herdiansyah
2012-01-01
Full Text Available The role of logistics nowadays is expanding from just providing transportation and warehousing to offering total integrated logistics. To remain competitive in the global market environment, business enterprises need to improve their logistics operations performance. The improvement will be achieved when we can provide a comprehensive analysis and optimize its network performances. In this paper, a mixed integer linier model for optimizing logistics network performance is developed. It provides a single-product multi-period multi-facilities model, as well as the multi-product concept. The problem is modeled in form of a network flow problem with the main objective to minimize total logistics cost. The problem can be solved using commercial linear programming package like CPLEX or LINDO. Even in small case, the solver in Excel may also be used to solve such model.Keywords: logistics network, integrated model, mathematical programming, network optimization
A Network Contention Model for the Extreme-scale Simulator
Energy Technology Data Exchange (ETDEWEB)
Engelmann, Christian [ORNL; Naughton III, Thomas J [ORNL
2015-01-01
The Extreme-scale Simulator (xSim) is a performance investigation toolkit for high-performance computing (HPC) hardware/software co-design. It permits running a HPC application with millions of concurrent execution threads, while observing its performance in a simulated extreme-scale system. This paper details a newly developed network modeling feature for xSim, eliminating the shortcomings of the existing network modeling capabilities. The approach takes a different path for implementing network contention and bandwidth capacity modeling using a less synchronous and accurate enough model design. With the new network modeling feature, xSim is able to simulate on-chip and on-node networks with reasonable accuracy and overheads.
Efficient Neural Network Modeling for Flight and Space Dynamics Simulation
Directory of Open Access Journals (Sweden)
Ayman Hamdy Kassem
2011-01-01
Full Text Available This paper represents an efficient technique for neural network modeling of flight and space dynamics simulation. The technique will free the neural network designer from guessing the size and structure for the required neural network model and will help to minimize the number of neurons. For linear flight/space dynamics systems, the technique can find the network weights and biases directly by solving a system of linear equations without the need for training. Nonlinear flight dynamic systems can be easily modeled by training its linearized models keeping the same network structure. The training is fast, as it uses the linear system knowledge to speed up the training process. The technique is tested on different flight/space dynamic models and showed promising results.
Foreground removal from WMAP 5 yr temperature maps using an MLP neural network
Nørgaard-Nielsen, H. U.
2010-09-01
Aims: One of the main obstacles for extracting the cosmic microwave background (CMB) signal from observations in the mm/sub-mm range is the foreground contamination by emission from Galactic component: mainly synchrotron, free-free, and thermal dust emission. The statistical nature of the intrinsic CMB signal makes it essential to minimize the systematic errors in the CMB temperature determinations. Methods: The feasibility of using simple neural networks to extract the CMB signal from detailed simulated data has already been demonstrated. Here, simple neural networks are applied to the WMAP 5 yr temperature data without using any auxiliary data. Results: A simple multilayer perceptron neural network with two hidden layers provides temperature estimates over more than 75 per cent of the sky with random errors significantly below those previously extracted from these data. Also, the systematic errors, i.e. errors correlated with the Galactic foregrounds, are very small. Conclusions: With these results the neural network method is well prepared for dealing with the high - quality CMB data from the ESA Planck Surveyor satellite. unknown author type, collab
New Temperature-based Models for Predicting Global Solar Radiation
International Nuclear Information System (INIS)
Hassan, Gasser E.; Youssef, M. Elsayed; Mohamed, Zahraa E.; Ali, Mohamed A.; Hanafy, Ahmed A.
