A Temperature-Dependent Battery Model for Wireless Sensor Networks.
Rodrigues, Leonardo M; Montez, Carlos; Moraes, Ricardo; Portugal, Paulo; Vasques, Francisco
2017-02-22
Energy consumption is a major issue in Wireless Sensor Networks (WSNs), as nodes are powered by chemical batteries with an upper bounded lifetime. Estimating the lifetime of batteries is a difficult task, as it depends on several factors, such as operating temperatures and discharge rates. Analytical battery models can be used for estimating both the battery lifetime and the voltage behavior over time. Still, available models usually do not consider the impact of operating temperatures on the battery behavior. The target of this work is to extend the widely-used Kinetic Battery Model (KiBaM) to include the effect of temperature on the battery behavior. The proposed Temperature-Dependent KiBaM (T-KiBaM) is able to handle operating temperatures, providing better estimates for the battery lifetime and voltage behavior. The performed experimental validation shows that T-KiBaM achieves an average accuracy error smaller than 0.33%, when estimating the lifetime of Ni-MH batteries for different temperature conditions. In addition, T-KiBaM significantly improves the original KiBaM voltage model. The proposed model can be easily adapted to handle other battery technologies, enabling the consideration of different WSN deployments.
A Temperature-Dependent Battery Model for Wireless Sensor Networks
Rodrigues, Leonardo M.; Montez, Carlos; Moraes, Ricardo; Portugal, Paulo; Vasques, Francisco
2017-01-01
Energy consumption is a major issue in Wireless Sensor Networks (WSNs), as nodes are powered by chemical batteries with an upper bounded lifetime. Estimating the lifetime of batteries is a difficult task, as it depends on several factors, such as operating temperatures and discharge rates. Analytical battery models can be used for estimating both the battery lifetime and the voltage behavior over time. Still, available models usually do not consider the impact of operating temperatures on the battery behavior. The target of this work is to extend the widely-used Kinetic Battery Model (KiBaM) to include the effect of temperature on the battery behavior. The proposed Temperature-Dependent KiBaM (T-KiBaM) is able to handle operating temperatures, providing better estimates for the battery lifetime and voltage behavior. The performed experimental validation shows that T-KiBaM achieves an average accuracy error smaller than 0.33%, when estimating the lifetime of Ni-MH batteries for different temperature conditions. In addition, T-KiBaM significantly improves the original KiBaM voltage model. The proposed model can be easily adapted to handle other battery technologies, enabling the consideration of different WSN deployments. PMID:28241444
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,...
Zhang, Jian; Yang, Xiao-hua; Chen, Xiao-juan
2015-01-01
Due to nonlinear and multiscale characteristics of temperature time series, a new model called wavelet network model based on multiple criteria decision making (WNMCDM) has been proposed, which combines the advantage of wavelet analysis, multiple criteria decision making, and artificial neural network. One case for forecasting extreme monthly maximum temperature of Miyun Reservoir has been conducted to examine the performance of WNMCDM model. Compared with nearest neighbor bootstrapping regr...
Identifying the optimal supply temperature in district heating networks - A modelling approach
DEFF Research Database (Denmark)
Mohammadi, Soma; Bojesen, Carsten
2014-01-01
The number of low-energy and energy renovated buildings with considerably low heating demand has been continuously increasing in recent years. Combined with utilizing low temperature sources, this development raises the necessity of introducing a new generation of District Heating [DH] Systems...... 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...
Mantovanelli, Ivana C. C.; Rivera, Elmer Ccopa; da Costa, Aline C.; Filho, Rubens Maciel
In this work a procedure for the development of a robust mathematical model for an industrial alcoholic fermentation process was evaluated. The proposed model is a hybrid neural model, which combines mass and energy balance equations with functional link networks to describe the kinetics. These networks have been shown to have a good nonlinear approximation capability, although the estimation of its weights is linear. The proposed model considers the effect of temperature on the kinetics and has the neural network weights reestimated always so that a change in operational conditions occurs. This allow to follow the system behavior when changes in operating conditions occur.
A Quantitative Risk Evaluation Model for Network Security Based on Body Temperature
Directory of Open Access Journals (Sweden)
Y. P. Jiang
2016-01-01
Full Text Available These days, in allusion to the traditional network security risk evaluation model, which have certain limitations for real-time, accuracy, characterization. This paper proposed a quantitative risk evaluation model for network security based on body temperature (QREM-BT, which refers to the mechanism of biological immune system and the imbalance of immune system which can result in body temperature changes, firstly, through the r-contiguous bits nonconstant matching rate algorithm to improve the detection quality of detector and reduce missing rate or false detection rate. Then the dynamic evolution process of the detector was described in detail. And the mechanism of increased antibody concentration, which is made up of activating mature detector and cloning memory detector, is mainly used to assess network risk caused by various species of attacks. Based on these reasons, this paper not only established the equation of antibody concentration increase factor but also put forward the antibody concentration quantitative calculation model. Finally, because the mechanism of antibody concentration change is reasonable and effective, which can effectively reflect the network risk, thus body temperature evaluation model was established in this paper. The simulation results showed that, according to body temperature value, the proposed model has more effective, real time to assess network security risk.
DEFF Research Database (Denmark)
Green, O.; Nadimi, E.S.; Blanes-Vidal, V.
2009-01-01
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...... temperatures in a full-sized silage stack over 53 days. Results showed that the wireless sensor nodes accurately monitored the temperature inside the silage stack at depths of 25 and 50cm and reliably transmitted the measured data through the network; between 98.9% and 99.4% of the packets disseminated from...
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.
Temperature-induced unfolding behavior of proteins studied by tensorial elastic network model.
Srivastava, Amit; Granek, Rony
2016-12-01
Motivated by single molecule experiments and recent molecular dynamics (MD) studies, we propose a simple and computationally efficient method based on a tensorial elastic network model to investigate the unfolding pathways of proteins under temperature variation. The tensorial elastic network model, which relies on the native state topology of a protein, combines the anisotropic network model, the bond bending elasticity, and the backbone twist elasticity to successfully predicts both the isotropic and anisotropic fluctuations in a manner similar to the Gaussian network model and anisotropic network model. The unfolding process is modeled by breaking the native contacts between residues one by one, and by assuming a threshold value for strain fluctuation. Using this method, we simulated the unfolding processes of four well-characterized proteins: chymotrypsin inhibitor, barnase, ubiquitein, and adenalyate kinase. We found that this step-wise process leads to two or more cooperative, first-order-like transitions between partial denaturation states. The sequence of unfolding events obtained using this method is consistent with experimental and MD studies. The results also imply that the native topology of proteins "encrypts" information regarding their unfolding process. Proteins 2016; 84:1767-1775. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
Marek, Juraj; Chvála, Aleš; Donoval, Daniel; Príbytný, Patrik; Molnár, Marián; Mikolášek, Miroslav
2014-04-01
A new, more accurate SPICE-like model of a power MOSFET containing a temperature dependent thermal network is described. The designed electro-thermal MOSFET model consists of several parts which represent different transistor behavior under different conditions such as reverse bias, avalanche breakdown and others. The designed model is able to simulate destruction of the device as thermal runaway and/or overcurrent destruction during the switching process of a wide variety of inductive loads. Modified thermal equivalent circuit diagrams were designed taking into account temperature dependence of thermal resistivity. The potential and limitations of the new models are presented and analyzed. The new model is compared with the standard and empirical models and brings a higher accuracy for rapid heating pulses. An unclamped inductive switching (UIS) test as a stressful condition was used to verify the proper behavior of the designed MOSFET 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.
Response surface and neural network based predictive models of cutting temperature in hard turning.
Mia, Mozammel; Dhar, Nikhil R
2016-11-01
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.
Chronopoulos, Kostas I; Tsiros, Ioannis X; Dimopoulos, Ioannis F; Alvertos, Nikolaos
2008-12-01
In this work artificial neural network (ANN) models are developed to estimate meteorological data values in areas with sparse meteorological stations. A more traditional interpolation model (multiple regression model, MLR) is also used to compare model results and performance. The application site is a canyon in a National Forest located in southern Greece. Four meteorological stations were established in the canyon; the models were then applied to estimate air temperature values as a function of the corresponding values of one or more reference stations. The evaluation of the ANN model results showed that fair to very good air temperature estimations may be achieved depending on the number of the meteorological stations used as reference stations. In addition, the ANN model was found to have better performance than the MLR model: mean absolute error values were found to be in the range 0.82-1.72 degrees C and 0.90-1.81 degrees C, for the ANN and the MLR models, respectively. These results indicate that ANN models may provide advantages over more traditional models or methods for temperature and other data estimations in areas where meteorological stations are sparse; they may be adopted, therefore, as an important component in various environmental modeling and management studies.
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
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.
Lobato, Justo; Cañizares, Pablo; Rodrigo, Manuel A.; Linares, José J.; Piuleac, Ciprian-George; Curteanu, Silvia
Neural network models represent an important tool of Artificial Intelligence for fuel cell researchers in order to help them to elucidate the processes within the cells, by allowing optimization of materials, cells, stacks, and systems and support control systems. In this work three types of neural networks, that have as common characteristic the supervised learning control (Multilayer Perceptron, Generalized Feedforward Network and Jordan and Elman Network), have been designed to model the performance of a polybenzimidazole-polymer electrolyte membrane fuel cells operating upon a temperature range of 100-175 °C. The influence of temperature of two periods was studied: the temperature in the conditioning period and temperature when the fuel cell was operating. Three inputs variables: the conditioning temperature, the operating temperature and current density were taken into account in order to evaluate their influence upon the potential, the cathode resistance and the ohmic resistance. The Multilayer Perceptron model provides good predictions for different values of operating temperatures and potential and, hence, it is the best choice among the study models, recommended to investigate the influence of process variables of PEMFCs.
Energy Technology Data Exchange (ETDEWEB)
Kroeger, P.G.; Kennett, R.J.; Colman, J.; Ginsberg, T. (Brookhaven National Lab., Upton, NY (United States))
1991-10-01
This report documents the THATCH code, which can be used to model general thermal and flow networks of solids and coolant channels in two-dimensional r-z geometries. The main application of THATCH is to model reactor thermo-hydraulic transients in High-Temperature Gas-Cooled Reactors (HTGRs). The available modules simulate pressurized or depressurized core heatup transients, heat transfer to general exterior sinks or to specific passive Reactor Cavity Cooling Systems, which can be air or water-cooled. Graphite oxidation during air or water ingress can be modelled, including the effects of added combustion products to the gas flow and the additional chemical energy release. A point kinetics model is available for analyzing reactivity excursions; for instance due to water ingress, and also for hypothetical no-scram scenarios. For most HTGR transients, which generally range over hours, a user-selected nodalization of the core in r-z geometry is used. However, a separate model of heat transfer in the symmetry element of each fuel element is also available for very rapid transients. This model can be applied coupled to the traditional coarser r-z nodalization. This report described the mathematical models used in the code and the method of solution. It describes the code and its various sub-elements. Details of the input data and file usage, with file formats, is given for the code, as well as for several preprocessing and postprocessing options. The THATCH model of the currently applicable 350 MW{sub th} reactor is described. Input data for four sample cases are given with output available in fiche form. Installation requirements and code limitations, as well as the most common error indications are listed. 31 refs., 23 figs., 32 tabs.
DEFF Research Database (Denmark)
Green, Ole; Shahrak Nadimi, Esmaeil; Blanes-Vidal, Victoria
2009-01-01
of withstanding the high loads that occurred during ensiling, storage, and feed-out. Mathematical models estimating the relations between the silage temperatures (at depths of 25 and 50 cm) and air and soil temperatures were obtained. Black-box modeling using the prediction error method (PEM) was selected...... 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...... as the identification method. Among different black-box models such as ARX, ARMAX, Output Error (OE), and Box-Jenkins (BJ), with different model orders, a third-order Box-Jenkins model structure gave the best performance in terms of prediction accuracy. The success rate of the models proposed for silage temperature...
Dai, Haifeng; Zhu, Letao; Zhu, Jiangong; Wei, Xuezhe; Sun, Zechang
2015-10-01
The accurate monitoring of battery cell temperature is indispensible to the design of battery thermal management system. To obtain the internal temperature of a battery cell online, an adaptive temperature estimation method based on Kalman filtering and an equivalent time-variant electrical network thermal (EENT) model is proposed. The EENT model uses electrical components to simulate the battery thermodynamics, and the model parameters are obtained with a least square algorithm. With a discrete state-space description of the EENT model, a Kalman filtering (KF) based internal temperature estimator is developed. Moreover, considering the possible time-varying external heat exchange coefficient, a joint Kalman filtering (JKF) based estimator is designed to simultaneously estimate the internal temperature and the external thermal resistance. Several experiments using the hard-cased LiFePO4 cells with embedded temperature sensors have been conducted to validate the proposed method. Validation results show that, the EENT model expresses the battery thermodynamics well, the KF based temperature estimator tracks the real central temperature accurately even with a poor initialization, and the JKF based estimator can simultaneously estimate both central temperature and external thermal resistance precisely. The maximum estimation errors of the KF- and JKF-based estimators are less than 1.8 °C and 1 °C respectively.
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.
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
Pavone, Andrea; Svensson, Jakob; Langenberg, Andreas; Pablant, Novimir; Wolf, Robert C.
2017-10-01
Artificial neural networks (ANNs) can reduce the computation time required for the application of Bayesian inference on large amounts of data by several orders of magnitude, making real-time analysis possible and, at the same time, providing a reliable alternative to more conventional inversion routines. The large scale fusion experiment Wendelstein 7-X (W7-X) requires tens of diagnostics for plasma parameter measurements and is using the Minerva Bayesian modelling framework as its main inference engine, which can handle joint inference in complex systems made of several physics models. Conventional inversion routines are applied to measured data to infer the posterior distribution of the free parameters of the models implemented in the framework. We have trained ANNs on a training set made of samples from the prior distribution of the free parameters and the corresponding data calculated with the forward model, so that the trained ANNs constitute a surrogate model of the physics model. The ANNs have been then applied to 2D images measured by an X-ray spectrometer, representing the spectral emission from plasma impurities measured along a fan of lines of sight covering a major fraction of the plasma cross-section, for the inference of ion temperature profiles and then compared with the conventional inversion routines, showing that they constitute a robust and reliable alternative for real time plasma parameter inference.
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.
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.
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.
Modeling the citation network by network cosmology.
Directory of Open Access Journals (Sweden)
Zheng Xie
Full Text Available 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...
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.
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. .
ESTIMATION OF PV MODULE SURFACE TEMPERATURE USING ARTIFICIAL NEURAL NETWORKS
Directory of Open Access Journals (Sweden)
Can Coskun
2016-12-01
Full Text Available This study aimed to use the artificial neural network (ANN method to estimate the surface temperature of a photovoltaic (PV panel. Using the experimentally obtained PV data, the accuracy of the ANN model was evaluated. To train the artificial neural network (ANN, outer temperature solar radiation and wind speed values were inputs and surface temperature was an output. The ANN was used to estimate PV panel surface temperature. Using the Levenberg-Marquardt (LM algorithm the feed forward artificial neural network was trained. Two back propagation type ANN algorithms were used and their performance was compared with the estimate from the LM algorithm. To train the artificial neural network, experimental data were used for two thirds with the remaining third used for testing. Additionally scaled conjugate gradient (SCG back propagation and resilient back propagation (RB type ANN algorithms were used for comparison with the LM algorithm. The performances of these three types of artificial neural network were compared and mean error rates of between 0.005962 and 0.012177% were obtained. The best estimate was produced by the LM algorithm. Estimation of PV surface temperature with artificial neural networks provides better results than conventional correlation methods. This study showed that artificial neural networks may be effectively used to estimate PV surface temperature.
Modeling network technology deployment rates with different network models
Chung, Yoo
2011-01-01
To understand the factors that encourage the deployment of a new networking technology, we must be able to model how such technology gets deployed. We investigate how network structure influences deployment with a simple deployment model and different network models through computer simulations. The results indicate that a realistic model of networking technology deployment should take network structure into account.
Low-Temperature Behaviour of Social and Economic Networks
Directory of Open Access Journals (Sweden)
Guido Caldarelli
2013-08-01
Full Text Available Real-world social and economic networks typically display a number of particular topological properties, such as a giant connected component, a broad degree distribution, the small-world property and the presence of communities of densely interconnected nodes. Several models, including ensembles of networks, also known in social science as Exponential Random Graphs, have been proposed with the aim of reproducing each of these properties in isolation. Here, we define a generalized ensemble of graphs by introducing the concept of graph temperature, controlling the degree of topological optimization of a network. We consider the temperature-dependent version of both existing and novel models and show that all the aforementioned topological properties can be simultaneously understood as the natural outcomes of an optimized, low-temperature topology. We also show that seemingly different graph models, as well as techniques used to extract information from real networks are all found to be particular low-temperature cases of the same generalized formalism. One such technique allows us to extend our approach to real weighted networks. Our results suggest that a low graph temperature might be a ubiquitous property of real socio-economic networks, placing conditions on the diffusion of information across these systems.
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.
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...
Polymer networks: Modeling and applications
Masoud, Hassan
Polymer networks are an important class of materials that are ubiquitously found in natural, biological, and man-made systems. The complex mesoscale structure of these soft materials has made it difficult for researchers to fully explore their properties. In this dissertation, we introduce a coarse-grained computational model for permanently cross-linked polymer networks than can properly capture common properties of these materials. We use this model to study several practical problems involving dry and solvated networks. Specifically, we analyze the permeability and diffusivity of polymer networks under mechanical deformations, we examine the release of encapsulated solutes from microgel capsules during volume transitions, and we explore the complex tribological behavior of elastomers. Our simulations reveal that the network transport properties are defined by the network porosity and by the degree of network anisotropy due to mechanical deformations. In particular, the permeability of mechanically deformed networks can be predicted based on the alignment of network filaments that is characterized by a second order orientation tensor. Moreover, our numerical calculations demonstrate that responsive microcapsules can be effectively utilized for steady and pulsatile release of encapsulated solutes. We show that swollen gel capsules allow steady, diffusive release of nanoparticles and polymer chains, whereas gel deswelling causes burst-like discharge of solutes driven by an outward flow of the solvent initially enclosed within a shrinking capsule. We further demonstrate that this hydrodynamic release can be regulated by introducing rigid microscopic rods in the capsule interior. We also probe the effects of velocity, temperature, and normal load on the sliding of elastomers on smooth and corrugated substrates. Our friction simulations predict a bell-shaped curve for the dependence of the friction coefficient on the sliding velocity. Our simulations also illustrate
Models of educational institutions' networking
Shilova Olga Nikolaevna
2015-01-01
The importance of educational institutions' networking in modern sociocultural conditions and a definition of networking in education are presented in the article. The results of research levels, methods and models of educational institutions' networking are presented and substantially disclosed.
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 (SM). 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 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 SM). This is particularly marked in regions with high variability in minimum and maximum θe, where more complex models
Techniques for Modelling Network Security
Lech Gulbinovič
2012-01-01
The article compares modelling techniques for network security, including the theory of probability, Markov processes, Petri networks and application of stochastic activity networks. The paper introduces the advantages and disadvantages of the above proposed methods and accepts the method of modelling the network of stochastic activity as one of the most relevant. The stochastic activity network allows modelling the behaviour of the dynamic system where the theory of probability is inappropri...
Smerdon, Jason; Werner, Johannes; Fernandez-Donado, Laura; Buntgen, Ulf; Charpentier Ljungqvist, Fredrik; Esper, Jan; Fidel Gonzalez-Rouco, J.; Luterbacher, Juerg; McCarroll, Danny; Wagner, Sebastian; Wahl, Eugene; Wanner, Heinz; Zorita, Eduardo
2013-04-01
A new reconstruction of European summer (JJA) land temperatures is presented and compared to 37 forced transient simulations of the last millennium from coupled General Circulation Models (CGCMs). The reconstructions are derived from eleven annually resolved tree-ring and documentary records from ten European countries/regions, compiled as part of the Euro_Med working group contribution to the PAGES 2k Regional Network. Records were selected based upon their summer temperature signal, annual resolution, and time-continuous sampling. All tree-ring data were detrended using the Regional Curve Standardization (RCS) method to retain low-frequency variance in the resulting mean chronologies. A nested Composite-Plus-Scale (CPS) mean temperature reconstruction extending from 138 B.C.E. to 2003 C.E. was derived using nine nests reflecting the availability of predictors back in time. Each nest was calculated using a weighted composite based on the correlation of each proxy with the CRUTEM4v mean European JJA land temperature (35°-70°N, 10°W-40°E). The CPS methodology was implemented using a sliding calibration period, initially extending from 1850-1953 C.E. and incrementing by one year until reaching the final period of 1900-2003 C.E. Within each calibration step, the 50 years excluded from calibration were used for validation. Validation statistics across all reconstruction ensemble members within each nest indicate skillful reconstructions (RE: 0.42-0.64; CE: 0.26-0.54) and are all above the maximum validation statistics achieved in an ensemble of red noise benchmarking experiments. A gridded (5°x5°) European summer (JJA) temperature reconstruction back to 750 C.E. was derived using Bayesian inference together with a localized stochastic description of the underlying processes. Instrumental data are JJA means from the 5° European land grid cells in the CRUTEM4v dataset. Predictive experiments using the full proxy data were made, resulting in a multivariate
Modeling Network Interdiction Tasks
2015-09-17
allow professionals and families to stay in touch through voice or video calls. Power grids provide electricity to homes , offices, and recreational...instances using IBMr ILOGr CPLEXr Optimization Studio V12.6. For each instance, two solutions are deter- mined. First, the MNDP-a model is solved with no...three values: 0.25, 0.50, or 0.75. The DMP-a model is solved for the various random network instances using IBMr ILOGr CPLEXr Optimization Studio V12.6
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...
A general regression neural network and Monte Carlo simulation model for predicting survival and growth of Salmonella on raw chicken skin as a function of serotype (Typhimurium, Kentucky, Hadar), temperature (5 to 50C) and time (0 to 8 h) was developed. Poultry isolates of Salmonella with natural r...
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.
Tensor network models of multiboundary wormholes
Peach, Alex; Ross, Simon F.
2017-05-01
We study the entanglement structure of states dual to multiboundary wormhole geometries using tensor network models. Perfect and random tensor networks tiling the hyperbolic plane have been shown to provide good models of the entanglement structure in holography. We extend this by quotienting the plane by discrete isometries to obtain models of the multiboundary states. We show that there are networks where the entanglement structure is purely bipartite, extending results obtained in the large temperature limit. We analyse the entanglement structure in a range of examples.
Do Network Models Just Model Networks? On The Applicability of Network-Oriented Modeling
Treur, J.; Shmueli, Erez
2017-01-01
In this paper for a Network-Oriented Modelling perspective based on temporal-causal networks it is analysed how generic and applicable it is as a general modelling approach and as a computational paradigm. This results in an answer to the question in the title different from: network models just
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.
Smerdon, J. E.; Büntgen, U.; Ljungqvist, F. C.; Esper, J.; Fernández-Donado, L.; Gonzalez-Rouco, F. J.; Luterbacher, J.; McCarroll, D.; Wagner, S.; Wahl, E. R.; Wanner, H.; Werner, J.; Zorita, E.
2012-12-01
A reconstruction of mean European summer (JJA) land temperatures from 138 B.C.E. to 2003 C.E. is presented and compared to 37 forced transient simulations of the last millennium from coupled General Circulation Models (CGCMs). Eleven annually resolved tree-ring and documentary records from ten European countries/regions were used for the reconstruction and compiled as part of the Euro_Med working group contribution to the PAGES 2k Regional Network. Records were selected based upon their summer temperature signal, annual resolution, and time-continuous sampling. All tree-ring data were detrended using the Regional Curve Standardization (RCS) method to retain low-frequency variance in the resulting mean chronologies. The calibration time series was the area-weighted JJA temperature computed from the CRUTEM4v dataset over a European land domain (35°-70°N, 10°W-40°E). A nested 'Composite-Plus-Scale' reconstruction was derived using nine nests reflecting the availability of predictors back in time. Each nest was calculated by standardizing the available predictor series over the calibration interval, and subsequently calculating a weighted composite in which each proxy was multiplied by its correlation with the target index. The CPS methodology was implemented using a resampling scheme that uses 104 years for calibration. The initial calibration period extended from 1850-1953 C.E. and was incremented by one year until reaching the final period of 1900-2003 C.E., yielding a total of 51 reconstructions for each nest. Within each calibration step, the 50 years excluded from calibration were used for validation. Validation statistics across all reconstruction ensemble members within each nest indicate skillful reconstructions (RE: 0.42-0.64; CE: 0.26-0.54) and are all above the maximum validation statistics achieved in an ensemble of red noise benchmarking experiments. Warm periods in the derived reconstruction during the 1st, 2nd, and 7th-12th centuries compare to
Dimensionality reduction and network inference for sea surface temperature data
Falasca, Fabrizio; Bracco, Annalisa; Nenes, Athanasios; Dovrolis, Constantine; Fountalis, Ilias
2017-04-01
Earth's climate is a complex dynamical system. The underlying components of the system interact with each other (in a linear or non linear way) on several spatial and time scales. Network science provides a set of tools to study the structure and dynamics of such systems. Here we propose an application of a novel network inference method, δ-MAPS, to investigate sea surface temperature (SST) fields in reanalyses and models. δ-MAPS first identifies the underlying components (domains) of the system, modeling them as spatially contiguous, potentially overlapping regions of highly correlated temporal activity, and then infers the weighted and potentially lagged interactions between them. The SST network is represented as a weighted and directed graph. Edge direction captures the temporal ordering of events, while edge weights capture the magnitude of the interaction between the domains. We focus on two reanalysis datasets (HadISST and COBE ) and on a dozen of runs of the CESM model (extracted from the so-called large ensemble). The networks are built using 45 years of data every 3 years for the total dataset temporal coverage (from 1871 to 2015 for HadISST, from 1891 to 2015 for COBE and from 1920 to 2100 for CESM members). We then explore similarities and differences between reanalyses and models in terms of the domains identified, the networks inferred and their time evolution. The spatial extent and shape of the identified domains is consistent between observations and models. According to our analysis the largest SST domain always corresponds to the El Niño Southern Oscillation (ENSO) while most of the other domains correspond to known climate modes. However, the network structure shows significant differences. For example, the unique role played by the South Tropical Atlantic in the observed network is not captured by any model run. Regarding the time evolution of the system we focus on the strength of ENSO: while we observe a positive trend for observations and
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
African Journals Online (AJOL)
LUCY
Algorithm and flow chart based on kinetic model were used for developing the Visual Basic code, ASP-. SIM. The code ... wastewater treatment using kinetic models ... Soluble Chemical oxygen demand. rbCOD. Readily biodegradable Chemical oxygen demand. TKN. Total Kjeldahl nitrogen. NH4-N. Ammonia nitrogen. TSS.