2016-01-01
Highlights: • New temperature-based models for estimating solar radiation are investigated. • The models are validated against 20-years measured data of global solar radiation. • The new temperature-based model shows the best performance for coastal sites. • The new temperature-based model is more accurate than the sunshine-based models. • The new model is highly applicable with weather temperature forecast techniques. - Abstract: This study presents new ambient-temperature-based models for estimating global solar radiation as alternatives to the widely used sunshine-based models owing to the unavailability of sunshine data at all locations around the world. Seventeen new temperature-based models are established, validated and compared with other three models proposed in the literature (the Annandale, Allen and Goodin models) to estimate the monthly average daily global solar radiation on a horizontal surface. These models are developed using a 20-year measured dataset of global solar radiation for the case study location (Lat. 30°51′N and long. 29°34′E), and then, the general formulae of the newly suggested models are examined for ten different locations around Egypt. Moreover, the local formulae for the models are established and validated for two coastal locations where the general formulae give inaccurate predictions. Mostly common statistical errors are utilized to evaluate the performance of these models and identify the most accurate model. The obtained results show that the local formula for the most accurate new model provides good predictions for global solar radiation at different locations, especially at coastal sites. Moreover, the local and general formulas of the most accurate temperature-based model also perform better than the two most accurate sunshine-based models from the literature. The quick and accurate estimations of the global solar radiation using this approach can be employed in the design and evaluation of performance for
Model and simulation of Krause model in dynamic open network
Zhu, Meixia; Xie, Guangqiang
2017-08-01
The construction of the concept of evolution is an effective way to reveal the formation of group consensus. This study is based on the modeling paradigm of the HK model (Hegsekmann-Krause). This paper analyzes the evolution of multi - agent opinion in dynamic open networks with member mobility. The results of the simulation show that when the number of agents is constant, the interval distribution of the initial distribution will affect the number of the final view, The greater the distribution of opinions, the more the number of views formed eventually; The trust threshold has a decisive effect on the number of views, and there is a negative correlation between the trust threshold and the number of opinions clusters. The higher the connectivity of the initial activity group, the more easily the subjective opinion in the evolution of opinion to achieve rapid convergence. The more open the network is more conducive to the unity of view, increase and reduce the number of agents will not affect the consistency of the group effect, but not conducive to stability.
Stability of the Hartree-Fock model with temperature
Dolbeault, Jean; Felmer, Patricio; Lewin, Mathieu
2008-01-01
This paper is devoted to the Hartree-Fock model with temperature in the euclidean space. For large classes of free energy functionals, minimizers are obtained as long as the total charge of the system does not exceed a threshold which depends on the temperature. The usual Hartree-Fock model is recovered in the zero temperature limit. An orbital stability result for the Cauchy problem is deduced from the variational approach.
A comprehensive multi-local-world model for complex networks
International Nuclear Information System (INIS)
Fan Zhengping; Chen Guanrong; Zhang Yunong
2009-01-01
The nodes in a community within a network are much more connected to each other than to the others outside the community in the same network. This phenomenon has been commonly observed from many real-world networks, ranging from social to biological even to technical networks. Meanwhile, the number of communities in some real-world networks, such as the Internet and most social networks, are evolving with time. To model this kind of networks, the present Letter proposes a multi-local-world (MLW) model to capture and describe their essential topological properties. Based on the mean-field theory, the degree distribution of this model is obtained analytically, showing that the generated network has a novel topological feature as being not completely random nor completely scale-free but behaving somewhere between them. As a typical application, the MLW model is applied to characterize the Internet against some other models such as the BA, GBA, Fitness and HOT models, demonstrating the superiority of the new model.
A physically based analytical spatial air temperature and humidity model
Yang Yang; Theodore A. Endreny; David J. Nowak
2013-01-01
Spatial variation of urban surface air temperature and humidity influences human thermal comfort, the settling rate of atmospheric pollutants, and plant physiology and growth. Given the lack of observations, we developed a Physically based Analytical Spatial Air Temperature and Humidity (PASATH) model. The PASATH model calculates spatial solar radiation and heat...
A mathematical model for transducer working at high temperature
International Nuclear Information System (INIS)
Fabre, J.P.
1974-01-01
A mathematical model is proposed for a lithium niobate piezoelectric transducer working at high temperature in liquid sodium. The model proposed suitably described the operation of the high temperature transducer presented; it allows the optimization of the efficiency and band-pass [fr
Modeling, Prediction, and Control of Heating Temperature for Tube Billet
Directory of Open Access Journals (Sweden)
Yachun Mao
2015-01-01
Full Text Available Annular furnaces have multivariate, nonlinear, large time lag, and cross coupling characteristics. The prediction and control of the exit temperature of a tube billet are important but difficult. We establish a prediction model for the final temperature of a tube billet through OS-ELM-DRPLS method. We address the complex production characteristics, integrate the advantages of PLS and ELM algorithms in establishing linear and nonlinear models, and consider model update and data lag. Based on the proposed model, we design a prediction control algorithm for tube billet temperature. The algorithm is validated using the practical production data of Baosteel Co., Ltd. Results show that the model achieves the precision required in industrial applications. The temperature of the tube billet can be controlled within the required temperature range through compensation control method.