Analysis of heat exchanger network for temperature fluctuation
Directory of Open Access Journals (Sweden)
Jin Zunlong
2015-09-01
Full Text Available Subject to temperature disturbance, exchangers in heat exchanger network will interact. It is necessary to evaluate the degree of temperature fluctuation in the network. There is inherently linear relationship between output and inlet temperatures of heat exchanger network. Based on this, the concept of temperature-change sensitivity coefficient was put forward. Quantitative influence of temperature fluctuation in the network was carried out in order to examine transmission character of temperature fluctuation in the system. And the information was obtained for improving the design quality of heat exchanger network. Favorable results were obtained by the introduced method compared with the experimental results. These results will assist engineers to distinguish primary and secondary influencing factors, which can be used in observing and controlling influencing factors accurately.
Modeling semiflexible polymer networks
Broedersz, Chase P.; MacKintosh, Fred C.
2014-01-01
Here, we provide an overview of theoretical approaches to semiflexible polymers and their networks. Such semiflexible polymers have large bending rigidities that can compete with the entropic tendency of a chain to crumple up into a random coil. Many studies on semiflexible polymers and their assemblies have been motivated by their importance in biology. Indeed, crosslinked networks of semiflexible polymers form a major structural component of tissue and living cells. Reconstituted networks o...
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.
Developing Personal Network Business Models
DEFF Research Database (Denmark)
Saugstrup, Dan; Henten, Anders
2006-01-01
on the 'state of the art' in the field of business modeling. Furthermore, the paper suggests three generic business models for PNs: a service oriented model, a self-organized model, and a combination model. Finally, examples of relevant services and applications in relation to three different cases......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...... are presented and analyzed in light of business modeling of PN....
Malcolm, I.; Fryer, R. J.; Bacon, P. J.; Stirling, D.
2015-12-01
There has been increasing interest in river temperature monitoring and research in recent years. This has been driven by factors including a greater awareness of the importance of river temperature for freshwater ecology, the potential for detrimental extremes under climate change and the availability of increasingly affordable dataloggers. A number of studies have attempted to collate and analyse pre-existing long-term (decadal) datasets to assess for evidence of temporal trends. These studies require considerable care given the magnitude of temporal trends (often manufacturer reported accuracy with consequences for the reliability and interpretation of findings. This study assessed the potential influence of logger bias on reported temperature trends in the Girnock Burn, Scotland over > 25 years. The bias of temperature measurements made by different dataloggers (two makes and five models) was determined through cross-calibration against a reference datalogger. Long-term trends in stream temperature metrics (daily mean, max, min) were characterised using Generalised Additive Mixed Models (GAMM). Models were fitted to (1) the raw data and (2) data corrected for logger bias. Significant non-linear temporal trends were observed in the raw data. These trends were accentuated when corrected for logger bias. Given the potential to accentuate or remove long-term trends, it is suggested that robust internal and external calibration and quality control procedures should be established for new temperature networks. Such approaches are capable of removing logger bias and improving accuracy by an order of magnitude over manufacturer stated values.
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
2016-11-09
standpoint remains more of an art than a science . Even when well executed, the ongoing evolution of the network may violate initial, security-critical design...from a security standpoint remains more of an art than a science . Even when well executed, the ongoing evolution of the network may violate initial...is outside the scope of this paper. As such, we focus on event probabilities. The output of the network porosity model is a stream of timestamped
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.
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.
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.
[Working Temperature Predication of Artificial Heart Based on Neural Network].
Li, Qilei; Yang, Ming; Ou, Wenchu; Meng, Fan; Xu, Zihao; Xu, Liang
2015-03-01
The purpose of this paper is to achieve a measurement of temperature prediction for artificial heart without sensor, for which the research briefly describes the application of back propagation neural network as well as the optimized, by genetic algorithm, BP network. Owing to the limit of environment after the artificial heart implanted, detectable parameters out of body are taken advantage of to predict the working temperature of the pump. Lastly, contrast is made to demonstrate the prediction result between BP neural network and genetically optimized BP network, by which indicates that the probability is 1.84% with the margin of error more than 1%.
Modeling semiflexible polymer networks
Broedersz, C.P.; MacKintosh, F.C.
2014-01-01
This is an overview of theoretical approaches to semiflexible polymers and their networks. Such semiflexible polymers have large bending rigidities that can compete with the entropic tendency of a chain to crumple up into a random coil. Many studies on semiflexible polymers and their assemblies have
Energy and exergy analysis of low temperature district heating network
DEFF Research Database (Denmark)
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...... 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...
Mobility Model for Tactical Networks
Rollo, Milan; Komenda, Antonín
In this paper a synthetic mobility model which represents behavior and movement pattern of heterogeneous units in disaster relief and battlefield scenarios is proposed. These operations usually take place in environment without preexisting communication infrastructure and units thus have to be connected by wireless communication network. Units cooperate to fulfill common tasks and communication network has to serve high amount of communication requests, especially data, voice and video stream transmissions. To verify features of topology control, routing and interaction protocols software simulations are usually used, because of their scalability, repeatability and speed. Behavior of all these protocols relies on the mobility model of the network nodes, which has to resemble real-life movement pattern. Proposed mobility model is goal-driven and provides support for various types of units, group mobility and realistic environment model with obstacles. Basic characteristics of the mobility model like node spatial distribution and average node degree were analyzed.
Modelling freeway networks by hybrid stochastic models
Boel, R.; Mihaylova, L.
2004-01-01
Traffic flow on freeways is a nonlinear, many-particle phenomenon, with complex interactions between the vehicles. This paper presents a stochastic hybrid model of freeway traffic at a time scale and at a level of detail suitable for on-line flow estimation, for routing and ramp metering control. The model describes the evolution of continuous and discrete state variables. The freeway is considered as a network of components, each component representing a different section of the network. The...
Werner, J.; Büntgen, U.; Ljungqvist, F. C.; Esper, J.; Fernández-Donado, L.; Gonzalez-Rouco, F. J.; Luterbacher, J.; McCarroll, D.; Smerdon, J. E.; Wagner, S.; Wahl, E. R.; Wanner, H.; Zorita, E.
2012-12-01
We present a new gridded (5°x5°) European summer (JJA) temperature reconstruction back to 750 C.E. and compare it to an ensemble of millennium length forced transient climate model (general circulation models, GCMs) runs. The reconstruction is based on ten long, high quality tree ring records and one composite documentary record, all of them annually resolved. The spatial coverage spans the area 41°N-68°N and 1°E-25°E. Instrumental data used are the land mass grid cells of the CRUTEM4v dataset. Seasonal summer means were calculated from the data over the period 1850-2010 C.E. as anomalies wrt. the 1961-90 C.E. climatology. The climate field reconstruction was performed using Bayesian inference together with a localized stochastic description of the underlying processes (Tingley and Huybers 2010a,b; Werner et al. 2012). To this end, chains using different initial conditions as well as subsets of the data were run to estimate the posterior distributions. From these results, predictive experiments using the full proxy data were made, resulting in a multivariate distribution of temperature reconstructions from 750 - 2003 C.E. The mean of this distribution is the optimal estimate of the gridded annual summer temperature anomalies, the width delivers impartial reconstruction uncertainties. The derived reconstruction is compared with independent long instrumental and proxy data on decadal-to-centennial time scales. The simulations are grouped in two categories depending on the magnitude of change in solar forcing used to drive the model. We then compare the new gridded reconstruction with the GCM results, focusing on two key periods over the last one and a half millennia: the Little Ice Age (LIA) and the Medieval Climate Anomaly (MCA). While all data show higher medieval temperatures and cooler temperatures during the LIA, the spatial distribution of anomalies and the range of values differ. When comparing the key periods to present day climate, both periods were
Modelling water temperature in TOXSWA
Jacobs, C.M.J.; Deneer, J.W.; Adriaanse, P.I.
2010-01-01
A reasonably accurate estimate of the water temperature is necessary for a good description of the degradation of plant protection products in water which is used in the surface water model TOXSWA. Based on a consideration of basic physical processes that describe the influence of weather on the
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.
Directory of Open Access Journals (Sweden)
Tarikul ISLAM
2015-08-01
Full Text Available This paper represents the study of the effect of temperature on different capacitive humidity sensors used in practice. Capacitance of the humidity sensor, which is a function of concentration of water vapor, also depends on ambient temperature. This variation of ambient temperature causes error in the performance of sensor outputs and its compensation is essential. In this paper, we have used an artificial neural network to compensate the effect of ambient temperature error. The proposed artificial neural network technique is based on inverse model of the sensor. The technique is applicable for compensation of linear or nonlinear temperature effect of humidity sensor. It can also compensate the nonlinearity of the capacitive humidity response which is an issue for all most all types of humidity sensor. Our simulation studies show the sensor output and artificial neural network model output matches closely. Even though sensor characteristics change with temperature, the proposed model performs well irrespective of any change in temperature. It can be extended for the temperature compensation of other sensors. The maximum error for nonlinearity using the ANN technique are 0.2 % and temperature error of 0.08 % for temperature range between 10 0C to 60 0C of Sensor 3 and 0.01 % for temperature range between 25 0C to 85 0C of Sensor 4 respectively.
A Multilayer Model of Computer Networks
Shchurov, Andrey A.
2015-01-01
The fundamental concept of applying the system methodology to network analysis declares that network architecture should take into account services and applications which this network provides and supports. This work introduces a formal model of computer networks on the basis of the hierarchical multilayer networks. In turn, individual layers are represented as multiplex networks. The concept of layered networks provides conditions of top-down consistency of the model. Next, we determined the...
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...
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
Data modeling of network dynamics
Jaenisch, Holger M.; Handley, James W.; Faucheux, Jeffery P.; Harris, Brad
2004-01-01
This paper highlights Data Modeling theory and its use for text data mining as a graphical network search engine. Data Modeling is then used to create a real-time filter capable of monitoring network traffic down to the port level for unusual dynamics and changes in business as usual. This is accomplished in an unsupervised fashion without a priori knowledge of abnormal characteristics. Two novel methods for converting streaming binary data into a form amenable to graphics based search and change detection are introduced. These techniques are then successfully applied to 1999 KDD Cup network attack data log-on sessions to demonstrate that Data Modeling can detect attacks without prior training on any form of attack behavior. Finally, two new methods for data encryption using these ideas are proposed.
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......, 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...
Thermal Network Modelling Handbook
1972-01-01
Thermal mathematical modelling is discussed in detail. A three-fold purpose was established: (1) to acquaint the new user with the terminology and concepts used in thermal mathematical modelling, (2) to present the more experienced and occasional user with quick formulas and methods for solving everyday problems, coupled with study cases which lend insight into the relationships that exist among the various solution techniques and parameters, and (3) to begin to catalog in an orderly fashion the common formulas which may be applied to automated conversational language techniques.
Meth math: modeling temperature responses to methamphetamine.
Molkov, Yaroslav I; Zaretskaia, Maria V; Zaretsky, Dmitry V
2014-04-15
Methamphetamine (Meth) can evoke extreme hyperthermia, which correlates with neurotoxicity and death in laboratory animals and humans. The objective of this study was to uncover the mechanisms of a complex dose dependence of temperature responses to Meth by mathematical modeling of the neuronal circuitry. On the basis of previous studies, we composed an artificial neural network with the core comprising three sequentially connected nodes: excitatory, medullary, and sympathetic preganglionic neuronal (SPN). Meth directly stimulated the excitatory node, an inhibitory drive targeted the medullary node, and, in high doses, an additional excitatory drive affected the SPN node. All model parameters (weights of connections, sensitivities, and time constants) were subject to fitting experimental time series of temperature responses to 1, 3, 5, and 10 mg/kg Meth. Modeling suggested that the temperature response to the lowest dose of Meth, which caused an immediate and short hyperthermia, involves neuronal excitation at a supramedullary level. The delay in response after the intermediate doses of Meth is a result of neuronal inhibition at the medullary level. Finally, the rapid and robust increase in body temperature induced by the highest dose of Meth involves activation of high-dose excitatory drive. The impairment in the inhibitory mechanism can provoke a life-threatening temperature rise and makes it a plausible cause of fatal hyperthermia in Meth users. We expect that studying putative neuronal sites of Meth action and the neuromediators involved in a detailed model of this system may lead to more effective strategies for prevention and treatment of hyperthermia induced by amphetamine-like stimulants.
Network Models of Mechanical Assemblies
Whitney, Daniel E.
Recent network research has sought to characterize complex systems with a number of statistical metrics, such as power law exponent (if any), clustering coefficient, community behavior, and degree correlation. Use of such metrics represents a choice of level of abstraction, a balance of generality and detailed accuracy. It has been noted that "social networks" consistently display clustering coefficients that are higher than those of random or generalized random networks, that they have small world properties such as short path lengths, and that they have positive degree correlations (assortative mixing). "Technological" or "non-social" networks display many of these characteristics except that they generally have negative degree correlations (disassortative mixing). [Newman 2003i] In this paper we examine network models of mechanical assemblies. Such systems are well understood functionally. We show that there is a cap on their average nodal degree and that they have negative degree correlations (disassortative mixing). We identify specific constraints arising from first principles, their structural patterns, and engineering practice that suggest why they have these properties. In addition, we note that their main "motif" is closed loops (as it is for electric and electronic circuits), a pattern that conventional network analysis does not detect but which is used by software intended to aid in the design of such systems.
Service entity network virtualization architecture and model
Jin, Xue-Guang; Shou, Guo-Chu; Hu, Yi-Hong; Guo, Zhi-Gang
2017-07-01
Communication network can be treated as a complex network carrying a variety of services and service can be treated as a network composed of functional entities. There are growing interests in multiplex service entities where individual entity and link can be used for different services simultaneously. Entities and their relationships constitute a service entity network. In this paper, we introduced a service entity network virtualization architecture including service entity network hierarchical model, service entity network model, service implementation and deployment of service entity networks. Service entity network oriented multiplex planning model were also studied and many of these multiplex models were characterized by a significant multiplex of the links or entities in different service entity network. Service entity networks were mapped onto shared physical resources by dynamic resource allocation controller. The efficiency of the proposed architecture was illustrated in a simulation environment that allows for comparative performance evaluation. The results show that, compared to traditional networking architecture, this architecture has a better performance.
Model-based control of district heating supply temperature
Energy Technology Data Exchange (ETDEWEB)
Saarinen, Linn
2010-11-15
A model-based control strategy for the supply temperature to a district heating network was tested during three weeks at Idbaecken's CHP plant. The aim was to increase the electricity efficiency by a lower supply temperature, without risking the delivery reliability of heat to the district heating customers. Simulations and tests showed that at high loads, the mean supply temperature could be reduced by 4 deg C and the electricity production could be increased by 2.5%
An artificial neural network based fast radiative transfer model for ...
Indian Academy of Sciences (India)
In the present study, a fast radiative transfer model using neural networks is proposed to simulate radiances corresponding to the wavenumbers of INSAT-3D. Realistic atmospheric temperature and humidity profiles have been used for training the network. Spectral response functions of GOES-13, a satellite similar in ...
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......, 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....... Working through these cases, students will learn to manage and evaluate realistic intelligence accounts....
CNEM: Cluster Based Network Evolution Model
Directory of Open Access Journals (Sweden)
Sarwat Nizamani
2015-01-01
Full Text Available This paper presents a network evolution model, which is based on the clustering approach. The proposed approach depicts the network evolution, which demonstrates the network formation from individual nodes to fully evolved network. An agglomerative hierarchical clustering method is applied for the evolution of network. In the paper, we present three case studies which show the evolution of the networks from the scratch. These case studies include: terrorist network of 9/11 incidents, terrorist network of WMD (Weapons Mass Destruction plot against France and a network of tweets discussing a topic. The network of 9/11 is also used for evaluation, using other social network analysis methods which show that the clusters created using the proposed model of network evolution are of good quality, thus the proposed method can be used by law enforcement agencies in order to further investigate the criminal networks
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.
Evaluation of brightness temperature from a forward model of ...
Indian Academy of Sciences (India)
get importance by providing high resolution data to assimilate into the numerical weather prediction models. A good network of operational radiome- ters along with radiosonde and satellite measure- ments may definitely meet the requirement of high resolution models in the near future. Retrieval of profiles of temperature ...
Modeling Temperature and Pricing Weather Derivatives Based on Temperature
Directory of Open Access Journals (Sweden)
Birhan Taştan
2017-01-01
Full Text Available This study first proposes a temperature model to calculate the temperature indices upon which temperature-based derivatives are written. The model is designed as a mean-reverting process driven by a Levy process to represent jumps and other features of temperature. Temperature indices are mainly measured as deviations from a base temperature, and, hence, the proposed model includes jumps because they may constitute an important part of this deviation for some locations. The estimated value of a temperature index and its distribution in this model apply an inversion formula to the temperature model. Second, this study develops a pricing process over calculated index values, which returns a customized price for temperature-based derivatives considering that temperature has unique effects on every economic entity. This personalized price is also used to reveal the trading behavior of a hypothesized entity in a temperature-based derivative trade with profit maximization as the objective. Thus, this study presents a new method that does not need to evaluate the risk-aversion behavior of any economic entity.
Towards a quantum network of room temperature quantum devices
Jordaan, Bertus; Shahrokhshahi, Reihaneh; Namazi, Mehdi; Goham, Connor; Figueroa, Eden
2017-04-01
Progressing quantum technologies to room temperature operation is key to unlock the potential and economical viability of novel many-device architectures. Along these lines, warm vapor alleviates the need for laser trapping and cooling in vacuum or cooling to cryogenic temperatures. Here we report our progress towards building a prototypical quantum network, containing several high duty cycle room-temperature quantum memories interconnected using high rate single photon sources. We have already demonstrated important capabilities, such as memory-built photon-shaping techniques, compatibility with BB84-like quantum communication links, and the possibility of interfacing with low bandwidth (MHz range), cavity enhanced, SPDC-based photon source tuned to the Rb transitions. This body of works suggest that an elementary quantum network of room temperature devices is already within experimental reach.
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
Directory of Open Access Journals (Sweden)
D. Schmidt
2007-06-01
Full Text Available 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.
Probabilistic logic modeling of network reliability for hybrid network architectures
Energy Technology Data Exchange (ETDEWEB)
Wyss, G.D.; Schriner, H.K.; Gaylor, T.R.
1996-10-01
Sandia National Laboratories has found that the reliability and failure modes of current-generation network technologies can be effectively modeled using fault tree-based probabilistic logic modeling (PLM) techniques. We have developed fault tree models that include various hierarchical networking technologies and classes of components interconnected in a wide variety of typical and atypical configurations. In this paper we discuss the types of results that can be obtained from PLMs and why these results are of great practical value to network designers and analysts. After providing some mathematical background, we describe the `plug-and-play` fault tree analysis methodology that we have developed for modeling connectivity and the provision of network services in several current- generation network architectures. Finally, we demonstrate the flexibility of the method by modeling the reliability of a hybrid example network that contains several interconnected ethernet, FDDI, and token ring segments. 11 refs., 3 figs., 1 tab.
Generalization performance of regularized neural network models
DEFF Research Database (Denmark)
Larsen, Jan; Hansen, Lars Kai
1994-01-01
Architecture optimization is a fundamental problem of neural network modeling. The optimal architecture is defined as the one which minimizes the generalization error. This paper addresses estimation of the generalization performance of regularized, complete neural network models. Regularization...
Plant Growth Models Using Artificial Neural Networks
Bubenheim, David
1997-01-01
In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.
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...
Constitutive Modelling of INCONEL 718 using Artificial Neural Network
Abiriand Bhekisipho Twala, Olufunminiyi
2017-08-01
Artificial neural network is used to model INCONEL 718 in this paper. The model accounts for precipitate hardening in the alloy. The input variables for the neural network model are strain, strain rate, temperature and microstructure state. The output variable is the flow stress. The early stopping technique is combined with Bayesian regularization process in training the network. Sample and non-sample measurement data were taken from the literature. The model predictions of flow stress of the alloy are in good agreement with experimental measurements.
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
Modeling the Dynamics of Compromised Networks
Energy Technology Data Exchange (ETDEWEB)
Soper, B; Merl, D M
2011-09-12
Accurate predictive models of compromised networks would contribute greatly to improving the effectiveness and efficiency of the detection and control of network attacks. Compartmental epidemiological models have been applied to modeling attack vectors such as viruses and worms. We extend the application of these models to capture a wider class of dynamics applicable to cyber security. By making basic assumptions regarding network topology we use multi-group epidemiological models and reaction rate kinetics to model the stochastic evolution of a compromised network. The Gillespie Algorithm is used to run simulations under a worst case scenario in which the intruder follows the basic connection rates of network traffic as a method of obfuscation.
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.
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.
Directory of Open Access Journals (Sweden)
Dede Sutarya
2014-01-01
Full Text Available Nonlinear system identification is becoming an important tool which can be used to improve control performance and achieve robust fault-tolerant behavior. Among the different nonlinear identification techniques, methods based on neural network model are gradually becoming established not only in the academia, but also in industrial application. An identification scheme of nonlinear systems for sintering furnace temperature in nuclear fuel fabrication using neural network autoregressive with exogenous inputs (NNARX model investigated in this paper. The main contribution of this paper is to identify the appropriate model and structure to be applied in control temperature in the sintering process in nuclear fuel fabrication, that is, a nonlinear dynamical system. Satisfactory agreement between identified and experimental data is found with normalized sum square error 1.9e-03 for heating step and 6.3859e-08 for soaking step. That result shows the model successfully predict the evolution of the temperature in the furnace.
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.
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...
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
An adaptive complex network model for brain functional networks.
Directory of Open Access Journals (Sweden)
Ignacio J Gomez Portillo
Full Text Available Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution.
Modeling gene regulatory networks: A network simplification algorithm
Ferreira, Luiz Henrique O.; de Castro, Maria Clicia S.; da Silva, Fabricio A. B.
2016-12-01
Boolean networks have been used for some time to model Gene Regulatory Networks (GRNs), which describe cell functions. Those models can help biologists to make predictions, prognosis and even specialized treatment when some disturb on the GRN lead to a sick condition. However, the amount of information related to a GRN can be huge, making the task of inferring its boolean network representation quite a challenge. The method shown here takes into account information about the interactome to build a network, where each node represents a protein, and uses the entropy of each node as a key to reduce the size of the network, allowing the further inferring process to focus only on the main protein hubs, the ones with most potential to interfere in overall network behavior.
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.
Temperature-based Instanton Analysis: Identifying Vulnerability in Transmission Networks
Energy Technology Data Exchange (ETDEWEB)
Kersulis, Jonas [Univ. of Michigan, Ann Arbor, MI (United States); Hiskens, Ian [Univ. of Michigan, Ann Arbor, MI (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Bienstock, Daniel [Columbia Univ., New York, NY (United States)
2015-04-08
A time-coupled instanton method for characterizing transmission network vulnerability to wind generation fluctuation is presented. To extend prior instanton work to multiple-time-step analysis, line constraints are specified in terms of temperature rather than current. An optimization formulation is developed to express the minimum wind forecast deviation such that at least one line is driven to its thermal limit. Results are shown for an IEEE RTS-96 system with several wind-farms.
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
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
Марк Миколайович Орел
2015-01-01
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
Modeling Diagnostic Assessments with Bayesian Networks
Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego
2007-01-01
This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…
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.
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...
A personalised thermal comfort model using a Bayesian network
Auffenberg, Frederik; Stein, Sebastian; Rogers, Alex
2015-01-01
In this paper, we address the challenge of predicting optimal comfort temperatures of individual users of a smart heating system. At present, such systems use simple models of user comfort when deciding on a set point temperature. These models generally fail to adapt to an individual user’s preferences, resulting in poor estimates of a user’s preferred temperature. To address this issue, we propose a personalised thermal comfort model that uses a Bayesian network to learn and adapt to a user’...