Hidden long evolutionary memory in a model biochemical network
Ali, Md. Zulfikar; Wingreen, Ned S.; Mukhopadhyay, Ranjan
2018-04-01
We introduce a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. Starting with a functional core module, random evolutionary drift increases network complexity even in the absence of specific selective pressures. Surprisingly, we uncover a hidden order in sequence space that gives rise to long-term evolutionary memory, implying strong constraints on network evolution due to the topology of accessible sequence space.
Mathematical modeling of large floating roof reservoir temperature arena
Directory of Open Access Journals (Sweden)
Liu Yang
2018-03-01
Full Text Available The current study is a simplification of related components of large floating roof tank and modeling for three dimensional temperature field of large floating roof tank. The heat transfer involves its transfer between the hot fluid in the oil tank, between the hot fluid and the tank wall and between the tank wall and the external environment. The mathematical model of heat transfer and flow of oil in the tank simulates the temperature field of oil in tank. Oil temperature field of large floating roof tank is obtained by numerical simulation, map the curve of central temperature dynamics with time and analyze axial and radial temperature of storage tank. It determines the distribution of low temperature storage tank location based on the thickness of the reservoir temperature. Finally, it compared the calculated results and the field test data; eventually validated the calculated results based on the experimental results.
Linear control theory for gene network modeling.
Shin, Yong-Jun; Bleris, Leonidas
2010-09-16
Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain) and linear state-space (time domain) can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.
Modeling Temporal Evolution and Multiscale Structure in Networks
DEFF Research Database (Denmark)
Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard
2013-01-01
Many real-world networks exhibit both temporal evolution and multiscale structure. We propose a model for temporally correlated multifurcating hierarchies in complex networks which jointly capture both effects. We use the Gibbs fragmentation tree as prior over multifurcating trees and a change......-point model to account for the temporal evolution of each vertex. We demonstrate that our model is able to infer time-varying multiscale structure in synthetic as well as three real world time-evolving complex networks. Our modeling of the temporal evolution of hierarchies brings new insights...
D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process
Directory of Open Access Journals (Sweden)
Shu-zhi Gao
2014-01-01
Full Text Available PVC stripping process is a kind of complicated industrial process with characteristics of highly nonlinear and time varying. Aiming at the problem of establishing the accurate mathematics model due to the multivariable coupling and big time delay, the dynamic fuzzy neural network (D-FNN is adopted to establish the PVC stripping process model based on the actual process operation datum. Then, the PVC stripping process is decoupled by the distributed neural network decoupling module to obtain two single-input-single-output (SISO subsystems (slurry flow to top tower temperature and steam flow to bottom tower temperature. Finally, the PID controller based on BP neural networks is used to control the decoupled PVC stripper system. Simulation results show the effectiveness of the proposed integrated intelligent control method.
Modeling moisture absorption kinetics of barley grain using viscoelastic model and neural networks
Directory of Open Access Journals (Sweden)
M Kamali
2015-09-01
Network (ANN as an important artificial intelligent method comparable to human brain capabilities is applied to train and store data in the form of weighted networks (Dayhoff, 1990. This method has superiority to many ordinary statistical and model making methods. In comparison to linear regression models, ANN does not require placing estimated values around mean values and for this reason it retains actual variations in the data being analyzed. Prediction by using trained ANN enables the researchers to decrease or increase input and output variables.Therefore, it is possible to produce a multivariate model with an output even more than the objectives deemed necessary (Heristev, 1998. The goal of this research was to predict instant moisture content of three barley varieties (Reyhan3, Fajr and MB862 during the soaking process under three temperature levels (10, 20 and 45 ◦C using two conventional ANN methods of multilayer perceptron (MLP and radial basis function (RBF in comparison with viscoelastic mathematical model and reporting the results. Materials and method: Barley varieties were collected from the Isfahan Province Agriculture Organization grain depository and were cleaned and the debris were separated before the experiments. The selected grains were sorted to three groups of small, medium and large grains sizes. To exclude the effect of grain size during moisture absorption, the medium size grains were used. The moisture content of the grains was determined based on the ASAE S352.2 DEC97 (ASAE, 1999 which were %8.23, %8.62 and %8.89 on a dry basis for Reyhan3, Fajr and MB862, respectively with no significant difference at %5 probability level (p>0.05. Experiments were conducted under three temperatures (10, 20 and 45 ◦C in the refrigerator, at room temperature and in the oven, respectively for each variety. In each experiment, 10 medium size grains were selected randomly and weighed with an AND laboratory scale model Gf-400 (made in Japan and placed in foam
Growth of cortical neuronal network in vitro: Modeling and analysis
International Nuclear Information System (INIS)
Lai, P.-Y.; Jia, L. C.; Chan, C. K.