Dynamic Model of High Temperature PEM Fuel Cell Stack Temperature
DEFF Research Database (Denmark)
Andreasen, Søren Juhl; Kær, Søren Knudsen
2007-01-01
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...... 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 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...
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)
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
The flavoured BFSS model at high temperature
Energy Technology Data Exchange (ETDEWEB)
Asano, Yuhma; Filev, Veselin G. [School of Theoretical Physics, Dublin Institute for Advanced Studies,10 Burlington Road, Dublin 4 (Ireland); Kováčik, Samuel [School of Theoretical Physics, Dublin Institute for Advanced Studies,10 Burlington Road, Dublin 4 (Ireland); Faculty of Mathematics, Physics and Informatics,Comenius University Bratislava, Mlynská dolina, Bratislava, 842 48 (Slovakia); O’Connor, Denjoe [School of Theoretical Physics, Dublin Institute for Advanced Studies,10 Burlington Road, Dublin 4 (Ireland)
2017-01-25
We study the high-temperature series expansion of the Berkooz-Douglas matrix model, which describes the D0/D4-brane system. At high temperature the model is weakly coupled and we develop the series to second order. We check our results against the high-temperature regime of the bosonic model (without fermions) and find excellent agreement. We track the temperature dependence of the bosonic model and find backreaction of the fundamental fields lifts the zero-temperature adjoint mass degeneracy. In the low-temperature phase the system is well described by a gaussian model with three masses m{sub A}{sup t}=1.964±0.003, m{sub A}{sup l}=2.001±0.003 and m{sub f}=1.463±0.001, the adjoint longitudinal and transverse masses and the mass of the fundamental fields respectively.
Object Oriented Modeling Of Social Networks
Zeggelink, Evelien P.H.; Oosten, Reinier van; Stokman, Frans N.
1996-01-01
The aim of this paper is to explain principles of object oriented modeling in the scope of modeling dynamic social networks. As such, the approach of object oriented modeling is advocated within the field of organizational research that focuses on networks. We provide a brief introduction into the
Bayesian estimation of the network autocorrelation model
Dittrich, D.; Leenders, R.T.A.J.; Mulder, J.
2017-01-01
The network autocorrelation model has been extensively used by researchers interested modeling social influence effects in social networks. The most common inferential method in the model is classical maximum likelihood estimation. This approach, however, has known problems such as negative bias of
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...
Molecular modeling of amorphous, non-woven polymer networks.
Krausse, Constantin A; Milek, Theodor; Zahn, Dirk
2015-10-01
We outline a simple and efficient approach to generating molecular models of amorphous polymer networks. Similar to established techniques of preparing woven polymer networks from quenching high-temperature molecular simulation runs, we use a molecular dynamics simulations of a generic melt as starting points. This generic melt is however only used to describe parts of the polymers, namely the cross-linker units which positions are adopted from particle positions of the quenched melt. Specific degrees of network connectivity are tuned by geometric criteria for linker-linker connections and by suitable multi-body interaction potentials applied to the generic melt simulations. Using this technique we demonstrate adjusting fourfold linker coordination in amorphous polymer networks comprising 10-20% under-coordinated linkers. Graphical Abstract Molecular modeling of amorphous, non-woven polymer networks.
Temperature characteristics modeling of Preisach theory
Directory of Open Access Journals (Sweden)
Chen Hao
2017-01-01
Full Text Available This paper proposes a modeling method of the temperature characteristics of Preisach theory. On the basis of the classical Preisach hysteresis model, the Curie temperature, the critical exponent and the ambient temperature are introduced after which the effect of temperature on the magnetic properties of ferromagnetic materials can be accurately reflected. A simulation analysis and a temperature characteristic experiment with silicon steel was carried out. The results are basically the same which proves the validity and the accuracy of the method.
Modeling data throughput on communication networks
Energy Technology Data Exchange (ETDEWEB)
Eldridge, J.M.
1993-11-01
New challenges in high performance computing and communications are driving the need for fast, geographically distributed networks. Applications such as modeling physical phenomena, interactive visualization, large data set transfers, and distributed supercomputing require high performance networking [St89][Ra92][Ca92]. One measure of a communication network`s performance is the time it takes to complete a task -- such as transferring a data file or displaying a graphics image on a remote monitor. Throughput, defined as the ratio of the number of useful data bits transmitted per the time required to transmit those bits, is a useful gauge of how well a communication system meets this performance measure. This paper develops and describes an analytical model of throughput. The model is a tool network designers can use to predict network throughput. It also provides insight into those parts of the network that act as a performance bottleneck.
High-temperature-pressure polymerized resin-infiltrated ceramic networks.
Nguyen, J F; Ruse, D; Phan, A C; Sadoun, M J
2014-01-01
The aim of this study was to produce composite blocks (CB) for CAD/CAM applications by high-temperature-pressure (HT/HP) polymerization of resin-infiltrated glass-ceramic networks. The effect of network sintering and the absence/presence of initiator was investigated. Mechanical properties were determined and compared with those of Paradigm MZ100 (3M ESPE) blocks and HT/HP polymerized experimental "classic" CB, in which the filler had been incorporated by conventional mixing. The networks were made from glass-ceramic powder (VITA Zahnfabrik) formed by slip casting and were either sintered or not. They were silanized, infiltrated by urethane dimethacrylate, with or without initiator, and polymerized under HT/HP (300 MPa, 180°C) to obtain resin-infiltrated glass-ceramic network (RIGCN) CB. HT/HP polymerized CB were also made from an experimental "classic" composite. Flexural strength (σf), fracture toughness (KIC), and Vickers hardness were determined and analyzed by one- or two-way analysis of variance (ANOVA), Scheffé multiple-means comparisons (α = 0.05), and Weibull statistics (for σf). Fractured surfaces were characterized with scanning electron microscopy. The mechanical properties of RIGCN CB were significantly higher. Sintering induced significant increases in σf and hardness, while the initiator significantly decreased hardness. The results suggested that RIGCN and HT/HP polymerization could be used to obtain CB with superior mechanical properties, suitable for CAD/CAM applications.
Tantet, A.J.J.; Dijkstra, H.A.
2014-01-01
On interannual- to multidecadal timescales variability in sea surface temperature appears to be organized in large-scale spatiotemporal patterns. In this paper, we investigate these patterns by studying the community structure of interaction networks constructed from sea surface temperature
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.
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...
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.
Temperature Modelling of the Biomass Pretreatment Process
DEFF Research Database (Denmark)
Prunescu, Remus Mihail; Blanke, Mogens; Jensen, Jakob M.
2012-01-01
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......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...... distribution. Therefore, an accurate temperature model is critical for observing the biomass pretreatment. More than that, the biomass is also pushed with a constant horizontal speed along the reactor in order to ensure a continuous throughput. The goal of this paper is to derive a temperature model...
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
Settings in social networks : A measurement model
Schweinberger, M; Snijders, TAB
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
Spinal Cord Injury Model System Information Network
... the UAB-SCIMS Contact the UAB-SCIMS UAB Spinal Cord Injury Model System Newly Injured Health Daily Living Consumer ... Information Network The University of Alabama at Birmingham Spinal Cord Injury Model System (UAB-SCIMS) maintains this Information Network ...
Radio Channel Modeling in Body Area Networks
An, L.; Bentum, Marinus Jan; Meijerink, Arjan; Scanlon, W.G.
2009-01-01
A body area network (BAN) is a network of bodyworn or implanted electronic devices, including wireless sensors which can monitor body parameters or to de- tect movements. One of the big challenges in BANs is the propagation channel modeling. Channel models can be used to understand wave propagation
Radio channel modeling in body area networks
An, L.; Bentum, Marinus Jan; Meijerink, Arjan; Scanlon, W.G.
2010-01-01
A body area network (BAN) is a network of bodyworn or implanted electronic devices, including wireless sensors which can monitor body parameters or to detect movements. One of the big challenges in BANs is the propagation channel modeling. Channel models can be used to understand wave propagation in
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.
Learning Bayesian Network Model Structure from Data
National Research Council Canada - National Science Library
Margaritis, Dimitris
2003-01-01
In this thesis I address the important problem of the determination of the structure of directed statistical models, with the widely used class of Bayesian network models as a concrete vehicle of my ideas...
NC truck network model development research.
2008-09-01
This research develops a validated prototype truck traffic network model for North Carolina. The model : includes all counties and metropolitan areas of North Carolina and major economic areas throughout the : U.S. Geographic boundaries, population a...
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.
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......A complex network is a systems in which a discrete set of units interact in a quantifiable manner. Representing systems as complex networks have become increasingly popular in a variety of scientific fields including biology, social sciences and economics. Parallel to this development 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...
A Network Formation Model Based on Subgraphs
Chandrasekhar, Arun
2016-01-01
We develop a new class of random-graph models for the statistical estimation of network formation that allow for substantial correlation in links. Various subgraphs (e.g., links, triangles, cliques, stars) are generated and their union results in a network. We provide estimation techniques for recovering the rates at which the underlying subgraphs were formed. We illustrate the models via a series of applications including testing for incentives to form cross-caste relationships in rural India, testing to see whether network structure is used to enforce risk-sharing, testing as to whether networks change in response to a community's exposure to microcredit, and show that these models significantly outperform stochastic block models in matching observed network characteristics. We also establish asymptotic properties of the models and various estimators, which requires proving a new Central Limit Theorem for correlated random variables.
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.
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.
Optimized null model for protein structure networks.
Milenković, Tijana; Filippis, Ioannis; Lappe, Michael; Przulj, Natasa
2009-06-26
Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by
Optimized null model for protein structure networks.
Directory of Open Access Journals (Sweden)
Tijana Milenković
Full Text Available Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model
Towards Reproducible Descriptions of Neuronal Network Models
Nordlie, Eilen; Gewaltig, Marc-Oliver; Plesser, Hans Ekkehard
2009-01-01
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. PMID:19662159
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.
Artificial neural network modeling of dissolved oxygen in reservoir.
Chen, Wei-Bo; Liu, Wen-Cheng
2014-02-01
The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.
Characterization and Modeling of Network Traffic
DEFF Research Database (Denmark)
Shawky, Ahmed; Bergheim, Hans; Ragnarsson, Olafur
2011-01-01
This paper attempts to characterize and model backbone network traffic, using a small number of statistics. In order to reduce cost and processing power associated with traffic analysis. The parameters affecting the behaviour of network traffic are investigated and the choice is that inter......-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....... The model investigates the traffic generation mechanisms, and grouping traffic into flows and applications....
Modeling, Optimization & Control of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat
2014-01-01
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....... 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...... to solve nonlinear optimal control problems. In the water supply system model, the hydraulic resistance of the valve is estimated by real data and it is considered to be a disturbance. The disturbance in our system is updated every 24 hours based on the amount of water usage by consumers every day. Model...
A network model of the interbank market
Li, Shouwei; He, Jianmin; Zhuang, Yaming
2010-12-01
This work introduces a network model of an interbank market based on interbank credit lending relationships. It generates some network features identified through empirical analysis. The critical issue to construct an interbank network is to decide the edges among banks, which is realized in this paper based on the interbank’s degree of trust. Through simulation analysis of the interbank network model, some typical structural features are identified in our interbank network, which are also proved to exist in real interbank networks. They are namely, a low clustering coefficient and a relatively short average path length, community structures, and a two-power-law distribution of out-degree and in-degree.
Model for Microcirculation Transportation Network Design
Directory of Open Access Journals (Sweden)
Qun Chen
2012-01-01
Full Text Available The idea of microcirculation transportation was proposed to shunt heavy traffic on arterial roads through branch roads. The optimization model for designing micro-circulation transportation network was developed to pick out branch roads as traffic-shunting channels and determine their required capacity, trying to minimize the total reconstruction expense and land occupancy subject to saturation and reconstruction space constraints, while accounting for the route choice behaviour of network users. Since micro-circulation transportation network design problem includes both discrete and continuous variables, a discretization method was developed to convert two groups of variables (discrete variables and continuous variables into one group of new discrete variables, transforming the mixed network design problem into a new kind of discrete network design problem with multiple values. The genetic algorithm was proposed to solve the new discrete network design problem. Finally a numerical example demonstrated the efficiency of the model and algorithm.
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.
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.
Finite-temperature field theory and quantum noise in an electrical network
Energy Technology Data Exchange (ETDEWEB)
Garavaglia, T.
1988-10-15
Finite-temperature (0less than or equal toT
Temperature stochastic modeling and weather derivatives pricing ...
African Journals Online (AJOL)
... over a sufficient period to apply a stochastic process that describes the evolution of the temperature. A numerical example of a swap contract pricing is presented, using an approximation formula as well as Monte Carlo simulations. Keywords: Weather derivatives, temperature stochastic model, Monte Carlo simulation.
Random graph models for dynamic networks
Zhang, Xiao; Moore, Cristopher; Newman, Mark E. J.
2017-10-01
Recent theoretical work on the modeling of network structure has focused primarily on networks that are static and unchanging, but many real-world networks change their structure over time. There exist natural generalizations to the dynamic case of many static network models, including the classic random graph, the configuration model, and the stochastic block model, where one assumes that the appearance and disappearance of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes. Here we give an introduction to this class of models, showing for instance how one can compute their equilibrium properties. We also demonstrate their use in data analysis and statistical inference, giving efficient algorithms for fitting them to observed network data using the method of maximum likelihood. This allows us, for example, to estimate the time constants of network evolution or infer community structure from temporal network data using cues embedded both in the probabilities over time that node pairs are connected by edges and in the characteristic dynamics of edge appearance and disappearance. We illustrate these methods with a selection of applications, both to computer-generated test networks and real-world examples.
A regional neural network ensemble for predicting mean daily river water temperature
DeWeber, Jefferson Tyrell; Wagner, Tyler
2014-09-01
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 and land use
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.
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 ...
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.
Stochastic discrete model of karstic networks
Jaquet, O.; Siegel, P.; Klubertanz, G.; Benabderrhamane, H.
Karst aquifers are characterised by an extreme spatial heterogeneity that strongly influences their hydraulic behaviour and the transport of pollutants. These aquifers are particularly vulnerable to contamination because of their highly permeable networks of conduits. A stochastic model is proposed for the simulation of the geometry of karstic networks at a regional scale. The model integrates the relevant physical processes governing the formation of karstic networks. The discrete simulation of karstic networks is performed with a modified lattice-gas cellular automaton for a representative description of the karstic aquifer geometry. Consequently, more reliable modelling results can be obtained for the management and the protection of karst aquifers. The stochastic model was applied jointly with groundwater modelling techniques to a regional karst aquifer in France for the purpose of resolving surface pollution issues.
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....
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
Modelling traffic congestion using queuing networks
Indian Academy of Sciences (India)
Traffic Flow-Density diagrams are obtained using simple Jackson queuing network analysis. Such simple analytical models can be used to capture the effect of non- homogenous traffic. Keywords. Flow-density curves; uninterrupted traffic; Jackson networks. 1. Introduction. Traffic management has become very essential in ...
Lowering the Healing Temperature of Photoswitchable Dynamic Covalent Polymer Networks.
Fuhrmann, Anne; Broi, Kevin; Hecht, Stefan
2018-01-01
To reduce the environmental footprint of the modern society, it is desirable to elongate the lifetime of consumer products, for example by implementing healable coatings and protective layers. However, since most healing processes carried out by heat or light suffer from material degradation, improving the robustness and integrity of healable materials is of tremendous importance to prolong their lifetime. In recent work, a prototype is created of a dynamic covalent polymer network, whose thermal healing ability can be switched "on" and "off" by light to provide a means to locally control repair of a damaged coating. Based on the initial concept, herein a new set of difunctional crosslinkers and linear polymers of various compositions is presented to form dynamic covalent polymer networks, in which the barrier for the retro Diels-Alder decrosslinking reaction is decreased. The approach results in lower healing temperatures and thus a longer lifetime of the material. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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.
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
Model-based control of networked systems
Garcia, Eloy; Montestruque, Luis A
2014-01-01
This monograph introduces a class of networked control systems (NCS) called model-based networked control systems (MB-NCS) and presents various architectures and control strategies designed to improve the performance of NCS. The overall performance of NCS considers the appropriate use of network resources, particularly network bandwidth, in conjunction with the desired response of the system being controlled. The book begins with a detailed description of the basic MB-NCS architecture that provides stability conditions in terms of state feedback updates . It also covers typical problems in NCS such as network delays, network scheduling, and data quantization, as well as more general control problems such as output feedback control, nonlinear systems stabilization, and tracking control. Key features and topics include: Time-triggered and event-triggered feedback updates Stabilization of uncertain systems subject to time delays, quantization, and extended absence of feedback Optimal control analysis and ...
Complex networks repair strategies: Dynamic models
Fu, Chaoqi; Wang, Ying; Gao, Yangjun; Wang, Xiaoyang
2017-09-01
Network repair strategies are tactical methods that restore the efficiency of damaged networks; however, unreasonable repair strategies not only waste resources, they are also ineffective for network recovery. Most extant research on network repair focuses on static networks, but results and findings on static networks cannot be applied to evolutionary dynamic networks because, in dynamic models, complex network repair has completely different characteristics. For instance, repaired nodes face more severe challenges, and require strategic repair methods in order to have a significant effect. In this study, we propose the Shell Repair Strategy (SRS) to minimize the risk of secondary node failures due to the cascading effect. Our proposed method includes the identification of a set of vital nodes that have a significant impact on network repair and defense. Our identification of these vital nodes reduces the number of switching nodes that face the risk of secondary failures during the dynamic repair process. This is positively correlated with the size of the average degree 〈 k 〉 and enhances network invulnerability.
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.
Gene Regulation Networks for Modeling Drosophila Development
Mjolsness, E.
1999-01-01
This chapter will very briefly introduce and review some computational experiments in using trainable gene regulation network models to simulate and understand selected episodes in the development of the fruit fly, Drosophila Melanogaster.
Graphical Model Theory for Wireless Sensor Networks
Energy Technology Data Exchange (ETDEWEB)
Davis, William B.
2002-12-08
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.
Mitigating risk during strategic supply network modeling
Müssigmann, Nikolaus
2006-01-01
Mitigating risk during strategic supply network modeling. - In: Managing risks in supply chains / ed. by Wolfgang Kersten ... - Berlin : Schmidt, 2006. - S. 213-226. - (Operations and technology management ; 1)
Road maintenance planning using network flow modelling
Yang, Chao; Remenyte-Prescott, Rasa; Andrews, John
2015-01-01
This paper presents a road maintenance planning model that can be used to balance out maintenance cost and road user cost, since performing road maintenance at night can be convenient for road users but costly for highway agency. Based on the platform of the network traffic flow modelling, the traffic through the worksite and its adjacent road links is evaluated. Thus, maintenance arrangements at a worksite can be optimized considering the overall network performance. In addition, genetic alg...
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.
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.
Posterior Predictive Model Checking in Bayesian Networks
Crawford, Aaron
2014-01-01
This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…
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.
The high temperature Ising model is a critical percolation model
Meester, R.W.J.; Camia, F.; Balint, A.
2010-01-01
We define a new percolation model by generalising the FK representation of the Ising model, and show that on the triangular lattice and at high temperatures, the critical point in the new model corresponds to the Ising model. Since the new model can be viewed as Bernoulli percolation on a random
A simple model for studying interacting networks
Liu, Wenjia; Jolad, Shivakumar; Schmittmann, Beate; Zia, R. K. P.
2011-03-01
Many specific physical networks (e.g., internet, power grid, interstates), have been characterized in considerable detail, but in isolation from each other. Yet, each of these networks supports the functions of the others, and so far, little is known about how their interactions affect their structure and functionality. To address this issue, we consider two coupled model networks. Each network is relatively simple, with a fixed set of nodes, but dynamically generated set of links which has a preferred degree, κ . In the stationary state, the degree distribution has exponential tails (far from κ), an attribute which we can explain. Next, we consider two such networks with different κ 's, reminiscent of two social groups, e.g., extroverts and introverts. Finally, we let these networks interact by establishing a controllable fraction of cross links. The resulting distribution of links, both within and across the two model networks, is investigated and discussed, along with some potential consequences for real networks. Supported in part by NSF-DMR-0705152 and 1005417.
Modeling gene regulatory network motifs using Statecharts.
Fioravanti, Fabio; Helmer-Citterich, Manuela; Nardelli, Enrico
2012-03-28
Gene regulatory networks are widely used by biologists to describe the interactions among genes, proteins and other components at the intra-cellular level. Recently, a great effort has been devoted to give gene regulatory networks a formal semantics based on existing computational frameworks.For this purpose, we consider Statecharts, which are a modular, hierarchical and executable formal model widely used to represent software systems. We use Statecharts for modeling small and recurring patterns of interactions in gene regulatory networks, called motifs. We present an improved method for modeling gene regulatory network motifs using Statecharts and we describe the successful modeling of several motifs, including those which could not be modeled or whose models could not be distinguished using the method of a previous proposal.We model motifs in an easy and intuitive way by taking advantage of the visual features of Statecharts. Our modeling approach is able to simulate some interesting temporal properties of gene regulatory network motifs: the delay in the activation and the deactivation of the "output" gene in the coherent type-1 feedforward loop, the pulse in the incoherent type-1 feedforward loop, the bistability nature of double positive and double negative feedback loops, the oscillatory behavior of the negative feedback loop, and the "lock-in" effect of positive autoregulation. We present a Statecharts-based approach for the modeling of gene regulatory network motifs in biological systems. The basic motifs used to build more complex networks (that is, simple regulation, reciprocal regulation, feedback loop, feedforward loop, and autoregulation) can be faithfully described and their temporal dynamics can be analyzed.
Neural network approaches for noisy language modeling.
Li, Jun; Ouazzane, Karim; Kazemian, Hassan B; Afzal, Muhammad Sajid
2013-11-01
Text entry from people is not only grammatical and distinct, but also noisy. For example, a user's typing stream contains all the information about the user's interaction with computer using a QWERTY keyboard, which may include the user's typing mistakes as well as specific vocabulary, typing habit, and typing performance. In particular, these features are obvious in disabled users' typing streams. This paper proposes a new concept called noisy language modeling by further developing information theory and applies neural networks to one of its specific application-typing stream. This paper experimentally uses a neural network approach to analyze the disabled users' typing streams both in general and specific ways to identify their typing behaviors and subsequently, to make typing predictions and typing corrections. In this paper, a focused time-delay neural network (FTDNN) language model, a time gap model, a prediction model based on time gap, and a probabilistic neural network model (PNN) are developed. A 38% first hitting rate (HR) and a 53% first three HR in symbol prediction are obtained based on the analysis of a user's typing history through the FTDNN language modeling, while the modeling results using the time gap prediction model and the PNN model demonstrate that the correction rates lie predominantly in between 65% and 90% with the current testing samples, and 70% of all test scores above basic correction rates, respectively. The modeling process demonstrates that a neural network is a suitable and robust language modeling tool to analyze the noisy language stream. The research also paves the way for practical application development in areas such as informational analysis, text prediction, and error correction by providing a theoretical basis of neural network approaches for noisy language modeling.
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.
Telestroke network business model strategies.
Fanale, Christopher V; Demaerschalk, Bart M
2012-10-01
Our objective is to summarize the evidence that supports the reliability of telemedicine for diagnosis and efficacy in acute stroke treatment, identify strategies for funding the development of a telestroke network, and to present issues with respect to economic sustainability, cost effectiveness, and the status of reimbursement for telestroke. Copyright © 2012 National Stroke Association. Published by Elsevier Inc. All rights reserved.
Drinking Water Temperature Modelling in Domestic Systems
Moerman, A.; Blokker, M.; Vreeburg, J.; van der Hoek, J.P.
2014-01-01
Domestic water supply systems are the final stage of the transport process to deliver potable water to the customers’ tap. Under the influence of temperature, residence time and pipe materials the drinking water quality can change while the water passes the domestic drinking water system. According to the Dutch Drinking Water Act the drinking water temperature may not exceed the 25 °C threshold at point-of-use level. This paper provides a mathematical approach to model the heating of drinking...
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.
Markov State Models of gene regulatory networks.
Chu, Brian K; Tse, Margaret J; Sato, Royce R; Read, Elizabeth L
2017-02-06
Gene regulatory networks with dynamics characterized by multiple stable states underlie cell fate-decisions. Quantitative models that can link molecular-level knowledge of gene regulation to a global understanding of network dynamics have the potential to guide cell-reprogramming strategies. Networks are often modeled by the stochastic Chemical Master Equation, but methods for systematic identification of key properties of the global dynamics are currently lacking. The method identifies the number, phenotypes, and lifetimes of long-lived states for a set of common gene regulatory network models. Application of transition path theory to the constructed Markov State Model decomposes global dynamics into a set of dominant transition paths and associated relative probabilities for stochastic state-switching. In this proof-of-concept study, we found that the Markov State Model provides a general framework for analyzing and visualizing stochastic multistability and state-transitions in gene networks. Our results suggest that this framework-adopted from the field of atomistic Molecular Dynamics-can be a useful tool for quantitative Systems Biology at the network scale.
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
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
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
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.
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.
Interpolation of climate variables and temperature modeling
Samanta, Sailesh; Pal, Dilip Kumar; Lohar, Debasish; Pal, Babita
2012-01-01
Geographic Information Systems (GIS) and modeling are becoming powerful tools in agricultural research and natural resource management. This study proposes an empirical methodology for modeling and mapping of the monthly and annual air temperature using remote sensing and GIS techniques. The study area is Gangetic West Bengal and its neighborhood in the eastern India, where a number of weather systems occur throughout the year. Gangetic West Bengal is a region of strong heterogeneous surface with several weather disturbances. This paper also examines statistical approaches for interpolating climatic data over large regions, providing different interpolation techniques for climate variables' use in agricultural research. Three interpolation approaches, like inverse distance weighted averaging, thin-plate smoothing splines, and co-kriging are evaluated for 4° × 4° area, covering the eastern part of India. The land use/land cover, soil texture, and digital elevation model are used as the independent variables for temperature modeling. Multiple regression analysis with standard method is used to add dependent variables into regression equation. Prediction of mean temperature for monsoon season is better than winter season. Finally standard deviation errors are evaluated after comparing the predicted temperature and observed temperature of the area. For better improvement, distance from the coastline and seasonal wind pattern are stressed to be included as independent variables.
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
A Transfer Learning Approach for Network Modeling
Huang, Shuai; Li, Jing; Chen, Kewei; Wu, Teresa; Ye, Jieping; Wu, Xia; Yao, Li
2012-01-01
Networks models have been widely used in many domains to characterize the interacting relationship between physical entities. A typical problem faced is to identify the networks of multiple related tasks that share some similarities. In this case, a transfer learning approach that can leverage the knowledge gained during the modeling of one task to help better model another task is highly desirable. In this paper, we propose a transfer learning approach, which adopts a Bayesian hierarchical model framework to characterize task relatedness and additionally uses the L1-regularization to ensure robust learning of the networks with limited sample sizes. A method based on the Expectation-Maximization (EM) algorithm is further developed to learn the networks from data. Simulation studies are performed, which demonstrate the superiority of the proposed transfer learning approach over single task learning that learns the network of each task in isolation. The proposed approach is also applied to identification of brain connectivity networks of Alzheimer’s disease (AD) from functional magnetic resonance image (fMRI) data. The findings are consistent with the AD literature. PMID:24526804
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.
Modelling complex networks by random hierarchical graphs
Directory of Open Access Journals (Sweden)
M.Wróbel
2008-06-01
Full Text Available Numerous complex networks contain special patterns, called network motifs. These are specific subgraphs, which occur oftener than in randomized networks of Erdős-Rényi type. We choose one of them, the triangle, and build a family of random hierarchical graphs, being Sierpiński gasket-based graphs with random "decorations". We calculate the important characteristics of these graphs - average degree, average shortest path length, small-world graph family characteristics. They depend on probability of decorations. We analyze the Ising model on our graphs and describe its critical properties using a renormalization-group technique.
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.
Deep space network software cost estimation model
Tausworthe, R. C.
1981-01-01
A parametric software cost estimation model prepared for Jet PRopulsion Laboratory (JPL) Deep Space Network (DSN) Data System implementation tasks is described. The resource estimation mdel modifies and combines a number of existing models. The model calibrates the task magnitude and difficulty, development environment, and software technology effects through prompted responses to a set of approximately 50 questions. Parameters in the model are adjusted to fit JPL software life-cycle statistics.
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.
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
Neural networks as models of psychopathology.
Aakerlund, L; Hemmingsen, R
1998-04-01
Neural network modeling is situated between neurobiology, cognitive science, and neuropsychology. The structural and functional resemblance with biological computation has made artificial neural networks (ANN) useful for exploring the relationship between neurobiology and computational performance, i.e., cognition and behavior. This review provides an introduction to the theory of ANN and how they have linked theories from neurobiology and psychopathology in schizophrenia, affective disorders, and dementia.
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.
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...
Empirical generalization assessment of neural network models
DEFF Research Database (Denmark)
Larsen, Jan; Hansen, Lars Kai
1995-01-01
This paper addresses the assessment of generalization performance of neural network models by use of empirical techniques. We suggest to use the cross-validation scheme combined with a resampling technique to obtain an estimate of the generalization performance distribution of a specific model...
Evaluation of EOR Processes Using Network Models
DEFF Research Database (Denmark)
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...
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.
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.
Models of network reliability analysis, combinatorics, and Monte Carlo
Gertsbakh, Ilya B
2009-01-01
Unique in its approach, Models of Network Reliability: Analysis, Combinatorics, and Monte Carlo provides a brief introduction to Monte Carlo methods along with a concise exposition of reliability theory ideas. From there, the text investigates a collection of principal network reliability models, such as terminal connectivity for networks with unreliable edges and/or nodes, network lifetime distribution in the process of its destruction, network stationary behavior for renewable components, importance measures of network elements, reliability gradient, and network optimal reliability synthesis
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.
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
Personalized Learning Network Teaching Model
Feng, Zhou
Adaptive learning system on the salient features, expounded personalized learning is adaptive learning system adaptive to learners key to learning. From the perspective of design theory, put forward an adaptive learning system to learn design thinking individual model, and using data mining techniques, the initial establishment of personalized adaptive systems model of learning.
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.
Model Microvascular Networks Can Have Many Equilibria.
Karst, Nathaniel J; Geddes, John B; Carr, Russell T
2017-03-01
We show that large microvascular networks with realistic topologies, geometries, boundary conditions, and constitutive laws can exhibit many steady-state flow configurations. This is in direct contrast to most previous studies which have assumed, implicitly or explicitly, that a given network can only possess one equilibrium state. While our techniques are general and can be applied to any network, we focus on two distinct network types that model human tissues: perturbed honeycomb networks and random networks generated from Voronoi diagrams. We demonstrate that the disparity between observed and predicted flow directions reported in previous studies might be attributable to the presence of multiple equilibria. We show that the pathway effect, in which hematocrit is steadily increased along a series of diverging junctions, has important implications for equilibrium discovery, and that our estimates of the number of equilibria supported by these networks are conservative. If a more complete description of the plasma skimming effect that captures red blood cell allocation at junctions with high feed hematocrit were to be obtained empirically, then the number of equilibria found by our approach would at worst remain the same and would in all likelihood increase significantly.
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
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.
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.
Two-temperature models for nitrogen dissociation
da Silva, M. Lino; Guerra, V.; Loureiro, J.
2007-12-01
Accurate sets of nitrogen state-resolved dissociation rates have been reduced to two-temperature (translational T and vibrational Tv) dissociation rates. The analysis of such two-temperature dissociation rates shows evidence of two different dissociation behaviors. For Tv 0.3 T dissociation proceeds predominantly form the near-dissociative vibrational levels, with an abrupt change of behavior at Tv = 0.3 T. These two-temperature sets have then been utilized as a benchmark for the comparison against popular multitemperature dissociation models (Park, Hansen, Marrone-Treanor, Hammerling, Losev-Shatalov, Gordiets, Kuznetsov, and Macheret-Fridman). This has allowed verifying the accuracy of each theoretical model, and additionally proposing adequate values for any semi-empirical parameters present in the different theories. The Macheret-Fridman model, who acknowledges the existence of the two aforementioned dissociation regimes, has been found to provide significantly more accurate results than the other models. Although these different theoretical approaches have been tested and validated solely for nitrogen dissociation processes, it is reasonable to expect that the general conclusions of this work, regarding the adequacy of the different dissociation models, could be extended to the description of arbitrary diatomic dissociation processes.
Phase transitions in Ising models on directed networks.
Lipowski, Adam; Ferreira, António Luis; Lipowska, Dorota; Gontarek, Krzysztof
2015-11-01
We examine Ising models with heat-bath dynamics on directed networks. Our simulations show that Ising models on directed triangular and simple cubic lattices undergo a phase transition that most likely belongs to the Ising universality class. On the directed square lattice the model remains paramagnetic at any positive temperature as already reported in some previous studies. We also examine random directed graphs and show that contrary to undirected ones, percolation of directed bonds does not guarantee ferromagnetic ordering. Only above a certain threshold can a random directed graph support finite-temperature ferromagnetic ordering. Such behavior is found also for out-homogeneous random graphs, but in this case the analysis of magnetic and percolative properties can be done exactly. Directed random graphs also differ from undirected ones with respect to zero-temperature freezing. Only at low connectivity do they remain trapped in a disordered configuration. Above a certain threshold, however, the zero-temperature dynamics quickly drives the model toward a broken symmetry (magnetized) state. Only above this threshold, which is almost twice as large as the percolation threshold, do we expect the Ising model to have a positive critical temperature. With a very good accuracy, the behavior on directed random graphs is reproduced within a certain approximate scheme.
Spiking modular neural networks: A neural network modeling approach for hydrological processes
National Research Council Canada - National Science Library
Kamban Parasuraman; Amin Elshorbagy; Sean K. Carey
2006-01-01
.... In this study, a novel neural network model called the spiking modular neural networks (SMNNs) is proposed. An SMNN consists of an input layer, a spiking layer, and an associator neural network layer...
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.
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".
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".
Algebraic Statistics for Network Models
2014-02-19
use algebra, combinatorics and Markov bases to give a constructing way of answering this question for ERGMs of interest. Question 2: How do we model...for every function. 06/06/13 Petrović. Manuscripts 8, 10. Invited lecture at the Scientific Session on Commutative Algebra and Combinatorics at the
Network Modeling and Simulation (NEMSE)
2013-07-01
Prioritized Packet Fragmentation", IEEE Trans. Multimedia , Oct. 2012. [13 SYSENG] . Defense Acquisition Guidebook, Chapter 4 System Engineering, and...2012 IEEE High Performance Extreme Computing Conference (HPEC) poster session [1 Ross]. Motivation Air Force Research Lab needs o Capability...is virtual. These eight virtualizations were: System-in-the-Loop (SITL) using OPNET Modeler, COPE, Field Programmable Gate Array ( FPGA Physical
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.
Lan Liu; Ryan K. L. Ko; Guangming Ren; Xiaoping Xu
2017-01-01
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 ne...
Summing up dynamics: modelling biological processes in variable temperature scenarios
Tijskens, L.M.M.; Verdenius, F.
2000-01-01
The interest of modelling biological processes with dynamically changing external conditions (temperature, relative humidity, gas conditions) increases. Several modelling approaches are currently available. Among them are approaches like modelling under standard conditions, temperature sum models
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.
Adaptive elastic networks as models of supercooled liquids
Yan, Le; Wyart, Matthieu
2015-08-01
The thermodynamics and dynamics of supercooled liquids correlate with their elasticity. In particular for covalent networks, the jump of specific heat is small and the liquid is strong near the threshold valence where the network acquires rigidity. By contrast, the jump of specific heat and the fragility are large away from this threshold valence. In a previous work [Proc. Natl. Acad. Sci. USA 110, 6307 (2013), 10.1073/pnas.1300534110], we could explain these behaviors by introducing a model of supercooled liquids in which local rearrangements interact via elasticity. However, in that model the disorder characterizing elasticity was frozen, whereas it is itself a dynamic variable in supercooled liquids. Here we study numerically and theoretically adaptive elastic network models where polydisperse springs can move on a lattice, thus allowing for the geometry of the elastic network to fluctuate and evolve with temperature. We show numerically that our previous results on the relationship between structure and thermodynamics hold in these models. We introduce an approximation where redundant constraints (highly coordinated regions where the frustration is large) are treated as an ideal gas, leading to analytical predictions that are accurate in the range of parameters relevant for real materials. Overall, these results lead to a description of supercooled liquids, in which the distance to the rigidity transition controls the number of directions in phase space that cost energy and the specific heat.
Yurkovich, James T; Zielinski, Daniel C; Yang, Laurence; Paglia, Giuseppe; Rolfsson, Ottar; Sigurjónsson, Ólafur E; Broddrick, Jared T; Bordbar, Aarash; Wichuk, Kristine; Brynjólfsson, Sigurður; Palsson, Sirus; Gudmundsson, Sveinn; Palsson, Bernhard O
2017-12-01
The temperature dependence of biological processes has been studied at the levels of individual biochemical reactions and organism physiology (e.g. basal metabolic rates) but has not been examined at the metabolic network level. Here, we used a systems biology approach to characterize the temperature dependence of the human red blood cell (RBC) metabolic network between 4 and 37 °C through absolutely quantified exo- and endometabolomics data. We used an Arrhenius-type model (Q10) to describe how the rate of a biochemical process changes with every 10 °C change in temperature. Multivariate statistical analysis of the metabolomics data revealed that the same metabolic network-level trends previously reported for RBCs at 4 °C were conserved but accelerated with increasing temperature. We calculated a median Q10 coefficient of 2.89 ± 1.03, within the expected range of 2-3 for biological processes, for 48 individual metabolite concentrations. We then integrated these metabolomics measurements into a cell-scale metabolic model to study pathway usage, calculating a median Q10 coefficient of 2.73 ± 0.75 for 35 reaction fluxes. The relative fluxes through glycolysis and nucleotide metabolism pathways were consistent across the studied temperature range despite the non-uniform distributions of Q10 coefficients of individual metabolites and reaction fluxes. Together, these results indicate that the rate of change of network-level responses to temperature differences in RBC metabolism is consistent between 4 and 37 °C. More broadly, we provide a baseline characterization of a biochemical network given no transcriptional or translational regulation that can be used to explore the temperature dependence of metabolism. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.
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.
Exergy and Energy Analysis of Low Temperature District Heating Network
DEFF Research Database (Denmark)
Li, Hongwei; Svendsen, Svend
. 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...
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.
Propagation models for computing biochemical reaction networks
Henzinger, Thomas A; Mateescu, Maria
2011-01-01
We introduce propagation models, a formalism designed to support general and efficient data structures for the transient analysis of biochemical reaction networks. We give two use cases for propagation abstract data types: the uniformization method and numerical integration. We also sketch an implementation of a propagation abstract data type, which uses abstraction to approximate states.
Modelling crime linkage with Bayesian networks
de Zoete, J.; Sjerps, M.; Lagnado, D.; Fenton, N.
2015-01-01
When two or more crimes show specific similarities, such as a very distinct modus operandi, the probability that they were committed by the same offender becomes of interest. This probability depends on the degree of similarity and distinctiveness. We show how Bayesian networks can be used to model
Lagrangian modeling of switching electrical networks
Scherpen, Jacquelien M.A.; Jeltsema, Dimitri; Klaassens, J. Ben
2003-01-01
In this paper, a general and systematic method is presented to model topologically complete electrical networks, with or without multiple or single switches, within the Euler–Lagrange framework. Apart from the physical insight that can be obtained in this way, the framework has proven to be useful
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
Modeling Network Transition Constraints with Hypergraphs
DEFF Research Database (Denmark)
Harrod, Steven
2011-01-01
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...
A neural network model for texture discrimination.
Xing, J; Gerstein, G L
1993-01-01
A model of texture discrimination in visual cortex was built using a feedforward network with lateral interactions among relatively realistic spiking neural elements. The elements have various membrane currents, equilibrium potentials and time constants, with action potentials and synapses. The model is derived from the modified programs of MacGregor (1987). Gabor-like filters are applied to overlapping regions in the original image; the neural network with lateral excitatory and inhibitory interactions then compares and adjusts the Gabor amplitudes in order to produce the actual texture discrimination. Finally, a combination layer selects and groups various representations in the output of the network to form the final transformed image material. We show that both texture segmentation and detection of texture boundaries can be represented in the firing activity of such a network for a wide variety of synthetic to natural images. Performance details depend most strongly on the global balance of strengths of the excitatory and inhibitory lateral interconnections. The spatial distribution of lateral connective strengths has relatively little effect. Detailed temporal firing activities of single elements in the lateral connected network were examined under various stimulus conditions. Results show (as in area 17 of cortex) that a single element's response to image features local to its receptive field can be altered by changes in the global context.
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.
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...... 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...... efficiently combines distributed learner models without the need to exchange internal structure of local Bayesian networks, nor local evidence between the involved platforms....
Network traffic model using GIPP and GIBP
Lee, Yong Duk; Van de Liefvoort, Appie; Wallace, Victor L.
1998-10-01
In telecommunication networks, the correlated nature of teletraffic patterns can have significant impact on queuing measures such as queue length, blocking and delay. There is, however, not yet a good general analytical description which can easily incorporate the correlation effect of the traffic, while at the same time maintaining the ease of modeling. The authors have shown elsewhere, that the covariance structures of the generalized Interrupted Poisson Process (GIPP) and the generalized Interrupted Bernoulli Process (GIBP) have an invariance property which makes them reasonably general, yet algebraically manageable, models for representing correlated network traffic. The GIPP and GIBP have a surprisingly rich sets of parameters, yet these invariance properties enable us to easily incorporate the covariance function as well as the interarrival time distribution into the model to better matchobservations. In this paper, we show an application of GIPP and GIBP for matching an analytical model to observed or experimental data.
Modeling Low-temperature Geochemical Processes
Nordstrom, D. K.
2003-12-01
Geochemical modeling has become a popular and useful tool for a wide number of applications from research on the fundamental processes of water-rock interactions to regulatory requirements and decisions regarding permits for industrial and hazardous wastes. In low-temperature environments, generally thought of as those in the temperature range of 0-100 °C and close to atmospheric pressure (1 atm=1.01325 bar=101,325 Pa), complex hydrobiogeochemical reactions participate in an array of interconnected processes that affect us, and that, in turn, we affect. Understanding these complex processes often requires tools that are sufficiently sophisticated to portray multicomponent, multiphase chemical reactions yet transparent enough to reveal the main driving forces. Geochemical models are such tools. The major processes that they are required to model include mineral dissolution and precipitation; aqueous inorganic speciation and complexation; solute adsorption and desorption; ion exchange; oxidation-reduction; or redox; transformations; gas uptake or production; organic matter speciation and complexation; evaporation; dilution; water mixing; reaction during fluid flow; reaction involving biotic interactions; and photoreaction. These processes occur in rain, snow, fog, dry atmosphere, soils, bedrock weathering, streams, rivers, lakes, groundwaters, estuaries, brines, and diagenetic environments. Geochemical modeling attempts to understand the redistribution of elements and compounds, through anthropogenic and natural means, for a large range of scale from nanometer to global. "Aqueous geochemistry" and "environmental geochemistry" are often used interchangeably with "low-temperature geochemistry" to emphasize hydrologic or environmental objectives.Recognition of the strategy or philosophy behind the use of geochemical modeling is not often discussed or explicitly described. Plummer (1984, 1992) and Parkhurst and Plummer (1993) compare and contrast two approaches for
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...... 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
Modeling Nitrogen Processing in Northeast US River Networks
Whittinghill, K. A.; Stewart, R.; Mineau, M.; Wollheim, W. M.; Lammers, R. B.
2013-12-01
Due to increased nitrogen (N) pollution from anthropogenic sources, the need for aquatic ecosystem services such as N removal has also increased. River networks provide a buffering mechanism that retains or removes anthropogenic N inputs. However, the effectiveness of N removal in rivers may decline with increased loading and, consequently, excess N is eventually delivered to estuaries. We used a spatially distributed river network N removal model developed within the Framework for Aquatic Modeling in the Earth System (FrAMES) to examine the geography of N removal capacity of Northeast river systems under various land use and climate conditions. FrAMES accounts for accumulation and routing of runoff, water temperatures, and serial biogeochemical processing using reactivity derived from the Lotic Intersite Nitrogen Experiment (LINX2). Nonpoint N loading is driven by empirical relationships with land cover developed from previous research in Northeast watersheds. Point source N loading from wastewater treatment plants is estimated as a function of the population served and the volume of water discharged. We tested model results using historical USGS discharge data and N data from historical grab samples and recently initiated continuous measurements from in-situ aquatic sensors. Model results for major Northeast watersheds illustrate hot spots of ecosystem service activity (i.e. N removal) using high-resolution maps and basin profiles. As expected, N loading increases with increasing suburban or agricultural land use area. Network scale N removal is highest during summer and autumn when discharge is low and river temperatures are high. N removal as the % of N loading increases with catchment size and decreases with increasing N loading, suburban land use, or agricultural land use. Catchments experiencing the highest network scale N removal generally have N inputs (both point and non-point sources) located in lower order streams. Model results can be used to better
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....... Such systems are known to be stabilized by spatial structure. Finally, I analyse data from a large mobile phone network and show that people who are topologically close in the network have similar communication patterns. This main part of the thesis is based on six different articles, which I have co...
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...
Yang, Kun; Chen, Yingying; Qin, Jun; Lu, Hui
2017-04-01
Multi-sphere interactions over the Tibetan Plateau directly impact its surrounding climate and environment at a variety of spatiotemporal scales. Remote sensing and modeling are expected to provide hydro-meteorological data needed for these process studies, but in situ observations are required to support their calibration and validation. For this purpose, we have established two networks on the Tibetan Plateau to measure densely two state variables (soil moisture and temperature) and four soil depths (0 5, 10, 20, and 40 cm). The experimental area is characterized by low biomass, high soil moisture dynamic range, and typical freeze-thaw cycle. As auxiliary parameters of these networks, soil texture and soil organic carbon content are measured at each station to support further studies. In order to guarantee continuous and high-quality data, tremendous efforts have been made to protect the data logger from soil water intrusion, to calibrate soil moisture sensors, and to upscale the point measurements. One soil moisture network is located in a semi-humid area in central Tibetan Plateau (Naqu), which consists of 56 stations with their elevation varying over 4470 4950 m and covers three spatial scales (1.0, 0.3, 0.1 degree). The other is located in a semi-arid area in southern Tibetan Plateau (Pali), which consists of 25 stations and covers an area of 0.25 degree. The spatiotemporal characteristics of the former network were analyzed, and a new spatial upscaling method was developed to obtain the regional mean soil moisture truth from the point measurements. Our networks meet the requirement for evaluating a variety of soil moisture products, developing new algorithms, and analyzing soil moisture scaling. Three applications with the network data are presented in this paper. 1. Evaluation of Current remote sensing and LSM products. The in situ data have been used to evaluate AMSR-E, AMSR2, SMOS and SMAP products and four modeled outputs by the Global Land Data
Chaotic Simulated Annealing by A Neural Network Model with Transient Chaos
Chen, L; Chen, Luonan; Aihara, Kazuyuki
1997-01-01
We propose a neural network model with transient chaos, or a transiently chaotic neural network (TCNN) as an approximation method for combinatorial optimization problem, by introducing transiently chaotic dynamics into neural networks. Unlike conventional neural networks only with point attractors, the proposed neural network has richer and more flexible dynamics, so that it can be expected to have higher ability of searching for globally optimal or near-optimal solutions. A significant property of this model is that the chaotic neurodynamics is temporarily generated for searching and self-organizing, and eventually vanishes with autonomous decreasing of a bifurcation parameter corresponding to the "temperature" in usual annealing process. Therefore, the neural network gradually approaches, through the transient chaos, to dynamical structure similar to such conventional models as the Hopfield neural network which converges to a stable equilibrium point. Since the optimization process of the transiently chaoti...
Neural Network Model of memory retrieval
Directory of Open Access Journals (Sweden)
Stefano eRecanatesi
2015-12-01
Full Text Available Human memory can store large amount of information. Nevertheless, recalling is often achallenging task. In a classical free recall paradigm, where participants are asked to repeat abriefly presented list of words, people make mistakes for lists as short as 5 words. We present amodel for memory retrieval based on a Hopfield neural network where transition between itemsare determined by similarities in their long-term memory representations. Meanfield analysis ofthe model reveals stable states of the network corresponding (1 to single memory representationsand (2 intersection between memory representations. We show that oscillating feedback inhibitionin the presence of noise induces transitions between these states triggering the retrieval ofdifferent memories. The network dynamics qualitatively predicts the distribution of time intervalsrequired to recall new memory items observed in experiments. It shows that items having largernumber of neurons in their representation are statistically easier to recall and reveals possiblebottlenecks in our ability of retrieving memories. Overall, we propose a neural network model ofinformation retrieval broadly compatible with experimental observations and is consistent with ourrecent graphical model (Romani et al., 2013.
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.
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.
Directory of Open Access Journals (Sweden)
S.O. Starkov
2017-06-01
Full Text Available Reactors with heavy water coolants and moderators have been used extensively in today's power industry. Monitoring of the moderator condition plays an important role in ensuring normal operation of a power plant. A cellular neural network, the architecture of which has been adapted for hardware implementation, is proposed for use in a system for prediction of the heavy water moderator temperature. A reactor model composed in accordance with the CANDU Darlington heavy water reactor design was used to form the training sample collection and to control correct operation of the neural network structure. The sample components for the adjustment and configuration of the network topology include key parameters that characterize the energy generation process in the core. The paper considers the feasibility of the temperature prediction only for the calandria's central cross-section. To solve this problem, the cellular neural network architecture has been designed, and major parts of the digital computational element and methods for their implementation based on an FPLD have also been developed. The method is described for organizing an optical coupling between individual neural modules within the network, which enables not only the restructuring of the topology in the training process, but also the assignment of priorities for the propagation of the information signals of neurons depending on the activity in a situation analysis at the neural network structure inlet. Asynchronous activation of cells was used based on an oscillating fractal network, the basis for which was a modified ring oscillator. The efficiency of training the proposed architecture using stochastic diffusion search algorithms is evaluated. A comparative analysis of the model behavior and the results of the neural network operation have shown that the use of the neural network approach is effective in safety systems of power plants.
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.
Letcher, Benjamin; Hocking, Daniel; O'Neil, Kyle; Whiteley, Andrew R.; Nislow, Keith H.; O'Donnell, Matthew
2016-01-01
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.
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
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
Kinematic Structural Modelling in Bayesian Networks
Schaaf, Alexander; de la Varga, Miguel; Florian Wellmann, J.