2006-01-01
We present a detailed analysis and theoretical growth models to account for recent experimental data on the growth of cortical neuronal networks in vitro [Phys. Rev. Lett. 93, 088101 (2004)]. The experimentally observed synchronized firing frequency of a well-connected neuronal network is shown to be proportional to the mean network connectivity. The growth of the network is consistent with the model of an early enhanced growth of connection, but followed by a retarded growth once the synchronized cluster is formed. Microscopic models with dominant excluded volume interactions are consistent with the observed exponential decay of the mean connection probability as a function of the mean network connectivity. The biological implications of the growth model are also discussed
SPLAI: Computational Finite Element Model for Sensor Networks
Directory of Open Access Journals (Sweden)
Ruzana Ishak
2006-01-01
Full Text Available Wireless sensor network refers to a group of sensors, linked by a wireless medium to perform distributed sensing task. The primary interest is their capability in monitoring the physical environment through the deployment of numerous tiny, intelligent, wireless networked sensor nodes. Our interest consists of a sensor network, which includes a few specialized nodes called processing elements that can perform some limited computational capabilities. In this paper, we propose a model called SPLAI that allows the network to compute a finite element problem where the processing elements are modeled as the nodes in the linear triangular approximation problem. Our model also considers the case of some failures of the sensors. A simulation model to visualize this network has been developed using C++ on the Windows environment.
Modeling the propagation of mobile malware on complex networks
Liu, Wanping; Liu, Chao; Yang, Zheng; Liu, Xiaoyang; Zhang, Yihao; Wei, Zuxue
2016-08-01
In this paper, the spreading behavior of malware across mobile devices is addressed. By introducing complex networks to model mobile networks, which follows the power-law degree distribution, a novel epidemic model for mobile malware propagation is proposed. The spreading threshold that guarantees the dynamics of the model is calculated. Theoretically, the asymptotic stability of the malware-free equilibrium is confirmed when the threshold is below the unity, and the global stability is further proved under some sufficient conditions. The influences of different model parameters as well as the network topology on malware propagation are also analyzed. Our theoretical studies and numerical simulations show that networks with higher heterogeneity conduce to the diffusion of malware, and complex networks with lower power-law exponents benefit malware spreading.
Analysis and logical modeling of biological signaling transduction networks
Sun, Zhongyao
The study of network theory and its application span across a multitude of seemingly disparate fields of science and technology: computer science, biology, social science, linguistics, etc. It is the intrinsic similarities embedded in the entities and the way they interact with one another in these systems that link them together. In this dissertation, I present from both the aspect of theoretical analysis and the aspect of application three projects, which primarily focus on signal transduction networks in biology. In these projects, I assembled a network model through extensively perusing literature, performed model-based simulations and validation, analyzed network topology, and proposed a novel network measure. The application of network modeling to the system of stomatal opening in plants revealed a fundamental question about the process that has been left unanswered in decades. The novel measure of the redundancy of signal transduction networks with Boolean dynamics by calculating its maximum node-independent elementary signaling mode set accurately predicts the effect of single node knockout in such signaling processes. The three projects as an organic whole advance the understanding of a real system as well as the behavior of such network models, giving me an opportunity to take a glimpse at the dazzling facets of the immense world of network science.