2017-04-01
We commonly capture our knowledge about the spatial distribution of distinct geological lithologies in the form of 3-D geological models. Several methods exist to create these models, each with its own strengths and limitations. We present here an approach to combine the functionalities of two modeling approaches - implicit interpolation and kinematic modelling methods - into one framework, while explicitly considering parameter uncertainties and thus model uncertainty. In recent work, we proposed an approach to implement implicit modelling algorithms into Bayesian networks. This was done to address the issues of input data uncertainty and integration of geological information from varying sources in the form of geological likelihood functions. However, one general shortcoming of implicit methods is that they usually do not take any physical constraints into consideration, which can result in unrealistic model outcomes and artifacts. On the other hand, kinematic structural modelling intends to reconstruct the history of a geological system based on physically driven kinematic events. This type of modelling incorporates simplified, physical laws into the model, at the cost of a substantial increment of usable uncertain parameters. In the work presented here, we show an integration of these two different modelling methodologies, taking advantage of the strengths of both of them. First, we treat the two types of models separately, capturing the information contained in the kinematic models and their specific parameters in the form of likelihood functions, in order to use them in the implicit modelling scheme. We then go further and combine the two modelling approaches into one single Bayesian network. This enables the direct flow of information between the parameters of the kinematic modelling step and the implicit modelling step and links the exclusive input data and likelihoods of the two different modelling algorithms into one probabilistic inference framework. In
Systems biology of plant molecular networks: from networks to models
Valentim, F.L.
2015-01-01
Developmental processes are controlled by regulatory networks (GRNs), which are tightly coordinated networks of transcription factors (TFs) that activate and repress gene expression within a spatial and temporal context. In Arabidopsis thaliana, the key components and network structures of the GRNs
Ferreira, Pedro M; Gomes, João M; Martins, Igor A C; Ruano, António E
2012-11-12
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.
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
Directory of Open Access Journals (Sweden)
Pedro M. Ferreira
2012-11-01
Full Text Available Accurate measurements of global solar radiation and atmospheric temperature,as well as the availability of the predictions of their evolution over time, are importantfor different areas of applications, such as agriculture, renewable energy and energymanagement, or thermal comfort in buildings. For this reason, an intelligent, light-weightand portable sensor was developed, using artificial neural network models as the time-seriespredictor mechanisms. These have been identified with the aid of a procedure based on themulti-objective genetic algorithm. As cloudiness is the most significant factor affecting thesolar radiation reaching a particular location on the Earth surface, it has great impact on theperformance of predictive solar radiation models for that location. This work also representsone step towards the improvement of such models by using ground-to-sky hemisphericalcolour digital images as a means to estimate cloudiness by the fraction of visible skycorresponding to clouds and to clear sky. The implementation of predictive models inthe prototype has been validated and the system is able to function reliably, providingmeasurements and four-hour forecasts of cloudiness, solar radiation and air temperature.
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.
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....
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.
A NEURAL OSCILLATOR-NETWORK MODEL OF TEMPORAL PATTERN GENERATION
Schomaker, Lambert
Most contemporary neural network models deal with essentially static, perceptual problems of classification and transformation. Models such as multi-layer feedforward perceptrons generally do not incorporate time as an essential dimension, whereas biological neural networks are inherently temporal
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
Rodríguez, J; Clemente, G; Sanjuán, N; Bon, J
2014-01-01
The drying kinetics of thyme was analyzed by considering different conditions: air temperature of between 40°C and 70°C , and air velocity of 1 m/s. A theoretical diffusion model and eight different empirical models were fitted to the experimental data. From the theoretical model application, the effective diffusivity per unit area of the thyme was estimated (between 3.68 × 10(-5) and 2.12 × 10 (-4) s(-1)). The temperature dependence of the effective diffusivity was described by the Arrhenius relationship with activation energy of 49.42 kJ/mol. Eight different empirical models were fitted to the experimental data. Additionally, the dependence of the parameters of each model on the drying temperature was determined, obtaining equations that allow estimating the evolution of the moisture content at any temperature in the established range. Furthermore, artificial neural networks were developed and compared with the theoretical and empirical models using the percentage of the relative errors and the explained variance. The artificial neural networks were found to be more accurate predictors of moisture evolution with VAR ≥ 99.3% and ER ≤ 8.7%.
Temperature-controlled metal/ligand stoichiometric ratio in Ag-TCNE coordination networks
Energy Technology Data Exchange (ETDEWEB)
Rodríguez-Fernández, Jonathan [Departamento de Física de la Materia Condensada, Universidad Autónoma de Madrid, Cantoblanco, 28049 Madrid (Spain); Lauwaet, Koen [IMDEA Nanoscience, c\\Faraday 9, Campus de Cantoblanco, 28049 Madrid (Spain); Herranz, Maria Ángeles [Departamento de Química Orgánica, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, Ciudad Universitaria s/n, Madrid (Spain); Martín, Nazario [IMDEA Nanoscience, c\\Faraday 9, Campus de Cantoblanco, 28049 Madrid (Spain); Departamento de Química Orgánica, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, Ciudad Universitaria s/n, Madrid (Spain); Gallego, José María [IMDEA Nanoscience, c\\Faraday 9, Campus de Cantoblanco, 28049 Madrid (Spain); Instituto de Ciencia de Materiales de Madrid, ICMM, CSIC, Sor Juana Inés de la Cruz s\; Miranda, Rodolfo; Otero, Roberto [Departamento de Física de la Materia Condensada, Universidad Autónoma de Madrid, Cantoblanco, 28049 Madrid (Spain); IMDEA Nanoscience, c\\Faraday 9, Campus de Cantoblanco, 28049 Madrid (Spain)
2015-03-14
The deposition of tetracyanoethylene (TCNE) on Ag(111), both at Room Temperature (RT, 300 K) and low temperatures (150 K), leads to the formation of coordination networks involving silver adatoms, as revealed by Variable-Temperature Scanning Tunneling Microscopy. Our results indicate that TCNE molecules etch away material from the step edges and possibly also from the terraces, which facilitates the formation of the observed coordination networks. Moreover, such process is temperature dependent, which allows for different stoichiometric ratios between Ag and TCNE just by adjusting the deposition temperature. X-ray Photoelectron Spectroscopy and Density Functional Theory calculations reveal that charge-transfer from the surface to the molecule and the concomitant geometrical distortions at both sides of the organic/inorganic interface might facilitate the extraction of silver atoms from the step-edges and, thus, its incorporation into the observed TCNE coordination networks.
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...
High Temperature Superconductivity in Cuprates: a model
Silva, P R
2010-01-01
A model is proposed such that quasi-particles (electrons or holes) residing in the CuO2 planes of cuprates may interact leading to metallic or superconducting behaviors. The metallic phase is obtained when the quasi-particles are treated as having classical kinetic energies and the superconducting phase occurs when the quasi-particles are taken as extremely relativistic objects. The interaction between both kinds of particles is provided by a force dependent-on-velocity. In the case of the superconducting behavior, the motion of apical oxygen ions provides the glue to establish the Cooper pair. The model furnishes explicit relations for the Fermi velocity, the perpendicular and the in-plane coherence lengths, the zero-temperature energy gap, the critical current density, the critical parallel and perpendicular magnetic fields. All these mentioned quantities are expressed in terms of fundamental physical constants as: charge and mass of the electron, light velocity in vacuum, Planck constant, electric permitti...
Model of Opinion Spreading in Social Networks
Kanovsky, Igor
2011-01-01
We proposed a new model, which capture the main difference between information and opinion spreading. In information spreading additional exposure to certain information has a small effect. Contrary, when an actor is exposed to 2 opinioned actors the probability to adopt the opinion is significant higher than in the case of contact with one such actor (called by J. Kleinberg "the 0-1-2 effect"). In each time step if an actor does not have an opinion, we randomly choose 2 his network neighbors. If one of them has an opinion, the actor adopts opinion with some low probability, if two - with a higher probability. Opinion spreading was simulated on different real world social networks and similar random scale-free networks. The results show that small world structure has a crucial impact on tipping point time. The "0-1-2" effect causes a significant difference between ability of the actors to start opinion spreading. Actor is an influencer according to his topological position in the network.
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.
Mathematical model for spreading dynamics of social network worms
Sun, Xin; Liu, Yan-Heng; Li, Bin; Li, Jin; Han, Jia-Wei; Liu, Xue-Jie
2012-04-01
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.
Modeling regulatory networks with weight matrices
DEFF Research Database (Denmark)
Weaver, D.C.; Workman, Christopher; Stormo, Gary D.
1999-01-01
Systematic gene expression analyses provide comprehensive information about the transcriptional responseto different environmental and developmental conditions. With enough gene expression data points,computational biologists may eventually generate predictive computer models of transcription...... regulation.Such models will require computational methodologies consistent with the behavior of known biologicalsystems that remain tractable. We represent regulatory relationships between genes as linear coefficients orweights, with the "net" regulation influence on a gene's expression being...... the mathematical summation of theindependent regulatory inputs. Test regulatory networks generated with this approach display stable andcyclically stable gene expression levels, consistent with known biological systems. We include variables tomodel the effect of environmental conditions on transcription regulation...
Artificial Neural Network Modeling of an Inverse Fluidized Bed ...
African Journals Online (AJOL)
The application of neural networks to model a laboratory scale inverse fluidized bed reactor has been studied. 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 ...
Modeling social influence through network autocorrelation : constructing the weight matrix
Leenders, Roger Th. A. J.
Many physical and social phenomena are embedded within networks of interdependencies, the so-called 'context' of these phenomena. In network analysis, this type of process is typically modeled as a network autocorrelation model. Parameter estimates and inferences based on autocorrelation models,
Hsieh, Chih-Sheng; Lee, Lung fei
2017-01-01
In this paper, we model network formation and network interactions under a unified framework. The key feature of our model is to allow individuals to respond to incentives stemming from interaction benefits on certain activities when they choose friends (network links), while capturing homophily in terms of unobserved characteristic variables in network formation and activities. There are two advantages of this modeling approach: first, one can evaluate whether incentives from certain interac...
Challenges on Probabilistic Modeling for Evolving Networks
Ding, Jianguo; Bouvry, Pascal
2013-01-01
With the emerging of new networks, such as wireless sensor networks, vehicle networks, P2P networks, cloud computing, mobile Internet, or social networks, the network dynamics and complexity expands from system design, hardware, software, protocols, structures, integration, evolution, application, even to business goals. Thus the dynamics and uncertainty are unavoidable characteristics, which come from the regular network evolution and unexpected hardware defects, unavoidable software errors,...
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....
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 cellular regulation of metabolism.
DEFF Research Database (Denmark)
Park, Byung Sik; Imran, Muhammad; Hoon, Im-Yong
2017-01-01
A secondary distribution network of a low temperature district heating system is designed and optimized for a residential apartment complex under the local conditions of South Korea in the TRNSYS simulation environment. The residential apartment complex is a typical example of Korean residential...... °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...
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...
Neural Network Program Package for Prosody Modeling
Directory of Open Access Journals (Sweden)
J. Santarius
2004-04-01
Full Text Available This contribution describes the programme for one part of theautomatic Text-to-Speech (TTS synthesis. Some experiments (for example[14] documented the considerable improvement of the naturalness ofsynthetic speech, but this approach requires completing the inputfeature values by hand. This completing takes a lot of time for bigfiles. We need to improve the prosody by other approaches which useonly automatically classified features (input parameters. Theartificial neural network (ANN approach is used for the modeling ofprosody parameters. The program package contains all modules necessaryfor the text and speech signal pre-processing, neural network training,sensitivity analysis, result processing and a module for the creationof the input data protocol for Czech speech synthesizer ARTIC [1].
Towards an evolutionary model of transcription networks.
Directory of Open Access Journals (Sweden)
Dan Xie
2011-06-01
Full Text Available DNA evolution models made invaluable contributions to comparative genomics, although it seemed formidable to include non-genomic features into these models. In order to build an evolutionary model of transcription networks (TNs, we had to forfeit the substitution model used in DNA evolution and to start from modeling the evolution of the regulatory relationships. We present a quantitative evolutionary model of TNs, subjecting the phylogenetic distance and the evolutionary changes of cis-regulatory sequence, gene expression and network structure to one probabilistic framework. Using the genome sequences and gene expression data from multiple species, this model can predict regulatory relationships between a transcription factor (TF and its target genes in all species, and thus identify TN re-wiring events. Applying this model to analyze the pre-implantation development of three mammalian species, we identified the conserved and re-wired components of the TNs downstream to a set of TFs including Oct4, Gata3/4/6, cMyc and nMyc. Evolutionary events on the DNA sequence that led to turnover of TF binding sites were identified, including a birth of an Oct4 binding site by a 2nt deletion. In contrast to recent reports of large interspecies differences of TF binding sites and gene expression patterns, the interspecies difference in TF-target relationship is much smaller. The data showed increasing conservation levels from genomic sequences to TF-DNA interaction, gene expression, TN, and finally to morphology, suggesting that evolutionary changes are larger at molecular levels and smaller at functional levels. The data also showed that evolutionarily older TFs are more likely to have conserved target genes, whereas younger TFs tend to have larger re-wiring rates.
Contributions and challenges for network models in cognitive neuroscience.
Sporns, Olaf
2014-05-01
The confluence of new approaches in recording patterns of brain connectivity and quantitative analytic tools from network science has opened new avenues toward understanding the organization and function of brain networks. Descriptive network models of brain structural and functional connectivity have made several important contributions; for example, in the mapping of putative network hubs and network communities. Building on the importance of anatomical and functional interactions, network models have provided insight into the basic structures and mechanisms that enable integrative neural processes. Network models have also been instrumental in understanding the role of structural brain networks in generating spatially and temporally organized brain activity. Despite these contributions, network models are subject to limitations in methodology and interpretation, and they face many challenges as brain connectivity data sets continue to increase in detail and complexity.
Modeling of regional warehouse network generation
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Popov Pavel Vladimirovich
2016-08-01
Full Text Available One of the factors that has a significant impact on the socio-economic development of the Russian Federation’s regions is the logistics infrastructure. It provides integrated transportation and distribution service of material flows. One of the main elements of logistics infrastructure is a storage infrastructure, which includes distribution center, distribution-and-sortout and sortout warehouses. It is the most expedient to place distribution center in the vicinity of the regional center. One of the tasks of the distribution network creation within the regions of the Russian Federation is to determine the location, capacity and number of stores. When determining regional network location of general purpose warehouses methodological approaches to solving the problems of location of production and non-production can be used which depend on various economic factors. The mathematical models for solving relevant problems are the deployment models. However, the existing models focus on the dimensionless power storage. The purpose of the given work is to develop a model to determine the optimal location of general-purpose warehouses on the Russian Federation area. At the first stage of the work, the authors assess the main economic indicators influencing the choice of the location of general purpose warehouses. An algorithm for solving the first stage, based on ABC, discriminant and cluster analysis were proposed by the authors in earlier papers. At the second stage the specific locations of general purpose warehouses and their power is chosen to provide the cost minimization for the construction and subsequent maintenance of warehouses and transportation heterogeneous products. In order to solve this problem the authors developed a mathematical model that takes into account the possibility of delivery in heterogeneous goods from suppliers and manufacturers in the distribution and storage sorting with specified set of capacities. The model allows
Computing the viscosity of supercooled liquids: Markov Network model.
Directory of Open Access Journals (Sweden)
Ju Li
Full Text Available The microscopic origin of glass transition, when liquid viscosity changes continuously by more than ten orders of magnitude, is challenging to explain from first principles. Here we describe the detailed derivation and implementation of a Markovian Network model to calculate the shear viscosity of deeply supercooled liquids based on numerical sampling of an atomistic energy landscape, which sheds some light on this transition. Shear stress relaxation is calculated from a master-equation description in which the system follows a transition-state pathway trajectory of hopping among local energy minima separated by activation barriers, which is in turn sampled by a metadynamics-based algorithm. Quantitative connection is established between the temperature variation of the calculated viscosity and the underlying potential energy and inherent stress landscape, showing a different landscape topography or "terrain" is needed for low-temperature viscosity (of order 10(7 Pa·s from that associated with high-temperature viscosity (10(-5 Pa·s. Within this range our results clearly indicate the crossover from an essentially Arrhenius scaling behavior at high temperatures to a low-temperature behavior that is clearly super-Arrhenius (fragile for a Kob-Andersen model of binary liquid. Experimentally the manifestation of this crossover in atomic dynamics continues to raise questions concerning its fundamental origin. In this context this work explicitly demonstrates that a temperature-dependent "terrain" characterizing different parts of the same potential energy surface is sufficient to explain the signature behavior of vitrification, at the same time the notion of a temperature-dependent effective activation barrier is quantified.
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.
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).
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.
Optimizing neural network models: motivation and case studies
Harp, S A; T. Samad
2012-01-01
Practical successes have been achieved with neural network models in a variety of domains, including energy-related industry. The large, complex design space presented by neural networks is only minimally explored in current practice. The satisfactory results that nevertheless have been obtained testify that neural networks are a robust modeling technology; at the same time, however, the lack of a systematic design approach implies that the best neural network models generally rem...
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.
Inferring gene regression networks with model trees
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Aguilar-Ruiz Jesus S
2010-10-01
Full Text Available Abstract Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear
Directory of Open Access Journals (Sweden)
Morshed Khandaker
2016-01-01
Full Text Available Nutrient conduit networks can be introduced within the Polyethylene Glycol Diacrylate (PEGDA tissue construct to enable cells to survive in the scaffold. Nutrient conduit networks can be created on PEGDA by macrochannel to nanochannel fabrication techniques. Such networks can influence the mechanical and cell activities of PEGDA scaffold. There is no study conducted to evaluate the effect of nutrient conduit networks on the maximum tensile stress and cell activities of the tissue scaffold. The study aimed to explore the influence of the network architecture on the maximum tensile stress of PEGDA scaffold and compared with the nonnetworked PEGDA scaffold. Our study found that there are 1.78 and 2.23 times decrease of maximum tensile stress due to the introduction of nutrient conduit networks to the PEGDA scaffold at 23°C and 37°C temperature conditions, respectively. This study also found statistically significant effect of network architecture, PI concentration, temperature, and wait time on the maximum failure stress of PEGDA samples (P value < 0.05. Cell viability results demonstrated that networked PEGDA hydrogels possessed increased viability compared to nonnetworked and decreased viability with increased photoinitiator concentrations. The results of this study can be used for the design of PEGDA scaffold with macrosize nutrient conduit network channels.
Two stage neural network modelling for robust model predictive control.
Patan, Krzysztof
2017-11-02
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.
Performance of a Predictive Model for Calculating Ascent Time to a Target Temperature
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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.
A possible temperature measurement model for fuel cell
Yu, Qiaoling; Zhang, Pu; Mao, Wenping; Liu, Wenzhong
2017-11-01
In this paper, an improved temperature measuring model for fuel cell temperature measurement is proposed based on the existed nanothermometer model, which is regarded as traditional temperature measuring model. With more realistic cases taken into consideration, the results of the improved model are more practical and accurate compared with the traditional one. Limited by the existed experimental conditions, this paper emphases on simulating the different conditions of the temperature distribution inside SOFC. As a result, the experiments are carried out with similar temperature distribution but under relatively lower temperatures, which can come to similar conclusions as by simulation.
Marsman, M; Borsboom, D; Kruis, J; Epskamp, S; van Bork, R; Waldorp, L J; Maas, H L J van der; Maris, G
2017-11-07
In recent years, network models have been proposed as an alternative representation of psychometric constructs such as depression. In such models, the covariance between observables (e.g., symptoms like depressed mood, feelings of worthlessness, and guilt) is explained in terms of a pattern of causal interactions between these observables, which contrasts with classical interpretations in which the observables are conceptualized as the effects of a reflective latent variable. However, few investigations have been directed at the question how these different models relate to each other. To shed light on this issue, the current paper explores the relation between one of the most important network models-the Ising model from physics-and one of the most important latent variable models-the Item Response Theory (IRT) model from psychometrics. The Ising model describes the interaction between states of particles that are connected in a network, whereas the IRT model describes the probability distribution associated with item responses in a psychometric test as a function of a latent variable. Despite the divergent backgrounds of the models, we show a broad equivalence between them and also illustrate several opportunities that arise from this connection.
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.
The Prediction of Concrete Temperature during Curing Using Regression and Artificial Neural Network
Directory of Open Access Journals (Sweden)
Zahra Najafi
2013-01-01
Full Text Available Cement hydration plays a vital role in the temperature development of early-age concrete due to the heat generation. Concrete temperature affects the workability, and its measurement is an important element in any quality control program. In this regard, a method, which estimates the concrete temperature during curing, is very valuable. In this paper, multivariable regression and neural network methods were used for estimating concrete temperature. In order to achieve this purpose, ten laboratory cylindrical specimens were prepared under controlled situation, and concrete temperature was measured by thermistors existent in vibrating wire strain gauges. Input data variables consist of time (hour, environment temperature, water to cement ratio, aggregate content, height, and specimen diameter. Concrete temperature has been measured in ten different concrete specimens. Nonlinear regression achieved the determined coefficient ( of 0.873. By using the same input set, the artificial neural network predicted concrete temperature with higher of 0.999. The results show that artificial neural network method significantly can be used to predict concrete temperature when regression results do not have appropriate accuracy.
Neural Networks For Electrohydrodynamic Effect Modelling
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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.
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
A network-oriented business modeling environment
Bisconti, Cristian; Storelli, Davide; Totaro, Salvatore; Arigliano, Francesco; Savarino, Vincenzo; Vicari, Claudia
The development of formal models related to the organizational aspects of an enterprise is fundamental when these aspects must be re-engineered and digitalized, especially when the enterprise is involved in the dynamics and value flows of a business network. Business modeling provides an opportunity to synthesize and make business processes, business rules and the structural aspects of an organization explicit, allowing business managers to control their complexity and guide an enterprise through effective decisional and strategic activities. This chapter discusses the main results of the TEKNE project in terms of software components that enable enterprises to configure, store, search and share models of any aspects of their business while leveraging standard and business-oriented technologies and languages to bridge the gap between the world of business people and IT experts and to foster effective business-to-business collaborations.
Analytic Models of High-Temperature Hohlraums
Energy Technology Data Exchange (ETDEWEB)
Stygar, W.A.; Olson, R.E.; Spielman, R.B.; Leeper, R.J.
2000-11-29
A unified set of high-temperature-hohlraum models has been developed. For a simple hohlraum, P{sub s} = [A{sub s}+(1{minus}{alpha}{sub W})A{sub W}+A{sub H}]{sigma}T{sub R}{sup 4} + (4V{sigma}/c)(dT{sub R}{sup r}/dt) where P{sub S} is the total power radiated by the source, A{sub s} is the source area, A{sub W} is the area of the cavity wall excluding the source and holes in the wall, A{sub H} is the area of the holes, {sigma} is the Stefan-Boltzmann constant, T{sub R} is the radiation brightness temperature, V is the hohlraum volume, and c is the speed of light. The wall albedo {alpha}{sub W} {triple_bond} (T{sub W}/T{sub R}){sup 4} where T{sub W} is the brightness temperature of area A{sub W}. The net power radiated by the source P{sub N} = P{sub S}-A{sub S}{sigma}T{sub R}{sup 4}, which suggests that for laser-driven hohlraums the conversion efficiency {eta}{sub CE} be defined as P{sub N}/P{sub LASER}. The characteristic time required to change T{sub R}{sup 4} in response to a change in P{sub N} is 4V/C[(l{minus}{alpha}{sub W})A{sub W}+A{sub H}]. Using this model, T{sub R}, {alpha}{sub W}, and {eta}{sub 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{sub N} = {l_brace}(1{minus}{alpha}{sub W})A{sub W}+A{sub H}+[(1{minus}{alpha}{sub C})(A{sub S}+A{sub W}{alpha}{sub W})A{sub C}/A{sub T}]{r_brace}{sigma}T{sub RC}{sup 4} where {alpha}{sub C} is the capsule albedo, A{sub C} is the capsule area, A{sub T} {triple_bond} (A{sub S}+A{sub W}+A{sub H}), and T{sub 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.
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%.
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.
Some queuing network models of computer systems
Herndon, E. S.
1980-01-01
Queuing network models of a computer system operating with a single workload type are presented. Program algorithms are adapted for use on the Texas Instruments SR-52 programmable calculator. By slightly altering the algorithm to process the G and H matrices row by row instead of column by column, six devices and an unlimited job/terminal population could be handled on the SR-52. Techniques are also introduced for handling a simple load dependent server and for studying interactive systems with fixed multiprogramming limits.
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.
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
Stochastic simulation of HIV population dynamics through complex network modelling
Sloot, P. M. A.; Ivanov, S. V.; Boukhanovsky, A. V.; van de Vijver, D. A. M. C.; Boucher, C. A. B.
We propose a new way to model HIV infection spreading through the use of dynamic complex networks. The heterogeneous population of HIV exposure groups is described through a unique network degree probability distribution. The time evolution of the network nodes is modelled by a Markov process and
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 c...
Stochastic simulation of HIV population dynamics through complex network modelling
Sloot, P.M.A.; Ivanov, S.V.; Boukhanovsky, A.V.; van de Vijver, D.A.M.C.; Boucher, C.A.B.