International Nuclear Information System (INIS)
Li Hui; Wang Ruipian; Hu Shouyin
2003-01-01
This paper makes researches on 10 MW High-Temperature Gas-Cooled Reactor fault diagnosis system using Artificial Neural Network, and uses the tendency value and real value of the data under the accidents to train and test two BP networks respectively. The final diagnostic result is the combination of the results of the two networks. The compound system can enhance the accuracy and adaptability of the diagnosis compared to the single network system
UML modelling of network warfare examples
CSIR Research Space (South Africa)
Veerasamy, N
2011-08-01
Full Text Available ] Affects both civilian and military domains [8] [9] Related to the concepts of infowar, information operations, hacking, hackivism, cyberterrorism and cybotage depending on motivations and techniques [10] Encompasses both technological solutions...]. In addition, Williers as well as Qingbao and Anwar discuss more offensive aspects of information security and Network Warfare like Hacking, Vulnerability Injection, Network Attacks, Denial of Capability, Interception and Blockage [6] [18] [19]. Various...
Neural Network Models for Time Series Forecasts
Tim Hill; Marcus O'Connor; William Remus
1996-01-01
Neural networks have been advocated as an alternative to traditional statistical forecasting methods. In the present experiment, time series forecasts produced by neural networks are compared with forecasts from six statistical time series methods generated in a major forecasting competition (Makridakis et al. [Makridakis, S., A. Anderson, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, R. Winkler. 1982. The accuracy of extrapolation (time series) methods: Results of a ...
Weston, David J; Hanson, Paul J; Norby, Richard J; Tuskan, Gerald A; Wullschleger, Stan D
2012-02-01
Network analysis is now a common statistical tool for molecular biologists. Network algorithms are readily used to model gene, protein and metabolic correlations providing insight into pathways driving biological phenomenon. One output from such an analysis is a candidate gene list that can be responsible, in part, for the biological process of interest. The question remains, however, as to whether molecular network analysis can be used to inform process models at higher levels of biological organization. In our previous work, transcriptional networks derived from three plant species were constructed, interrogated for orthology and then correlated with photosynthetic inhibition at elevated temperature. One unique aspect of that study was the link from co-expression networks to net photosynthesis. In this addendum, we propose a conceptual model where traditional network analysis can be linked to whole-plant models thereby informing predictions on key processes such as photosynthesis, nutrient uptake and assimilation, and C partitioning.
Finite temperature CPN-1 model and long range Neel order
International Nuclear Information System (INIS)
Ichinose, Ikuo; Yamamoto, Hisashi.
1989-09-01
We study in d space-dimensions the finite temperature behavior of long range Neel order (LRNO) in CP N-1 model as a low energy effective field theory of the antiferromagnetic Heisenberg model. For d≤1, or d≤2 at any nonzero temperature, LRNO disappears, in agreement with Mermin-Wagner-Coleman's theorem. For d=3 in the weak coupling region, LRNO exists below the critical temperature T N (Neel temperature). T N decreases as the interlayer coupling becomes relatively weak compared with that within Cu-O layers. (author)
Energy based model for temperature dependent behavior of ferromagnetic materials
International Nuclear Information System (INIS)
Sah, Sanjay; Atulasimha, Jayasimha
2017-01-01
An energy based model for temperature dependent anhysteretic magnetization curves of ferromagnetic materials is proposed and benchmarked against experimental data. This is based on the calculation of macroscopic magnetic properties by performing an energy weighted average over all possible orientations of the magnetization vector. Most prior approaches that employ this method are unable to independently account for the effect of both inhomogeneity and temperature in performing the averaging necessary to model experimental data. Here we propose a way to account for both effects simultaneously and benchmark the model against experimental data from ~5 K to ~300 K for two different materials in both annealed (fewer inhomogeneities) and deformed (more inhomogeneities) samples. This demonstrates that this framework is well suited to simulate temperature dependent experimental magnetic behavior. - Highlights: • Energy based model for temperature dependent ferromagnetic behavior. • Simultaneously accounts for effect of temperature and inhomogeneities. • Benchmarked against experimental data from 5 K to 300 K.
Stochastic model and method of zoning water networks
Тевяшев, Андрей Дмитриевич; Матвиенко, Ольга Ивановна
2014-01-01
Water consumption at different time of the day is uneven. The model of steady flow distribution in water-supply networks is calculated for maximum consumption and effectively used in the network design and reconstruction. Quasi-stationary modes, in which the parameters are random variables and vary relative to their mean values are more suitable for operational management and planning of rational network operation modes.Leaks, which sometimes exceed 50 % of the volume of water supplied, are o...
A three-dimensional computational model of collagen network mechanics.
Directory of Open Access Journals (Sweden)
Byoungkoo Lee
Full Text Available Extracellular matrix (ECM strongly influences cellular behaviors, including cell proliferation, adhesion, and particularly migration. In cancer, the rigidity of the stromal collagen environment is thought to control tumor aggressiveness, and collagen alignment has been linked to tumor cell invasion. While the mechanical properties of collagen at both the single fiber scale and the bulk gel scale are quite well studied, how the fiber network responds to local stress or deformation, both structurally and mechanically, is poorly understood. This intermediate scale knowledge is important to understanding cell-ECM interactions and is the focus of this study. We have developed a three-dimensional elastic collagen fiber network model (bead-and-spring model and studied fiber network behaviors for various biophysical conditions: collagen density, crosslinker strength, crosslinker density, and fiber orientation (random vs. prealigned. We found the best-fit crosslinker parameter values using shear simulation tests in a small strain region. Using this calibrated collagen model, we simulated both shear and tensile tests in a large linear strain region for different network geometry conditions. The results suggest that network geometry is a key determinant of the mechanical properties of the fiber network. We further demonstrated how the fiber network structure and mechanics evolves with a local formation, mimicking the effect of pulling by a pseudopod during cell migration. Our computational fiber network model is a step toward a full biomechanical model of cellular behaviors in various ECM conditions.
Hybrid network defense model based on fuzzy evaluation.
Cho, Ying-Chiang; Pan, Jen-Yi
2014-01-01
With sustained and rapid developments in the field of information technology, the issue of network security has become increasingly prominent. The theme of this study is network data security, with the test subject being a classified and sensitive network laboratory that belongs to the academic network. The analysis is based on the deficiencies and potential risks of the network's existing defense technology, characteristics of cyber attacks, and network security technologies. Subsequently, a distributed network security architecture using the technology of an intrusion prevention system is designed and implemented. In this paper, first, the overall design approach is presented. This design is used as the basis to establish a network defense model, an improvement over the traditional single-technology model that addresses the latter's inadequacies. Next, a distributed network security architecture is implemented, comprising a hybrid firewall, intrusion detection, virtual honeynet projects, and connectivity and interactivity between these three components. Finally, the proposed security system is tested. A statistical analysis of the test results verifies the feasibility and reliability of the proposed architecture. The findings of this study will potentially provide new ideas and stimuli for future designs of network security architecture.
A Secure Network Coding-based Data Gathering Model and Its Protocol in Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Qian Xiao
2012-09-01
Full Text Available To provide security for data gathering based on network coding in wireless sensor networks (WSNs, a secure network coding-based data gathering model is proposed, and a data-privacy preserving and pollution preventing (DPPaamp;PP protocol using network coding is designed. DPPaamp;PP makes use of a new proposed pollution symbol selection and pollution (PSSP scheme based on a new obfuscation idea to pollute existing symbols. Analyses of DPPaamp;PP show that it not only requires low overhead on computation and communication, but also provides high security on resisting brute-force attacks.
Kailasam, Kamalakannan; Schmidt, Johannes; Bildirir, Hakan; Zhang, Guigang; Blechert, Siegfried; Wang, Xinchen; Thomas, Arne
2013-06-25
Two emerging material classes are combined in this work, namely polymeric carbon nitrides and microporous polymer networks. The former, polymeric carbon nitrides, are composed of amine-bridged heptazine moieties and showed interesting performance as a metal-free photocatalyst. These materials have, however, to be prepared at high temperatures, making control of their chemical structure difficult. The latter, microporous polymer networks have received increasing interest due to their high surface area, giving rise to interesting applications in gas storage or catalysis. Here, the central building block of carbon nitrides, a functionalized heptazine as monomer, and tecton are used to create microporous polymer networks. The resulting heptazine-based microporous polymers show high porosity, while their chemical structure resembles the ones of carbon nitrides. The polymers show activity for the photocatalytic production of hydrogen from water, even under visible light illumination. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Quantification Model for Estimating Temperature Field Distributions of Apple Fruit
Zhang , Min; Yang , Le; Zhao , Huizhong; Zhang , Leijie; Zhong , Zhiyou; Liu , Yanling; Chen , Jianhua
2009-01-01
International audience; A quantification model of transient heat conduction was provided to simulate apple fruit temperature distribution in the cooling process. The model was based on the energy variation of apple fruit of different points. It took into account, heat exchange of representative elemental volume, metabolism heat and external heat. The following conclusions could be obtained: first, the quantification model can satisfactorily describe the tendency of apple fruit temperature dis...