2008-01-01
We propose a new way to model HIV infection spreading through the use of dynamic complex networks. The heterogeneous population of HIV exposure groups is described through a unique network degree probability distribution. The time evolution of the network nodes is modelled by a Markov process and
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.
VEPCO network model reconciliation of LANL and MZA model data
Energy Technology Data Exchange (ETDEWEB)
NONE
1992-12-15
The LANL DC load flow model of the VEPCO transmission network shows 210 more substations than the AC load flow model produced by MZA utility Consultants. MZA was requested to determine the source of the difference. The AC load flow model used for this study utilizes 2 standard network algorithms (Decoupled or Newton). The solution time of each is affected by the number of substations. The more substations included, the longer the model will take to solve. In addition, the ability of the algorithms to converge to a solution is affected by line loadings and characteristics. Convergence is inhibited by numerous lightly loaded and electrically short lines. The MZA model reduces the total substations to 343 by creating equivalent loads and generation. Most of the omitted substations are lightly loaded and rated at 115 kV. The MZA model includes 16 substations not included in the LANL model. These represent new generation including Non-Utility Generator (NUG) sites, additional substations and an intertie (Wake, to CP and L). This report also contains data from the Italian State AC power flow model and the Duke Power Company AC flow model.
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.
A Model of Genetic Variation in Human Social Networks
Fowler, James H; Christakis, Nicholas A
2008-01-01
Social networks influence the evolution of cooperation and they exhibit strikingly systematic patterns across a wide range of human contexts. Both of these facts suggest that variation in the topological attributes of human social networks might have a genetic basis. While genetic variation accounts for a significant portion of the variation in many complex social behaviors, the heritability of egocentric social network attributes is unknown. Here we show that three of these attributes (in-degree, transitivity, and centrality) are heritable. We then develop a "mirror network" method to test extant network models and show that none accounts for observed genetic variation in human social networks. We propose an alternative "attract and introduce" model that generates significant heritability as well as other important network features, and we show that this model with two simple forms of heterogeneity is well suited to the modeling of real social networks in humans. These results suggest that natural selection ...
Neural Network Based Model of an Industrial Oil-Fired Boiler System ...
African Journals Online (AJOL)
In this study, an oil-fired boiler system is modeled as a multivariable plant with two inputs (feed water rate and oil-fired flow rate) and two outputs (steam temperature and pressure). The plant parameters are modeled using artificial neural network, based on experimental data collected directly from the physical plant.
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...
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
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.
Modelling highly variable daily maximum water temperatures in a ...
African Journals Online (AJOL)
... hourly water temperatures were used to calculate daily maximum water temperatures for nine sites within the Sabie-Sand River system, Mpumalanga Province, South Africa. A suite of statistical models for simulating daily maximum water temperatures, of differing complexity and using inputs of air temperature, flow rates, ...
Directory of Open Access Journals (Sweden)
Mathilde Tassin
Full Text Available The development of CAD-CAM techniques called for new materials suited to this technique and offering a safe and sustainable clinical implementation. The infiltration of resin in a ceramic network under high pressure and high temperature defines a new class of hybrid materials, namely polymer infiltrated ceramics network (PICN, for this purpose which requires to be evaluated biologically. We used oral stem cells (gingival and pulpal as an in vitro experimental model.Four biomaterials were grinded, immersed in a culture medium and deposed on stem cells from dental pulp (DPSC and gingiva (GSC: Enamic (VITA®, Experimental Hybrid Material (EHM, EHM with initiator (EHMi and polymerized Z100™ composite material (3M®. After 7 days of incubation; viability, apoptosis, proliferation, cytoskeleton, inflammatory response and morphology were evaluated in vitro.Proliferation was insignificantly delayed by all the tested materials. Significant cytotoxicity was observed in presence of resin based composites (MTT assay, however no detectable apoptosis and some dead cells were detected like in PICN materials. Cell morphology, major cytoskeleton and extracellular matrix components were not altered. An intimate contact appeared between the materials and cells.The three new tested biomaterials did not exhibit adverse effects on oral stem cells in our experimental conditions and may be an interesting alternative to ceramics or composite based CAD-CAM blocks.
Slow dynamics in a primitive tetrahedral network model.
De Michele, Cristiano; Tartaglia, Piero; Sciortino, Francesco
2006-11-28
We report extensive Monte Carlo and event-driven molecular dynamics simulations of the fluid and liquid phase of a primitive model for silica recently introduced by Ford et al. [J. Chem. Phys. 121, 8415 (2004)]. We evaluate the isodiffusivity lines in the temperature-density plane to provide an indication of the shape of the glass transition line. Except for large densities, arrest is driven by the onset of the tetrahedral bonding pattern and the resulting dynamics is strong in Angell's classification scheme [J. Non-Cryst. Solids 131-133, 13 (1991)]. We compare structural and dynamic properties with corresponding results of two recently studied primitive models of network forming liquids-a primitive model for water and an angular-constraint-free model of four-coordinated particles-to pin down the role of the geometric constraints associated with bonding. Eventually we discuss the similarities between "glass" formation in network forming liquids and "gel" formation in colloidal dispersions of patchy particles.
A scale-free neural network for modelling neurogenesis
Perotti, Juan I.; Tamarit, Francisco A.; Cannas, Sergio A.
2006-11-01
In this work we introduce a neural network model for associative memory based on a diluted Hopfield model, which grows through a neurogenesis algorithm that guarantees that the final network is a small-world and scale-free one. We also analyze the storage capacity of the network and prove that its performance is larger than that measured in a randomly dilute network with the same connectivity.
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.
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.
Determining Application Runtimes Using Queueing Network Modeling
Energy Technology Data Exchange (ETDEWEB)
Elliott, Michael L. [Univ. of San Francisco, CA (United States)
2006-12-14
Determination of application times-to-solution for large-scale clustered computers continues to be a difficult problem in high-end computing, which will only become more challenging as multi-core consumer machines become more prevalent in the market. Both researchers and consumers of these multi-core systems desire reasonable estimates of how long their programs will take to run (time-to-solution, or TTS), and how many resources will be consumed in the execution. Currently there are few methods of determining these values, and those that do exist are either overly simplistic in their assumptions or require great amounts of effort to parameterize and understand. One previously untried method is queuing network modeling (QNM), which is easy to parameterize and solve, and produces results that typically fall within 10 to 30% of the actual TTS for our test cases. Using characteristics of the computer network (bandwidth, latency) and communication patterns (number of messages, message length, time spent in communication), the QNM model of the NAS-PB CG application was applied to MCR and ALC, supercomputers at LLNL, and the Keck Cluster at USF, with average errors of 2.41%, 3.61%, and -10.73%, respectively, compared to the actual TTS observed. While additional work is necessary to improve the predictive capabilities of QNM, current results show that QNM has a great deal of promise for determining application TTS for multi-processor computer systems.
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
Modeling stochasticity in biochemical reaction networks
Constantino, P. H.; Vlysidis, M.; Smadbeck, P.; Kaznessis, Y. N.
2016-03-01
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.
Modelling of A Trust and Reputation Model in Wireless Networks
Directory of Open Access Journals (Sweden)
Saurabh Mishra
2015-09-01
Full Text Available Security is the major challenge for Wireless Sensor Networks (WSNs. The sensor nodes are deployed in non controlled environment, facing the danger of information leakage, adversary attacks and other threats. Trust and Reputation models are solutions for this problem and to identify malicious, selfish and compromised nodes. This paper aims to evaluate varying collusion effect with respect to static (SW, dynamic (DW, static with collusion (SWC, dynamic with collusion (DWC and oscillating wireless sensor networks to derive the joint resultant of Eigen Trust Model. An attempt has been made for the same by comparing aforementioned networks that are purely dedicated to protect the WSNs from adversary attacks and maintain the security issues. The comparison has been made with respect to accuracy and path length and founded that, collusion for wireless sensor networks seems intractable with the static and dynamic WSNs when varied with specified number of fraudulent nodes in the scenario. Additionally, it consumes more energy and resources in oscillating and collusive environments.
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.
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.
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
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...... 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...... more than 80 per cent of the sky that are to a high degree uncorrelated with the foreground signals. A single network will be able to cover the dynamic range of the Planck noise level over the entire sky....
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...... from the DH network. Sensitivity analysis was carried out in order to evaluate the area of applicability of the proposed method. Hence varied values of the original capacity and the current capacity of the existing radiators were evaluated with the design temperature values that were defined by two...
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 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 ...
A temperature dependent slip factor based thermal model for friction ...
Indian Academy of Sciences (India)
Abstract. 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 ...
Simulating canopy temperature for modelling heat stress in cereals
Crop models must be improved to account for the large effects of heat stress effects on crop yields. To date, most approaches in crop models use air temperature despite evidence that crop canopy temperature better explains yield reductions associated with high temperature events. This study presents...
CNMO: Towards the Construction of a Communication Network Modelling Ontology
Rahman, Muhammad Azizur; Pakstas, Algirdas; Wang, Frank Zhigang
Ontologies that explicitly identify objects, properties, and relationships in specific domains are essential for collaboration that involves sharing of data, knowledge or resources. A communications network modelling ontology (CNMO) has been designed to represent a network model as well as aspects related to its development and actual network operation. Network nodes/sites, link, traffic sources, protocols as well as aspects of the modeling/simulation scenario and operational aspects are defined with their formal representation. A CNMO may be beneficial for various network design/simulation/research communities due to the uniform representation of network models. This ontology is designed using terminology and concepts from various network modeling, simulation and topology generation tools.
Topological evolution of virtual social networks by modeling social activities
Sun, Xin; Dong, Junyu; Tang, Ruichun; Xu, Mantao; Qi, Lin; Cai, Yang
2015-09-01
With the development of Internet and wireless communication, virtual social networks are becoming increasingly important in the formation of nowadays' social communities. Topological evolution model is foundational and critical for social network related researches. Up to present most of the related research experiments are carried out on artificial networks, however, a study of incorporating the actual social activities into the network topology model is ignored. This paper first formalizes two mathematical abstract concepts of hobbies search and friend recommendation to model the social actions people exhibit. Then a social activities based topology evolution simulation model is developed to satisfy some well-known properties that have been discovered in real-world social networks. Empirical results show that the proposed topology evolution model has embraced several key network topological properties of concern, which can be envisioned as signatures of real social networks.
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.
An Efficient Multitask Scheduling Model for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Hongsheng Yin
2014-01-01
Full Text Available The sensor nodes of multitask wireless network are constrained in performance-driven computation. Theoretical studies on the data processing model of wireless sensor nodes suggest satisfying the requirements of high qualities of service (QoS of multiple application networks, thus improving the efficiency of network. In this paper, we present the priority based data processing model for multitask sensor nodes in the architecture of multitask wireless sensor network. The proposed model is deduced with the M/M/1 queuing model based on the queuing theory where the average delay of data packets passing by sensor nodes is estimated. The model is validated with the real data from the Huoerxinhe Coal Mine. By applying the proposed priority based data processing model in the multitask wireless sensor network, the average delay of data packets in a sensor nodes is reduced nearly to 50%. The simulation results show that the proposed model can improve the throughput of network efficiently.
Vehicle Scheduling with Network Flow Models
Directory of Open Access Journals (Sweden)
Gustavo P. Silva
2010-04-01
Full Text Available
Este trabalho retrata a primeira fase de uma pesquisa de doutorado voltada para a utilização de modelos de fluxo em redes para programação de veículos (de ônibus, em particular. A utilização de modelos deste tipo ainda e muito pouco explorada na literatura, principalmente pela dificuldade imposta pelo grande numero de variáveis resultante. Neste trabalho são apresentadas formulações para tratamento do problema de programação de veículos associados a um único depósito (ou garagem como problema de fluxo em redes, incluindo duas técnicas para reduzir o numero de arcos na rede criada e, conseqüentemente, o numero de variáveis a tratar. Uma destas técnicas de redução de arcos foi implementada e o problema de fluxo resultante foi direcionado para ser resolvido, nesta fase da pesquisa, por uma versão disponível do algoritmo Simplex para redes. Problemas teste baseados em dados reais da cidade de Reading, UK, foram resolvidos com a utilização da formulação de fluxo em redes adotada, e os resultados comparados com aqueles obtidos pelo método heurístico BOOST, o qual tem sido largamente testado e comercializado pela School of Computer Studies da Universidade de Leeds, UK. Os resultados alcançados demonstram a possibilidade de tratamento de problemas reais com a técnica de redução de arcos.
ABSTRACT
This paper presents the successful results of a first phase of a doctoral research addressed to solving vehicle (bus, in particular scheduling problems through network flow formulations. Network flow modeling for this kind of problem is a promising, but not a well explored approach, mainly because of the large number of variables related to number of arcs of real case networks. The paper presents and discusses some network flow formulations for the single depot bus vehicle scheduling problem, along with two techniques of arc reduction. One of these arc reduction techniques has been implemented and the underlying
Bicriteria Models of Vehicles Recycling Network Facility Location
Merkisz-Guranowska, Agnieszka
2012-06-01
The paper presents the issues related to modeling of a vehicle recycling network. The functioning of the recycling network is within the realm of interest of a variety of government agendas, companies participating in the network, vehicle manufacturers and vehicle end users. The interests of these groups need to be considered when deciding about the network organization. The paper presents bicriteria models of network entity location that take into account the preferences of the vehicle owners and network participants related to the network construction and reorganization. A mathematical formulation of the optimization tasks has been presented including the objective functions and limitations that the solutions have to comply with. Then, the models were used for the network optimization in Poland.
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.
Natural Models for Evolution on Networks
Mertzios, George B; Raptopoulos, Christoforos; Spirakis, Paul G
2011-01-01
Evolutionary dynamics have been traditionally studied in the context of homogeneous populations, mainly described my the Moran process. Recently, this approach has been generalized in \\cite{LHN} by arranging individuals on the nodes of a network. Undirected networks seem to have a smoother behavior than directed ones, and thus it is more challenging to find suppressors/amplifiers of selection. In this paper we present the first class of undirected graphs which act as suppressors of selection, by achieving a fixation probability that is at most one half of that of the complete graph, as the number of vertices increases. Moreover, we provide some generic upper and lower bounds for the fixation probability of general undirected graphs. As our main contribution, we introduce the natural alternative of the model proposed in \\cite{LHN}, where all individuals interact simultaneously and the result is a compromise between aggressive and non-aggressive individuals. That is, the behavior of the individuals in our new m...
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.
Lieder, Ernestine; Weiler, Markus; Blume, Theresa
2016-04-01
Understanding water and energy fluxes at the stream and catchment scale remains a challenging task. Within the CAOS-project-framework it is our aim to investigate spatiotemporal patterns of stream temperature and to deduce understanding about the underlying hydrological system. A low cost sensor network was installed in summer 2015 to monitor stream temperature and EC patterns in time and space. 90 HOBO temperature sensors, which were modified to additionally measure EC, were installed at 30 confluences across the Attert catchment (288 km²) in Luxembourg. The design of the sensor network allows for the investigation of three research questions: a) spatial patterns of stream temperatures and EC and their dynamics across the region b) estimation of relative streamflow contributions and their temporal dynamics by using simple mixing models and c) estimation of heat transport. The data will thus provide valuable insight in runoff contributions from different sub-catchments, and a combined analysis with distributed measurements of soil moisture and shallow groundwater will improve our process understanding by linking hillslope scale processes with stream responses. First results indicate that streams in different geologies show distinct temperature and EC patterns throughout the observation period. Differences are also found with respect to temporal dynamics both for longer periods as well as diurnal fluctuations. These differences are likely to be caused by differences in flow paths on the one hand (e.g. amount of groundwater contribution) and exposure to direct radiation on the other hand.
Development of a iron pipe corrosion simulation model for a water supply network
Bernats, M.; Osterhus, S. W.; Dzelzitis, K.; Juhna, T.
2012-01-01
Corrosion in water supply networks is unwanted process that causes pipe material loss and subsequent pipe failures. Nowadays pipe replacing strategy most often is based on pipe age, which is not always the most important factor in pipe burst rate. In this study a methodology for developing a mathematical model to predict the decrease of pipe thickness in a large cast iron networks is presented. The quality of water, the temperature and the water flow regime were the main factors taken into ac...
Dynamic swelling behavior of interpenetrating polymer networks in response to temperature and pH
Slaughter, Brandon V.; Blanchard, Aaron T.; Maass, Katie F.; Peppas, Nicholas A.
2015-01-01
Temperature responsive hydrogels based on ionic polymers exhibit swelling transitions in aqueous solutions as a function of shifting pH and ionic strength, in addition to temperature. Applying these hydrogels to useful applications, particularly for biomedical purposes such as drug delivery and regenerative medicine, is critically dependent on understanding the hydrogel solution responses as a function of all three parameters together. In this work, interpenetrating polymer network (IPN) hydr...
Temperature Effect on Uncooled Semiconductor Laser Diode to the Network Performance System
Mohammad S. Ab-Rahman; Nurain I. Shuhaimi
2012-01-01
Problem statement: The characteristics of a laser diode are highly dependent on the temperature of the laser chip. Thus, the effect of temperature on the network performance of uncooled semiconductor laser diode are studied by simulating its equivalent electrical circuit, developed from the rate equations that governing optical components directly into an electrical simulation framework. Approach: The simulations are carried out using circuit analysis program named OptiSPICE and OptiSystem. I...
Precise Network Modeling of Systems Genetics Data Using the Bayesian Network Webserver.
Ziebarth, Jesse D; Cui, Yan
2017-01-01
The Bayesian Network Webserver (BNW, http://compbio.uthsc.edu/BNW ) is an integrated platform for Bayesian network modeling of biological datasets. It provides a web-based network modeling environment that seamlessly integrates advanced algorithms for probabilistic causal modeling and reasoning with Bayesian networks. BNW is designed for precise modeling of relatively small networks that contain less than 20 nodes. The structure learning algorithms used by BNW guarantee the discovery of the best (most probable) network structure given the data. To facilitate network modeling across multiple biological levels, BNW provides a very flexible interface that allows users to assign network nodes into different tiers and define the relationships between and within the tiers. This function is particularly useful for modeling systems genetics datasets that often consist of multiscalar heterogeneous genotype-to-phenotype data. BNW enables users to, within seconds or minutes, go from having a simply formatted input file containing a dataset to using a network model to make predictions about the interactions between variables and the potential effects of experimental interventions. In this chapter, we will introduce the functions of BNW and show how to model systems genetics datasets with BNW.
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...
Throughput capacity computation model for hybrid wireless networks
African Journals Online (AJOL)
wireless networks. We present in this paper, a computational model for obtaining throughput capacity for hybrid wireless networks. For a hybrid network with n nodes and m base stations, we observe through simulation that the throughput capacity increases linearly with the base station infrastructure connected by the wired ...
Modelling crime linkage with Bayesian networks.
de Zoete, Jacob; Sjerps, Marjan; Lagnado, David; Fenton, Norman
2015-05-01
When two or more crimes show specific similarities, such as a very distinct modus operandi, the probability that they were committed by the same offender becomes of interest. This probability depends on the degree of similarity and distinctiveness. We show how Bayesian networks can be used to model different evidential structures that can occur when linking crimes, and how they assist in understanding the complex underlying dependencies. That is, how evidence that is obtained in one case can be used in another and vice versa. The flip side of this is that the intuitive decision to "unlink" a case in which exculpatory evidence is obtained leads to serious overestimation of the strength of the remaining cases. Copyright © 2014 Forensic Science Society. Published by Elsevier Ireland Ltd. All rights reserved.
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...
A mathematical model for networks with structures in the mesoscale
Criado, Regino; Flores, Julio; Gacia Del Amo, Alejandro Jose; Gómez, Jesus; Romance, Miguel
2011-01-01
Abstract The new concept of multilevel network is introduced in order to embody some topological properties of complex systems with structures in the mesoscale which are not completely captured by the classical models. This new model, which generalizes the hyper-network and hyper-structure models, fits perfectly with several real-life complex systems, including social and public transportation networks. We present an analysis of the structural properties of the mu...
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 ...
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.
Xia, Dunzhu; Kong, Lun; Hu, Yiwei; Ni, Peizhen
2015-02-01
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.
A Cascade-Based Emergency Model for Water Distribution Network
Directory of Open Access Journals (Sweden)
Qing Shuang
2015-01-01
Full Text Available Water distribution network is important in the critical physical infrastructure systems. The paper studies the emergency resource strategies on water distribution network with the approach of complex network and cascading failures. The model of cascade-based emergency for water distribution network is built. The cascade-based model considers the network topology analysis and hydraulic analysis to provide a more realistic result. A load redistribution function with emergency recovery mechanisms is established. From the aspects of uniform distribution, node betweenness, and node pressure, six recovery strategies are given to reflect the network topology and the failure information, respectively. The recovery strategies are evaluated with the complex network indicators to describe the failure scale and failure velocity. The proposed method is applied by an illustrative example. The results showed that the recovery strategy considering the node pressure can enhance the network robustness effectively. Besides, this strategy can reduce the failure nodes and generate the least failure nodes per time.
Integrating public transort networks in the axial model
Gil, J.
2012-01-01
This study presents a first step in the development of a model that integrates public transport networks with the space syntax axial model, towards a network model that can describe the multi?modal movement structure of a city and study its patterns and flows. It describes the method for building an
An intercausal cancellation model for Bayesian-network engineering
Woudenberg, Steven P D; Van Der Gaag, Linda C.; Rademaker, Carin M A
2015-01-01
When constructing Bayesian networks with domain experts, network engineers often use the noisy-OR model, and causal interaction models more generally, to alleviate the burden of probability elicitation: the use of such a model serves to reduce the number of probabilities to be elicited on the one
Common quandaries and their practical solutions in Bayesian network modeling
Bruce G. Marcot
2017-01-01
Use and popularity of Bayesian network (BN) modeling has greatly expanded in recent years, but many common problems remain. Here, I summarize key problems in BN model construction and interpretation,along with suggested practical solutions. Problems in BN model construction include parameterizing probability values, variable definition, complex network structures,...
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
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.
Spectral Modelling for Spatial Network Analysis
Nourian, P.; Rezvani, S.; Sariyildiz, I.S.; van der Hoeven, F.D.; Attar, Ramtin; Chronis, Angelos; Hanna, Sean; Turrin, Michela
2016-01-01
Spatial Networks represent the connectivity structure between units of space as a weighted graph whose links are weighted as to the strength of connections. In case of urban spatial networks, the units of space correspond closely to streets and in architectural spatial networks the units correspond
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...
Temperature-dependent rate models of vascular cambium cell mortality
Matthew B. Dickinson; Edward A. Johnson
2004-01-01
We use two rate-process models to describe cell mortality at elevated temperatures as a means of understanding vascular cambium cell death during surface fires. In the models, cell death is caused by irreversible damage to cellular molecules that occurs at rates that increase exponentially with temperature. The models differ in whether cells show cumulative effects of...
Performance Modeling for Heterogeneous Wireless Networks with Multiservice Overflow Traffic
DEFF Research Database (Denmark)
Huang, Qian; Ko, King-Tim; Iversen, Villy Bæk
2009-01-01
Performance modeling is important for the purpose of developing efficient dimensioning tools for large complicated networks. But it is difficult to achieve in heterogeneous wireless networks, where different networks have different statistical characteristics in service and traffic models....... Multiservice loss analysis based on multi-dimensional Markov chain becomes intractable in these networks due to intensive computations required. This paper focuses on performance modeling for heterogeneous wireless networks based on a hierarchical overlay infrastructure. A method based on decomposition...... of the correlated traffic is used to achieve an approximate performance modeling for multiservice in hierarchical heterogeneous wireless networks with overflow traffic. The accuracy of the approximate performance obtained by our proposed modeling is verified by simulations....
Piecewise linear and Boolean models of chemical reaction networks.
Veliz-Cuba, Alan; Kumar, Ajit; Josić, Krešimir
2014-12-01
Models of biochemical networks are frequently complex and high-dimensional. Reduction methods that preserve important dynamical properties are therefore essential for their study. Interactions in biochemical networks are frequently modeled using Hill functions ([Formula: see text]). Reduced ODEs and Boolean approximations of such model networks have been studied extensively when the exponent [Formula: see text] is large. However, while the case of small constant [Formula: see text] appears in practice, it is not well understood. We provide a mathematical analysis of this limit and show that a reduction to a set of piecewise linear ODEs and Boolean networks can be mathematically justified. The piecewise linear systems have closed-form solutions that closely track those of the fully nonlinear model. The simpler, Boolean network can be used to study the qualitative behavior of the original system. We justify the reduction using geometric singular perturbation theory and compact convergence, and illustrate the results in network models of a toggle switch and an oscillator.
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.
Spatial and temporal variation of water temperature regimes on the Snoqualmie River network
Ashley E. Steel; Colin Sowder; Erin E. Peterson
2016-01-01
Although mean temperatures change annually and are highly correlated with elevation, the entire thermal regime on the Snoqualmie River, Washington, USA does not simply shift with elevation or season. Particular facets of the thermal regime have unique spatial patterns on the river network and at particular times of the year. We used a spatially and temporally dense...
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...
de Ponte, M. A.; Mizrahi, S. S.; Moussa, M. H. Y.
2009-09-01
In this paper we extend the results presented in (de Ponte, Mizrahi and Moussa 2007 Phys. Rev. A 76 032101) to treat quantitatively the effects of reservoirs at finite temperature in a bosonic dissipative network: a chain of coupled harmonic oscillators whatever its topology, i.e., whichever the way the oscillators are coupled together, the strength of their couplings and their natural frequencies. Starting with the case where distinct reservoirs are considered, each one coupled to a corresponding oscillator, we also analyze the case where a common reservoir is assigned to the whole network. Master equations are derived for both situations and both regimes of weak and strong coupling strengths between the network oscillators. Solutions of these master equations are presented through the normal ordered characteristic function. These solutions are shown to be significantly involved when temperature effects are considered, making difficult the analysis of collective decoherence and dispersion in dissipative bosonic networks. To circumvent these difficulties, we turn to the Wigner distribution function which enables us to present a technique to estimate the decoherence time of network states. Our technique proceeds by computing separately the effects of dispersion and the attenuation of the interference terms of the Wigner function. A detailed analysis of the dispersion mechanism is also presented through the evolution of the Wigner function. The interesting collective dispersion effects are discussed and applied to the analysis of decoherence of a class of network states. Finally, the entropy and the entanglement of a pure bipartite system are discussed.
Energy Technology Data Exchange (ETDEWEB)
De Ponte, M A; Mizrahi, S S [Departamento de Fisica, Universidade Federal de Sao Carlos, Caixa Postal 676, Sao Carlos, 13565-905, Sao Paulo (Brazil); Moussa, M H Y [Instituto de Fisica de Sao Carlos, Universidade de Sao Paulo, Caixa Postal 369, 13560-590 Sao Carlos, SP (Brazil)
2009-09-11
In this paper we extend the results presented in (de Ponte, Mizrahi and Moussa 2007 Phys. Rev. A 76 032101) to treat quantitatively the effects of reservoirs at finite temperature in a bosonic dissipative network: a chain of coupled harmonic oscillators whatever its topology, i.e., whichever the way the oscillators are coupled together, the strength of their couplings and their natural frequencies. Starting with the case where distinct reservoirs are considered, each one coupled to a corresponding oscillator, we also analyze the case where a common reservoir is assigned to the whole network. Master equations are derived for both situations and both regimes of weak and strong coupling strengths between the network oscillators. Solutions of these master equations are presented through the normal ordered characteristic function. These solutions are shown to be significantly involved when temperature effects are considered, making difficult the analysis of collective decoherence and dispersion in dissipative bosonic networks. To circumvent these difficulties, we turn to the Wigner distribution function which enables us to present a technique to estimate the decoherence time of network states. Our technique proceeds by computing separately the effects of dispersion and the attenuation of the interference terms of the Wigner function. A detailed analysis of the dispersion mechanism is also presented through the evolution of the Wigner function. The interesting collective dispersion effects are discussed and applied to the analysis of decoherence of a class of network states. Finally, the entropy and the entanglement of a pure bipartite system are discussed.
Heterogeneous information network model for equipment-standard system
Yin, Liang; Shi, Li-Chen; Zhao, Jun-Yan; Du, Song-Yang; Xie, Wen-Bo; Yuan, Fei; Chen, Duan-Bing
2018-01-01
Entity information network is used to describe structural relationships between entities. Taking advantage of its extension and heterogeneity, entity information network is more and more widely applied to relationship modeling. Recent years, lots of researches about entity information network modeling have been proposed, while seldom of them concentrate on equipment-standard system with properties of multi-layer, multi-dimension and multi-scale. In order to efficiently deal with some complex issues in equipment-standard system such as standard revising, standard controlling, and production designing, a heterogeneous information network model for equipment-standard system is proposed in this paper. Three types of entities and six types of relationships are considered in the proposed model. Correspondingly, several different similarity-measuring methods are used in the modeling process. The experiments show that the heterogeneous information network model established in this paper can reflect relationships between entities accurately. Meanwhile, the modeling process has a good performance on time consumption.
Modified Penna bit-string network evolution model for scale-free networks with assortative mixing
Kim, Yup; Choi, Woosik; Yook, Soon-Hyung
2012-02-01
Motivated by biological aging dynamics, we introduce a network evolution model for social interaction networks. In order to study the effect of social interactions originating from biological and sociological reasons on the topological properties of networks, we introduce the activitydependent rewiring process. From the numerical simulations, we show that the degree distribution of the obtained networks follows a power-law distribution with an exponentially decaying tail, P( k) ˜ ( k + c)- γ exp(- k/k 0). The obtained value of γ is in the range 2 networks. Moreover, we also show that the degree-degree correlation of the network is positive, which is a characteristic of social interaction networks. The possible applications of our model to real systems are also discussed.
Directory of Open Access Journals (Sweden)
Daniel Petru GHENCEA
2016-05-01
Full Text Available Simulation spindle behavior in terms of temperature and vibration at higher speeds is more economical and more secure (avoid undesirable mechanical events than testing practice. Testing practice has an important role in finalizing the product but throughout the course of prototype testing is more advantageous economic development simulation parameters based on data sets collected to dangerous speeds. In this paper we present an analysis mode hybrid (artificial neural networks - fuzzy logic on prediction the evolution of temperatures and vibrations at higher speeds for which no measurements were made. The main advantage of the method is the simultaneous prediction of the dynamics of temperature and vibration levels.
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.
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.
A model of evaluating the pseudogap temperature for high ...
Indian Academy of Sciences (India)
We have presented a model of evaluating the pseudogap temperature for high temperature superconductors using paraconductivity approach. The theoretical analysis is based on the crossing point technique of the conductivity expressions. The pseudogap temperature T ∗ is found to depend on dimension and is ...
Quantification Model for Estimating Temperature Field Distributions of Apple Fruit
Zhang, Min; Yang, Le; Zhao, Huizhong; Zhang, Leijie; Zhong, Zhiyou; Liu, Yanling; Chen, Jianhua
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 distribution in the cooling process. Then there was obvious difference between apple fruit temperature and environment temperature. Compared to the change of environment temperature, a long hysteresis phenomenon happened to the temperature of apple fruit body. That is to say, there was a significant temperature change of apple fruit body in a period of time after environment temperature dropping. And then the change of temerature of apple fruit body in the cooling process became slower and slower. This can explain the time delay phenomenon of biology. After that, the temperature differences of every layer increased from centre to surface of apple fruit gradually. That is to say, the minimum temperature differences closed to centre of apple fruit body and the maximum temperature differences closed to the surface of apple fruit body. Finally, the temperature of every part of apple fruit body will tend to consistent and be near to the environment temperature in the cooling process. It was related to the metabolism heat of plant body at any time.
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...
A model for evaluating stream temperature response to climate change scenarios in Wisconsin
Westenbroek, Stephen M.; Stewart, Jana S.; Buchwald, Cheryl A.; Mitro, Matthew G.; Lyons, John D.; Greb, Steven
2010-01-01
Global climate change is expected to alter temperature and flow regimes for streams in Wisconsin over the coming decades. Stream temperature will be influenced not only by the predicted increases in average air temperature, but also by changes in baseflow due to changes in precipitation patterns and amounts. In order to evaluate future stream temperature and flow regimes in Wisconsin, we have integrated two existing models in order to generate a water temperature time series at a regional scale for thousands of stream reaches where site-specific temperature observations do not exist. The approach uses the US Geological Survey (USGS) Soil-Water-Balance (SWB) model, along with a recalibrated version of an existing artificial neural network (ANN) stream temperature model. The ANN model simulates stream temperatures on the basis of landscape variables such as land use and soil type, and also includes climate variables such as air temperature and precipitation amounts. The existing ANN model includes a landscape variable called DARCY designed to reflect the potential for groundwater recharge in the contributing area for a stream segment. SWB tracks soil-moisture and potential recharge at a daily time step, providing a way to link changing climate patterns and precipitation amounts over time to baseflow volumes, and presumably to stream temperatures. The recalibrated ANN incorporates SWB-derived estimates of potential recharge to supplement the static estimates of groundwater flow potential derived from a topographically based model (DARCY). SWB and the recalibrated ANN will be supplied with climate drivers from a suite of general circulation models and emissions scenarios, enabling resource managers to evaluate possible changes in stream temperature regimes for Wisconsin.
Network models of frugivory and seed dispersal: Challenges and opportunities
Carlo, Tomás A.; Yang, Suann
2011-11-01
Network analyses have emerged as a new tool to study frugivory and seed dispersal (FSD) mutualisms because networks can model and simplify the complexity of multiple community-wide species interactions. Moreover, network theory suggests that structural properties, such as the presence of highly generalist species, are linked to the stability of mutualistic communities. However, we still lack empirical validation of network model predictions. Here we outline new research avenues to connect network models to FSD processes, and illustrate the challenges and opportunities of this tool with a field study. We hypothesized that generalist frugivores would be important for forest stability by dispersing seeds into deforested areas and initiating reforestation. We then constructed a network of plant-frugivore interactions using published data and identified the most generalist frugivores. To test the importance of generalists we measured: 1) the frequency with which frugivores moved between pasture and forest, 2) the bird-generated seed rain under perches in the pasture, and 3) the perching frequency of birds above seed traps. The generalist frugivores in the forest network were not important for seed dispersal into pastures, and thus for forest recovery, because the forest network excluded habitat heterogeneities, frugivore behavior, and movements. More research is needed to develop ways to incorporate relevant FSD processes into network models in order for these models to be more useful to community ecology and conservation. The network framework can serve to spark and renew interest in FSD and further our understanding of plant-animal communities.
Hybrid neural network bushing model for vehicle dynamics simulation
Energy Technology Data Exchange (ETDEWEB)
Sohn, Jeong Hyun [Pukyong National University, Busan (Korea, Republic of); Lee, Seung Kyu [Hyosung Corporation, Changwon (Korea, Republic of); Yoo, Wan Suk [Pusan National University, Busan (Korea, Republic of)
2008-12-15
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
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
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......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...... 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...
DEFF Research Database (Denmark)
Ommen, Torben Schmidt; Elmegaard, Brian
2012-01-01
with a heat pump, as the remaining heat demands are often not required at temperature levels as high as the tap water. The scope of this work is to evaluate the power consumption and second law efficiency of booster heat pumps for tap water production in a low temperature district heating network. The heat...... pump and storage arrangement is evaluated based on a tapping sequence from the Danish standards (DS439). Based an initial investigation of possible designs, three configurations have been chosen for the evaluation. Of the three heat pumps, two are implemented on the primary side to boost the network...... exchanger sizes and the isentropic efficiency of the compressor used in the heat pump. The superior configuration shows exergetic efficiencies higher than 0.5 when forward temperatures is around 45 ºC....
Optical Network Models and Their Application to Software-Defined Network Management
Directory of Open Access Journals (Sweden)
Thomas Szyrkowiec
2017-01-01
Full Text Available Software-defined networking is finding its way into optical networks. Here, it promises a simplification and unification of network management for optical networks allowing automation of operational tasks despite the highly diverse and vendor-specific commercial systems and the complexity and analog nature of optical transmission. Common abstractions and interfaces are a fundamental component for software-defined optical networking. Currently, a number of models for optical networks are available. They all claim to provide open and vendor agnostic management of optical equipment. In this work, we survey and compare the most important models and propose an intent interface for creating virtual topologies which is integrated in the existing model ecosystem.
An image segmentation method based on network clustering model
Jiao, Yang; Wu, Jianshe; Jiao, Licheng
2018-01-01
Network clustering phenomena are ubiquitous in nature and human society. In this paper, a method involving a network clustering model is proposed for mass segmentation in mammograms. First, the watershed transform is used to divide an image into regions, and features of the image are computed. Then a graph is constructed from the obtained regions and features. The network clustering model is applied to realize clustering of nodes in the graph. Compared with two classic methods, the algorithm based on the network clustering model performs more effectively in experiments.
Small is beautiful: models of small neuronal networks.
Lamb, Damon G; Calabrese, Ronald L
2012-08-01
Modeling has contributed a great deal to our understanding of how individual neurons and neuronal networks function. In this review, we focus on models of the small neuronal networks of invertebrates, especially rhythmically active CPG networks. Models have elucidated many aspects of these networks, from identifying key interacting membrane properties to pointing out gaps in our understanding, for example missing neurons. Even the complex CPGs of vertebrates, such as those that underlie respiration, have been reduced to small network models to great effect. Modeling of these networks spans from simplified models, which are amenable to mathematical analyses, to very complicated biophysical models. Some researchers have now adopted a population approach, where they generate and analyze many related models that differ in a few to several judiciously chosen free parameters; often these parameters show variability across animals and thus justify the approach. Models of small neuronal networks will continue to expand and refine our understanding of how neuronal networks in all animals program motor output, process sensory information and learn. Copyright © 2012 Elsevier Ltd. All rights reserved.
Liu, Ji; Tan, Cindy Soo Yun; Yu, Ziyi; Li, Nan; Abell, Chris; Scherman, Oren A
2017-06-01
Recent progress on highly tough and stretchable polymer networks has highlighted the potential of wearable electronic devices and structural biomaterials such as cartilage. For some given applications, a combination of desirable mechanical properties including stiffness, strength, toughness, damping, fatigue resistance, and self-healing ability is required. However, integrating such a rigorous set of requirements imposes substantial complexity and difficulty in the design and fabrication of these polymer networks, and has rarely been realized. Here, we describe the construction of supramolecular polymer networks through an in situ copolymerization of acrylamide and functional monomers, which are dynamically complexed with the host molecule cucurbit[8]uril (CB[8]). High molecular weight, thus sufficient chain entanglement, combined with a small-amount dynamic CB[8]-mediated non-covalent crosslinking (2.5 mol%), yields extremely stretchable and tough supramolecular polymer networks, exhibiting remarkable self-healing capability at room temperature. These supramolecular polymer networks can be stretched more than 100× their original length and are able to lift objects 2000× their weight. The reversible association/dissociation of the host-guest complexes bestows the networks with remarkable energy dissipation capability, but also facile complete self-healing at room temperature. In addition to their outstanding mechanical properties, the networks are ionically conductive and transparent. The CB[8]-based supramolecular networks are synthetically accessible in large scale and exhibit outstanding mechanical properties. They could readily lead to the promising use as wearable and self-healable electronic devices, sensors and structural biomaterials. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Water temperature modeling in the Garonne River (France
Directory of Open Access Journals (Sweden)
Larnier K.
2010-10-01
Full Text Available Stream water temperature is one of the most important parameters for water quality and ecosystem studies. Temperature can influence many chemical and biological processes and therefore impacts on the living conditions and distribution of aquatic ecosystems. Simplified models such as statistical models can be very useful for practitioners and water resource management. The present study assessed two statistical models – an equilibrium-based model and stochastic autoregressive model with exogenous inputs – in modeling daily mean water temperatures in the Garonne River from 1988 to 2005. The equilibrium temperature-based model is an approach where net heat flux at the water surface is expressed as a simpler form than in traditional deterministic models. The stochastic autoregressive model with exogenous inputs consists of decomposing the water temperature time series into a seasonal component and a short-term component (residual component. The seasonal component was modeled by Fourier series and residuals by a second-order autoregressive process (Markov chain with use of short-term air temperatures as exogenous input. The models were calibrated using data of the first half of the period 1988–2005 and validated on the second half. Calibration of the models was done using temperatures above 20 °C only to ensure better prediction of high temperatures that are currently at stake for the aquatic conditions of the Garonne River, and particularly for freshwater migrating fishes such as Atlantic Salmon (Salmo salar L.. The results obtained for both approaches indicated that both models performed well with an average root mean square error for observed temperatures above 20 °C that varied on an annual basis from 0.55 °C to 1.72 °C on validation, and good predictions of temporal occurrences and durations of three temperature threshold crossings linked to the conditions of migration and survival of Atlantic Salmon.
A soil moisture and temperature network for SMOS validation in Western Denmark
Directory of Open Access Journals (Sweden)
S. Bircher
2012-05-01
Full Text Available The Soil Moisture and Ocean Salinity Mission (SMOS acquires surface soil moisture data of global coverage every three days. Product validation for a range of climate and environmental conditions across continents is a crucial step. For this purpose, a soil moisture and soil temperature sensor network was established in the Skjern River Catchment, Denmark. The objectives of this article are to describe a method to implement a network suited for SMOS validation, and to present sample data collected by the network to verify the approach. The design phase included (1 selection of a single 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 and precipitation between the north-east and south-west were found to be small. A first comparison between the 0–5 cm network averages and the SMOS soil moisture (level 2 product is in range with worldwide validation results, showing comparable trends for SMOS retrieved soil moisture (R^{2} of 0.49 as well as initial soil moisture and temperature from ECMWF used in the retrieval algorithm (R^{2} of 0.67 and 0.97, respectively. While retrieved/initial SMOS soil moisture indicate significant under-/overestimation of the network data (biases of −0.092/0.057 m^{3} m^{−3}, the initial temperature is in good agreement (bias of −0.2 °C. Based on these findings, the network performs according to expectations and proves to be
Stochastic Online Learning in Dynamic Networks under Unknown Models
2016-08-02
Stochastic Online Learning in Dynamic Networks under Unknown Models This research aims to develop fundamental theories and practical algorithms for...12211 Research Triangle Park, NC 27709-2211 Online learning , multi-armed bandit, dynamic networks REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S... Online Learning in Dynamic Networks under Unknown Models Report Title This research aims to develop fundamental theories and practical algorithms for
A Cellular Automata Models of Evolution of Transportation Networks
Directory of Open Access Journals (Sweden)
Mariusz Paszkowski
2002-01-01
Full Text Available We present a new approach to modelling of transportation networks. Supply of resources and their influence on the evolution of the consuming environment is a princqral problem considered. ne present two concepts, which are based on cellular automata paradigm. In the first model SCAM4N (Simple Cellular Automata Model of Anastomosing Network, the system is represented by a 2D mesh of elementary cells. The rules of interaction between them are introduced for modelling ofthe water flow and other phenomena connected with anastomosing river: Due to limitations of SCAMAN model, we introduce a supplementary model. The MANGraCA (Model of Anastomosing Network with Graph of Cellular Automata model beside the classical mesh of automata, introduces an additional structure: the graph of cellular automata, which represents the network pattern. Finally we discuss the prospective applications ofthe models. The concepts of juture implementation are also presented.
A Deployment of Fine-Grained Sensor Network and Empirical Analysis of Urban Temperature
Directory of Open Access Journals (Sweden)
Yoshito Tobe
2010-03-01
Full Text Available Temperature in an urban area exhibits a complicated pattern due to complexity of infrastructure. Despite geographical proximity, structures of a group of buildings and streets affect changes in temperature. To investigate the pattern of fine-grained distribution of temperature, we installed a densely distributed sensor network called UScan. In this paper, we describe the system architecture of UScan as well as experience learned from installing 200 sensors in downtown Tokyo. The field experiment of UScan system operated for two months to collect long-term urban temperature data. To analyze the collected data in an efficient manner, we propose a lightweight clustering methodology to study the correlation between the pattern of temperature and various environmental factors including the amount of sunshine, the width of streets, and the existence of trees. The analysis reveals meaningful results and asserts the necessity of fine-grained deployment of sensors in an urban area.
Directory of Open Access Journals (Sweden)
Jean-Michel Gibert
2007-02-01
Full Text Available Phenotypic plasticity is the ability of a genotype to produce contrasting phenotypes in different environments. Although many examples have been described, the responsible mechanisms are poorly understood. In particular, it is not clear how phenotypic plasticity is related to buffering, the maintenance of a constant phenotype against genetic or environmental variation. We investigate here the genetic basis of a particularly well described plastic phenotype: the abdominal pigmentation in female Drosophila melanogaster. Cold temperature induces a dark pigmentation, in particular in posterior segments, while higher temperature has the opposite effect. We show that the homeotic gene Abdominal-B (Abd-B has a major role in the plasticity of pigmentation in the abdomen. Abd-B plays opposite roles on melanin production through the regulation of several pigmentation enzymes. This makes the control of pigmentation very unstable in the posterior abdomen, and we show that the relative spatio-temporal expression of limiting pigmentation enzymes in this region of the body is thermosensitive. Temperature acts on melanin production by modulating a chromatin regulator network, interacting genetically with the transcription factor bric-à-brac (bab, a target of Abd-B and Hsp83, encoding the chaperone Hsp90. Genetic disruption of this chromatin regulator network increases the effect of temperature and the instability of the pigmentation pattern in the posterior abdomen. Colocalizations on polytene chromosomes suggest that BAB and these chromatin regulators cooperate in the regulation of many targets, including several pigmentation enzymes. We show that they are also involved in sex comb development in males and that genetic destabilization of this network is also strongly modulated by temperature for this phenotype. Thus, we propose that phenotypic plasticity of pigmentation is a side effect reflecting a global impact of temperature on epigenetic mechanisms
The influence of cold temperature on cellular excitability of hippocampal networks.
Directory of Open Access Journals (Sweden)
Elvira de la Peña
Full Text Available 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.
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....
Drinking Water Temperature Modelling in Domestic Systems
Moerman, A.; Blokker, M.; Vreeburg, J.; Van der Hoek, J.P.
2014-01-01
Domestic water supply systems are the final stage of the transport process to deliver potable water to the customers’ tap. Under the influence of temperature, residence time and pipe materials the drinking water quality can change while the water passes the domestic drinking water system. According
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.
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 ...
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...... is managed by running the stack at a high stoichiometric air flow. This is possible because of the polybenzimidazole (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....... The temperature is predicted in these three parts, where they also are measured. The heat balance of the system involves a fuel cell model to describe the heat added by the fuel cells when a current is drawn. Furthermore the model also predicts the temperatures when heating the stack with external heating...
Castet, Jean-Francois; Saleh, Joseph H
2013-01-01
This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs) allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats) of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also examined, and the
Directory of Open Access Journals (Sweden)
Jean-Francois Castet
Full Text Available This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also
3D Temperature Distribution Model Based on Thermal Infrared Image
Directory of Open Access Journals (Sweden)
Tong Jia
2017-01-01
Full Text Available This paper aims to study the construction of 3D temperature distribution reconstruction system based on binocular vision technology. Initially, a traditional calibration method cannot be directly used, because the thermal infrared camera is only sensitive to temperature. Therefore, the thermal infrared camera is calibrated separately. Belief propagation algorithm is also investigated and its smooth model is improved in terms of stereo matching to optimize mismatching rate. Finally, the 3D temperature distribution model is built based on the matching of 3D point cloud and 2D thermal infrared information. Experimental results show that the method can accurately construct the 3D temperature distribution model and has strong robustness.
Artificial Neural Network Modeling of an Inverse Fluidized Bed ...
African Journals Online (AJOL)
MICHAEL
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 obtained from an inverse fluidized bed reactor treating the starch industry wastewater.
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
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.
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.
Modelling of flame temperature of solution combustion synthesis of ...
Indian Academy of Sciences (India)
A regression model has also been developed to correlate the input parameters, viz. batch size, diluents, fuel to oxidizer ratio and initial furnace temperature, with flame temperature of the solution combustion reaction. The adequacy of the developed model has been checked using analysis of variance technique.
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...
High time resolution reconstruction of electron temperature profiles with a neural network in C-2U
Player, Gabriel; Magee, Richard; Trask, Erik; Korepanov, Sergey; Clary, Ryan; Tri Alpha Energy Team
2017-10-01
One of the most important parameters governing fast ion dynamics in a plasma is the electron temperature, as the fast ion-electron collision rate goes as νei Te3 / 2 . Unfortunately, the electron temperature is difficult to directly measure-methods relying on high-powered laser pulses or fragile probes lead to limited time resolution or measurements restricted to the edge. In order to rectify the lack of time resolution on the Thomson scattering data in the core, a type of learning algorithm, specifically a neural network, was implemented. This network uses 3 hidden layers to correlate information from nearly 250 signals, including magnetics, interferometers, and several arrays of bolometers, with Thomson scattering data over the entire C-2U database, totalling nearly 20,000 samples. The network uses the Levenberg-Marquardt algorithm with Bayesian regularization to learn from the large number of samples and inputs how to accurately reconstruct the entire electron temperature time history at a resolution of 500 kHz, a huge improvement over the 2 time points per shot provided by Thomson scattering. These results can be used in many different types of analysis and plasma characterization-in this work, we use the network to quantify electron heating.
Neural and Neural Gray-Box Modeling for Entry Temperature Prediction in a Hot Strip Mill
Barrios, José Angel; Torres-Alvarado, Miguel; Cavazos, Alberto; Leduc, Luis
2011-10-01
In hot strip mills, initial controller set points have to be calculated before the steel bar enters the mill. Calculations rely on the good knowledge of rolling variables. Measurements are available only after the bar has entered the mill, and therefore they have to be estimated. Estimation of process variables, particularly that of temperature, is of crucial importance for the bar front section to fulfill quality requirements, and the same must be performed in the shortest possible time to preserve heat. Currently, temperature estimation is performed by physical modeling; however, it is highly affected by measurement uncertainties, variations in the incoming bar conditions, and final product changes. In order to overcome these problems, artificial intelligence techniques such as artificial neural networks and fuzzy logic have been proposed. In this article, neural network-based systems, including neural-based Gray-Box models, are applied to estimate scale breaker entry temperature, given its importance, and their performance is compared to that of the physical model used in plant. Several neural systems and several neural-based Gray-Box models are designed and tested with real data. Taking advantage of the flexibility of neural networks for input incorporation, several factors which are believed to have influence on the process are also tested. The systems proposed in this study were proven to have better performance indexes and hence better prediction capabilities than the physical models currently used in plant.
Modelling the growth of Leuconostoc mesenteroides by Artificial Neural Networks.