Generalized Tavis-Cummings models and quantum networks
Gorokhov, A. V.
2018-04-01
The properties of quantum networks based on generalized Tavis-Cummings models are theoretically investigated. We have calculated the information transfer success rate from one node to another in a simple model of a quantum network realized with two-level atoms placed in the cavities and interacting with an external laser field and cavity photons. The method of dynamical group of the Hamiltonian and technique of corresponding coherent states were used for investigation of the temporal dynamics of the two nodes model.
Analysis of organizational culture with social network models
Titov, S.
2015-01-01
Organizational culture is nowadays an object of numerous scientific papers. However, only marginal part of existing research attempts to use the formal models of organizational cultures. The lack of organizational culture models significantly limits the further research in this area and restricts the application of the theory to practice of organizational culture change projects. The article consists of general views on potential application of network models and social network analysis to th...
Wei, B. G.; Wu, X. Y.; Yao, Z. F.; Huang, H.
2017-11-01
Transformers are essential devices of the power system. The accurate computation of the highest temperature (HST) of a transformer’s windings is very significant, as for the HST is a fundamental parameter in controlling the load operation mode and influencing the life time of the insulation. Based on the analysis of the heat transfer processes and the thermal characteristics inside transformers, there is taken into consideration the influence of factors like the sunshine, external wind speed etc. on the oil-immersed transformers. Experimental data and the neural network are used for modeling and protesting of the HST, and furthermore, investigations are conducted on the optimization of the structure and algorithms of neutral network are conducted. Comparison is made between the measured values and calculated values by using the recommended algorithm of IEC60076 and by using the neural network algorithm proposed by the authors; comparison that shows that the value computed with the neural network algorithm approximates better the measured value than the value computed with the algorithm proposed by IEC60076.
Artificial neural network model of pork meat cubes osmotic dehydratation
Directory of Open Access Journals (Sweden)
Pezo Lato L.
2013-01-01
Full Text Available Mass transfer of pork meat cubes (M. triceps brachii, shaped as 1x1x1 cm, during osmotic dehydration (OD and under atmospheric pressure was investigated in this paper. The effects of different parameters, such as concentration of sugar beet molasses (60-80%, w/w, temperature (20-50ºC, and immersion time (1-5 h in terms of water loss (WL, solid gain (SG, final dry matter content (DM, and water activity (aw, were investigated using experimental results. Five artificial neural network (ANN models were developed for the prediction of WL, SG, DM, and aw in OD of pork meat cubes. These models were able to predict process outputs with coefficient of determination, r2, of 0.990 for SG, 0.985 for WL, 0.986 for aw, and 0.992 for DM compared to experimental measurements. The wide range of processing variables considered for the formulation of these models, and their easy implementation in a spreadsheet calculus make it very useful and practical for process design and control.
Time dependent mechanical modeling for polymers based on network theory
Energy Technology Data Exchange (ETDEWEB)
Billon, Noëlle [MINES ParisTech, PSL-Research University, CEMEF – Centre de mise en forme des matériaux, CNRS UMR 7635, CS 10207 rue Claude Daunesse 06904 Sophia Antipolis Cedex (France)
2016-05-18
Despite of a lot of attempts during recent years, complex mechanical behaviour of polymers remains incompletely modelled, making industrial design of structures under complex, cyclic and hard loadings not totally reliable. The non linear and dissipative viscoelastic, viscoplastic behaviour of those materials impose to take into account non linear and combined effects of mechanical and thermal phenomena. In this view, a visco-hyperelastic, viscoplastic model, based on network description of the material has recently been developed and designed in a complete thermodynamic frame in order to take into account those main thermo-mechanical couplings. Also, a way to account for coupled effects of strain-rate and temperature was suggested. First experimental validations conducted in the 1D limit on amorphous rubbery like PMMA in isothermal conditions led to pretty goods results. In this paper a more complete formalism is presented and validated in the case of a semi crystalline polymer, a PA66 and a PET (either amorphous or semi crystalline) are used. Protocol for identification of constitutive parameters is described. It is concluded that this new approach should be the route to accurately model thermo-mechanical behaviour of polymers using a reduced number of parameters of some physical meaning.