García-Gimeno, R M; Hervás-Martínez, C; Rodríguez-Pérez, R; Zurera-Cosano, G
2005-12-15
The combined effect of temperature (10.5 to 24.5 degrees C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the predicted specific growth rate (Gr), lag-time (Lag) and maximum population density (yEnd) of Leuconostoc mesenteroides under aerobic and anaerobic conditions, was studied using an Artificial Neural Network-based model (ANN) in comparison with Response Surface Methodology (RS). For both aerobic and anaerobic conditions, two types of ANN model were elaborated, unidimensional for each of the growth parameters, and multidimensional in which the three parameters Gr, Lag, and yEnd are combined. Although in general no significant statistical differences were observed between both types of model, we opted for the unidimensional model, because it obtained the lowest mean value for the standard error of prediction for generalisation. The ANN models developed provided reliable estimates for the three kinetic parameters studied; the SEP values in aerobic conditions ranged from between 2.82% for Gr, 6.05% for Lag and 10% for yEnd, a higher degree accuracy than those of the RS model (Gr: 9.54%; Lag: 8.89%; yEnd: 10.27%). Similar results were observed for anaerobic conditions. During external validation, a higher degree of accuracy (Af) and bias (Bf) were observed for the ANN model compared with the RS model. ANN predictive growth models are a valuable tool, enabling swift determination of L. mesenteroides growth parameters.
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...
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.
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
Completely random measures for modelling block-structured sparse networks
DEFF Research Database (Denmark)
Herlau, Tue; Schmidt, Mikkel Nørgaard; Mørup, Morten
2016-01-01
Many statistical methods for network data parameterize the edge-probability by attributing latent traits to the vertices such as block structure and assume exchangeability in the sense of the Aldous-Hoover representation theorem. Empirical studies of networks indicate that many real-world networks...... [2014] proposed the use of a different notion of exchangeability due to Kallenberg [2006] and obtained a network model which admits power-law behaviour while retaining desirable statistical properties, however this model does not capture latent vertex traits such as block-structure. In this work we re......-introduce the use of block-structure for network models obeying allenberg’s notion of exchangeability and thereby obtain a model which admits the inference of block-structure and edge inhomogeneity. We derive a simple expression for the likelihood and an efficient sampling method. The obtained model...
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.
Temperature dependent climate projection deficiencies in CMIP5 models
DEFF Research Database (Denmark)
Christensen, Jens H.; Boberg, Fredrik
2012-01-01
Monthly mean temperatures for 34 GCMs available from the CMIP5 project are compared with observations from CRU for 26 different land regions covering all major land areas in the world for the period 1961-2000 by means of quantile-quantile (q-q) diagrams. A warm period positive temperature dependent...... bias is identified for many of the models within many of the chosen climate regions. However, the exact temperature dependence varies considerably between the models. We analyse the role of this difference as a contributing factor for some models to project stronger regional warming than others...... that in general models with a positive temperature dependent bias tend to have a large projected temperature change, and these tendencies increase with increasing global warming level. We argue that this appears to be linked with the ability of models to capture complex feedbacks accurately. In particular land...
A thermal network model for induction motors of hermetic reciprocating compressors
Dutra, T.; Deschamps, C. J.
2015-08-01
This paper describes a simulation model for small reciprocating compressors with emphasis on the electrical motor modelling. Heat transfer is solved through algebraic equations derived from lumped thermal energy balances applied to the compressor components. Thermal conductances between the motor components are characterized via a thermal network model. The single-phase induction motor is modelled via an equivalent circuit, allowing predictions for the motor performance and distributed losses. The predicted temperature distribution is used to evaluate the stator and rotor windings resistances. The thermal and electric models are solved in a coupled manner with a model for the compression cycle. Predictions of temperature distribution, motor efficiency, as well as isentropic and volumetric efficiencies, are compared with experimental data at different operating conditions. The model is then applied to analyse the motor temperature as a function of input voltage and stator wire diameter.
Application Interaction Model for Opportunistic Networking
de Souza Schwartz, Ramon; van Dijk, H.W.; Scholten, Johan
In Opportunistic Networks, autonomous nodes discover, assess and potentially seize opportunities for communication and distributed processing whenever these emerge. In this paper, we consider prerequisites for a successful implementation of such a way of processing in networks that consist mainly of
A Model for Telestrok Network Evaluation
DEFF Research Database (Denmark)
Storm, Anna; Günzel, Franziska; Theiss, Stephan
2011-01-01
Different telestroke network concepts have been implemented worldwide to enable fast and efficient treatment of stroke patients in underserved rural areas. Networks could demonstrate the improvement in clinical outcome, but have so far excluded a cost-effectiveness analysis. With health economic ...
Concentration dependent model of protein-protein interaction networks
Zhang, Jingshan
2007-01-01
The scale free structure p(k)~k^{-gamma} of protein-protein interaction networks can be produced by a static physical model. We find the earlier study of deterministic threshold models with exponential fitness distributions can be generalized to explain the apparent scale free degree distribution of the physical model, and this explanation provides a generic mechanism of "scale free" networks. We predict the dependence of gamma on experimental protein concentrations. The clustering coefficient distribution of the model is also studied.
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.
Sarkar, S; Mukherjee, K; Ray, A; Srivastav, A; Wettergren, T A
2012-08-01
This paper presents the qualitative nature of communication network operations as abstraction of typical thermodynamic parameters (e.g., order parameter, temperature, and pressure). Specifically, statistical mechanics-inspired models of critical phenomena (e.g., phase transitions and size scaling) for heterogeneous packet transmission are developed in terms of multiple intensive parameters, namely, the external packet load on the network system and the packet transmission probabilities of heterogeneous packet types. Network phase diagrams are constructed based on these traffic parameters, and decision and control strategies are formulated for heterogeneous packet transmission in the network system. In this context, decision functions and control objectives are derived in closed forms, and the pertinent results of test and validation on a simulated network system are presented.
Modelling hand skin temperature in relation to body composition.
Katić, Katarina; Li, Rongling; Kingma, Boris; Zeiler, Wim
2017-10-01
Skin temperature is a challenging parameter to predict due to the complex interaction of physical and physiological variations. Previous studies concerning the correlation of regional physiological characteristics and body composition showed that obese people have higher hand skin temperature compared to the normal weight people. To predict hand skin temperature in a different environment, a two-node hand thermophysiological model was developed and validated with published experimental data. In addition, a sensitivity analysis was performed which showed that the variations in skin blood flow and blood temperature are most influential on hand skin temperature. The hand model was applied to simulate the hand skin temperature of the obese and normal weight subgroup in different ambient conditions. Higher skin blood flow and blood temperature were used in the simulation of obese people. The results showed a good agreement with experimental data from the literature, with the maximum difference of 0.31°C. If the difference between blood flow and blood temperature of obese and normal weight people was not taken into account, the hand skin temperature of obese people was predicted with an average deviation of 1.42°C. In conclusion, when modelling hand skin temperatures, it should be considered that regional skin temperature distribution differs in obese and normal weight people. Copyright © 2017 Elsevier Ltd. All rights reserved.
Rumor spreading model with noise interference in complex social networks
Zhu, Liang; Wang, Youguo
2017-03-01
In this paper, a modified susceptible-infected-removed (SIR) model has been proposed to explore rumor diffusion on complex social networks. We take variation of connectivity into consideration and assume the variation as noise. On the basis of related literature on virus networks, the noise is described as standard Brownian motion while stochastic differential equations (SDE) have been derived to characterize dynamics of rumor diffusion both on homogeneous networks and heterogeneous networks. Then, theoretical analysis on homogeneous networks has been demonstrated to investigate the solution of SDE model and the steady state of rumor diffusion. Simulations both on Barabási-Albert (BA) network and Watts-Strogatz (WS) network display that the addition of noise accelerates rumor diffusion and expands diffusion size, meanwhile, the spreading speed on BA network is much faster than on WS network under the same noise intensity. In addition, there exists a rumor diffusion threshold in statistical average meaning on homogeneous network which is absent on heterogeneous network. Finally, we find a positive correlation between peak value of infected individuals and noise intensity while a negative correlation between rumor lifecycle and noise intensity overall.
A computational model of hemodynamic parameters in cortical capillary networks.
Safaeian, Navid; Sellier, Mathieu; David, Tim
2011-02-21
The analysis of hemodynamic parameters and functional reactivity of cerebral capillaries is still controversial. To assess the hemodynamic parameters in the cortical capillary network, a generic model was created using 2D voronoi tessellation in which each edge represents a capillary segment. This method is capable of creating an appropriate generic model of cerebral capillary network relating to each part of the brain cortex because the geometric model is able to vary the capillary density. The modeling presented here is based on morphometric parameters extracted from physiological data of the human cortex. The pertinent hemodynamic parameters were obtained by numerical simulation based on effective blood viscosity as a function of hematocrit and microvessel diameter, phase separation and plasma skimming effects. The hemodynamic parameters of capillary networks with two different densities (consistent with the variation of the morphometric data in the human cortical capillary network) were analyzed. The results show pertinent hemodynamic parameters for each model. The heterogeneity (coefficient variation) and the mean value of hematocrits, flow rates and velocities of the both network models were specified. The distributions of blood flow throughout the both models seem to confirm the hypothesis in which all capillaries in a cortical network are recruited at rest (normal condition). The results also demonstrate a discrepancy of the network resistance between two models, which are derived from the difference in the number density of capillary segments between the models. Copyright Â© 2010 Elsevier Ltd. All rights reserved.
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.
HIGH TEMPERATURE HIGH PRESSURE THERMODYNAMIC MEASUREMENTS FOR COAL MODEL COMPOUNDS
Energy Technology Data Exchange (ETDEWEB)
Vinayak N. Kabadi
1999-02-20
It is well known that the fluid phase equilibria can be represented by a number of {gamma}-models , but unfortunately most of them do not function well under high temperature. In this calculation, we mainly investigate the performance of UNIQUAC and NRTL models under high temperature, using temperature dependent parameters rather than using the original formulas. the other feature of this calculation is that we try to relate the excess Gibbs energy G{sup E}and enthalpy of mixing H{sup E}simultaneously. In other words, we will use the high temperature and pressure G{sup E} and H{sup E}data to regress the temperature dependant parameters to find out which model and what kind of temperature dependant parameters should be used.
A modelling and reasoning framework for social networks policies
Governatori, Guido; Iannella, Renato
2011-02-01
Policy languages (such as privacy and rights) have had little impact on the wider community. Now that social networks have taken off, the need to revisit policy languages and realign them towards social networks requirements has become more apparent. One such language is explored as to its applicability to the social networks masses. We also argue that policy languages alone are not sufficient and thus they should be paired with reasoning mechanisms to provide precise and unambiguous execution models of the policies. To this end, we propose a computationally oriented model to represent, reason with and execute policies for social networks.
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
Ultra-capacitor electrical modeling using temperature dependent parameters
Energy Technology Data Exchange (ETDEWEB)
Lajnef, W.; Briat, O.; Azzopardi, S.; Woirgard, E.; Vinassa, J.M. [Bordeaux-1 Univ., Lab. IXL CNRS UMR 5818 - ENSEIRB, 33 - Talence (France)
2004-07-01
This paper deals with ultra-capacitor electrical modeling. For a proper characterization and identification, a dedicated test bench is designed. First, the ultra-capacitor electric behavior is presented and an electrical model is proposed. The model parameters are identified using a combination of constant currents and frequency response measurements. Then, the temperature dependence of the ultra-capacitor parameters is investigated. Therefore, constant currents and impedance spectroscopy tests are done at different ambient temperatures. Finally, the electrical model parameters are adjusted according to temperature. (authors)
The prediction of maximum temperature for single chips' cooling using artificial neural networks
Ozsunar, Abuzer; Arcaklıoglu, Erol; Nusret Dur, F.
2009-02-01
A CFD simulation usually requires extensive computer storage and lengthy computational time. The application of artificial neural network models to thermal management of chips is still limited. In this study, the main objective is to find a neural network solution for obtaining suitable thickness levels and material for a chip subjected to a constant heat power. To achieve this aim a neural network is trained and tested using the results of the CFD program package Fluent. The back-propagation learning algorithm with three different variants, single layer and logistic sigmoid transfer function is employed in the network. By using the weights of the network, various formulations are designed for the output. The network has resulted in R 2 values of 0.999, and the mean% errors smaller than 0.8 and 0.7 for the training and test data, respectively. The analysis is extended for different thickness and input power values. Comparison of some randomly selected results obtained by the neural network model and the CFD program has yielded a maximum error of 1.8%, mean absolute percentage error of 0.55% and R 2 of 0.99994.
Modeling Temporal Evolution and Multiscale Structure in Networks
DEFF Research Database (Denmark)
Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard
2013-01-01
-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......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...
Gallagher, H. Colin; Robins, Garry
2015-01-01
As part of the shift within second language acquisition (SLA) research toward complex systems thinking, researchers have called for investigations of social network structure. One strand of social network analysis yet to receive attention in SLA is network statistical models, whereby networks are explained in terms of smaller substructures of…
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.
Network Inoculation: Heteroclinics and phase transitions in an epidemic model
Yang, Hui; Gross, Thilo
2016-01-01
In epidemiological modelling, dynamics on networks, and in particular adaptive and heterogeneous networks have recently received much interest. Here we present a detailed analysis of a previously proposed model that combines heterogeneity in the individuals with adaptive rewiring of the network structure in response to a disease. We show that in this model qualitative changes in the dynamics occur in two phase transitions. In a macroscopic description one of these corresponds to a local bifurcation whereas the other one corresponds to a non-local heteroclinic bifurcation. This model thus provides a rare example of a system where a phase transition is caused by a non-local bifurcation, while both micro- and macro-level dynamics are accessible to mathematical analysis. The bifurcation points mark the onset of a behaviour that we call network inoculation. In the respective parameter region exposure of the system to a pathogen will lead to an outbreak that collapses, but leaves the network in a configuration wher...
Random field Ising model and community structure in complex networks
Son, S.-W.; Jeong, H.; Noh, J. D.
2006-04-01
We propose a method to determine the community structure of a complex network. In this method the ground state problem of a ferromagnetic random field Ising model is considered on the network with the magnetic field Bs = +∞, Bt = -∞, and Bi≠s,t=0 for a node pair s and t. The ground state problem is equivalent to the so-called maximum flow problem, which can be solved exactly numerically with the help of a combinatorial optimization algorithm. The community structure is then identified from the ground state Ising spin domains for all pairs of s and t. Our method provides a criterion for the existence of the community structure, and is applicable equally well to unweighted and weighted networks. We demonstrate the performance of the method by applying it to the Barabási-Albert network, Zachary karate club network, the scientific collaboration network, and the stock price correlation network. (Ising, Potts, etc.)
Martínez-Martínez, Víctor; Baladrón, Carlos; Gomez-Gil, Jaime; Ruiz-Ruiz, Gonzalo; Navas-Gracia, Luis M; Aguiar, Javier M; Carro, Belén
2012-10-17
This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed.
Dynamical complexity in the perception-based network formation model
Jo, Hang-Hyun; Moon, Eunyoung
2016-12-01
Many link formation mechanisms for the evolution of social networks have been successful to reproduce various empirical findings in social networks. However, they have largely ignored the fact that individuals make decisions on whether to create links to other individuals based on cost and benefit of linking, and the fact that individuals may use perception of the network in their decision making. In this paper, we study the evolution of social networks in terms of perception-based strategic link formation. Here each individual has her own perception of the actual network, and uses it to decide whether to create a link to another individual. An individual with the least perception accuracy can benefit from updating her perception using that of the most accurate individual via a new link. This benefit is compared to the cost of linking in decision making. Once a new link is created, it affects the accuracies of other individuals' perceptions, leading to a further evolution of the actual network. As for initial actual networks, we consider both homogeneous and heterogeneous cases. The homogeneous initial actual network is modeled by Erdős-Rényi (ER) random networks, while we take a star network for the heterogeneous case. In any cases, individual perceptions of the actual network are modeled by ER random networks with controllable linking probability. Then the stable link density of the actual network is found to show discontinuous transitions or jumps according to the cost of linking. As the number of jumps is the consequence of the dynamical complexity, we discuss the effect of initial conditions on the number of jumps to find that the dynamical complexity strongly depends on how much individuals initially overestimate or underestimate the link density of the actual network. For the heterogeneous case, the role of the highly connected individual as an information spreader is also discussed.
Huisman, Tijs; Boucherie, Richardus J.; van Dijk, N.M.
2002-01-01
The performance of new railway networks cannot be measured or simulated, as no detailed train schedules are available. Railway infrastructure and capacities are to be determined long before the actual traffic is known. This paper therefore proposes a solvable queueing network model to compute
Isami, Shuhei; Nishimori, Hiraku; Awazu, Akinori
2015-01-01
Simple elastic network models of DNA were developed to reveal the structure-dynamics relationships for several nucleotide sequences. First, we propose a simple all-atom elastic network model of DNA that can explain the profiles of temperature factors for several crystal structures of DNA. Second, we propose a coarse-grained elastic network model of DNA, where each nucleotide is described only by one node. This model could effectively reproduce the detailed dynamics obtained with the all-atom elastic network model according to the sequence-dependent geometry. Through normal-mode analysis for the coarse-grained elastic network model, we exhaustively analyzed the dynamic features of a large number of long DNA sequences, approximately $\\sim 150$ bp in length. These analyses revealed positive correlations between the nucleosome-forming abilities and the inter-strand fluctuation strength of double-stranded DNA for several DNA sequences.
Directory of Open Access Journals (Sweden)
Shuhei Isami
Full Text Available Simple elastic network models of DNA were developed to reveal the structure-dynamics relationships for several nucleotide sequences. First, we propose a simple all-atom elastic network model of DNA that can explain the profiles of temperature factors for several crystal structures of DNA. Second, we propose a coarse-grained elastic network model of DNA, where each nucleotide is described only by one node. This model could effectively reproduce the detailed dynamics obtained with the all-atom elastic network model according to the sequence-dependent geometry. Through normal-mode analysis for the coarse-grained elastic network model, we exhaustively analyzed the dynamic features of a large number of long DNA sequences, approximately ∼150 bp in length. These analyses revealed positive correlations between the nucleosome-forming abilities and the inter-strand fluctuation strength of double-stranded DNA for several DNA sequences.
Stochastic modelling and control of communication networks
Zuraniewski, P.W.
2011-01-01
The unprecedented growth of the Information Technologies sector observed within the past years creates an excellent opportunity to conduct new, exciting and interdisciplinary research. Increasing complexity of the communication networks calls for incorporating rigorously developed and reliable
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....... are covered in the categorisation include fixed vs. general networks, specialised vs. general nodes, linear vs. nonlinear costs, single vs. multi commodity, uncapacitated vs. capacitated activities, single vs. multi modal and static vs. dynamic. The models examined address both strategic and tactical planning...
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.
Sanders, Michael J.; Markstrom, Steven L.; Regan, R. Steven; Atkinson, R. Dwight
2017-09-15
A module for simulation of daily mean water temperature in a network of stream segments has been developed as an enhancement to the U.S. Geological Survey Precipitation Runoff Modeling System (PRMS). This new module is based on the U.S. Fish and Wildlife Service Stream Network Temperature model, a mechanistic, one-dimensional heat transport model. The new module is integrated in PRMS. Stream-water temperature simulation is activated by selection of the appropriate input flags in the PRMS Control File and by providing the necessary additional inputs in standard PRMS input files.This report includes a comprehensive discussion of the methods relevant to the stream temperature calculations and detailed instructions for model input preparation.
Information Dynamics in Networks: Models and Algorithms
2016-09-13
ICDCS). 29-JUN-15, Columbus, OH, USA. : , . Value-Based Network Externalities and Optimal Auction Design, Conference on Web and Internet Economics...NAME Total Number: NAME Total Number: PERCENT_SUPPORTEDNAME FTE Equivalent: Total Number: ...... ...... Inventions (DD882) Scientific Progress In...Value-based network externalities and optimal auction design. In Web and Internet Economics - 10th International Conference, WINE 2014, Beijing, China, December 14-17, pages 147–160, 2014. 6
Home-Network Security Model in Ubiquitous Environment
Dong-Young Yoo; Jong-Whoi Shin; Jin-Young Choi
2007-01-01
Social interest and demand on Home-Network has been increasing greatly. Although various services are being introduced to respond to such demands, they can cause serious security problems when linked to the open network such as Internet. This paper reviews the security requirements to protect the service users with assumption that the Home-Network environment is connected to Internet and then proposes the security model based on the requirement. The proposed security mode...
A Three-Dimensional Computational Model of Collagen Network Mechanics
Lee, Byoungkoo; Zhou, Xin; Riching, Kristin; Eliceiri, Kevin W.; Keely, Patricia J.; Guelcher, Scott A.; Weaver, Alissa M.; Jiang, Yi
2014-01-01
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. PMID:25386649
Boltzmann sampling for an XY model using a non-degenerate optical parametric oscillator network
Takeda, Y.; Tamate, S.; Yamamoto, Y.; Takesue, H.; Inagaki, T.; Utsunomiya, S.
2018-01-01
We present an experimental scheme of implementing multiple spins in a classical XY model using a non-degenerate optical parametric oscillator (NOPO) network. We built an NOPO network to simulate a one-dimensional XY Hamiltonian with 5000 spins and externally controllable effective temperatures. The XY spin variables in our scheme are mapped onto the phases of multiple NOPO pulses in a single ring cavity and interactions between XY spins are implemented by mutual injections between NOPOs. We show the steady-state distribution of optical phases of such NOPO pulses is equivalent to the Boltzmann distribution of the corresponding XY model. Estimated effective temperatures converged to the setting values, and the estimated temperatures and the mean energy exhibited good agreement with the numerical simulations of the Langevin dynamics of NOPO phases.
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.
Neurocognitive networks: findings, models, and theory.
Meehan, Timothy P; Bressler, Steven L
2012-11-01
Through its early history, cognitive neuroscience largely followed a modular paradigm wherein high-level cognitive functions were mapped onto locally segregated brain regions. However, recent evidence drives a continuing shift away from modular theories of cognitive brain function, and toward theories which hold that cognition arises from the integrated activity of large-scale, distributed networks of brain regions. A growing consensus favors the fundamental concept of this new paradigm: the large-scale cognitive brain network, or neurocognitive network. This consensus was the motivation for Neurocognitive Networks 2010 (NCN 2010), a conference sponsored by the Cognitive Neuroscience Program of the National Science Foundation, organized by Drs. Steven Bressler and Craig Richter of Florida Atlantic University (FAU), and held at FAU in Boca Raton, FL on January 29-30, 2010. NCN 2010 gathered together some of today's leading investigators of neurocognitive networks. This paper serves to review their presentations as they relate to the paradigm of neurocognitive networks, as well as to compile the emergent themes, questions, and possible future research directions that arose from the conference. Copyright © 2012 Elsevier Ltd. All rights reserved.
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.
Modeling of Bandwidth Aggregation over Heterogeneous Wireless Access Networks
DEFF Research Database (Denmark)
Popovska Avramova, Andrijana; Dittmann, Lars
2012-01-01
Motivated by the multihomming capability of the mobile devices and the fact that the heterogeneous wireless access networks overlap in coverage, mobile operators are looking for solutions that will benefit by simultaneous use of the available multiple access interfaces. Multipath or multilink...... applications. The analysis is performed on a multipath model developed with OPNET Modeler, which is an advanced research tool that supports modeling and integration of various kinds of built-in networks....
When individual behaviour matters: homogeneous and network models in epidemiology
Bansal, Shweta; Grenfell, Bryan T.; Meyers, Lauren Ancel
2007-01-01
Heterogeneity in host contact patterns profoundly shapes population-level disease dynamics. Many epidemiological models make simplifying assumptions about the patterns of disease-causing interactions among hosts. In particular, homogeneous-mixing models assume that all hosts have identical rates of disease-causing contacts. In recent years, several network-based approaches have been developed to explicitly model heterogeneity in host contact patterns. Here, we use a network perspective to qua...
Efficient room temperature hydrogen sensor based on UV-activated ZnO nano-network
Kumar, Mohit; Kumar, Rahul; Rajamani, Saravanan; Ranwa, Sapana; Fanetti, Mattia; Valant, Matjaz; Kumar, Mahesh
2017-09-01
Room temperature hydrogen sensors were fabricated from Au embedded ZnO nano-networks using a 30 mW GaN ultraviolet LED. The Au-decorated ZnO nano-networks were deposited on a SiO2/Si substrate by a chemical vapour deposition process. X-ray diffraction (XRD) spectrum analysis revealed a hexagonal wurtzite structure of ZnO and presence of Au. The ZnO nanoparticles were interconnected, forming nano-network structures. Au nanoparticles were uniformly distributed on ZnO surfaces, as confirmed by FESEM imaging. Interdigitated electrodes (IDEs) were fabricated on the ZnO nano-networks using optical lithography. Sensor performances were measured with and without UV illumination, at room temperate, with concentrations of hydrogen varying from 5 ppm to 1%. The sensor response was found to be ˜21.5% under UV illumination and 0% without UV at room temperature for low hydrogen concentration of 5 ppm. The UV-photoactivated mode enhanced the adsorption of photo-induced O- and O2- ions, and the d-band electron transition from the Au nanoparticles to ZnO—which increased the chemisorbed reaction between hydrogen and oxygen. The sensor response was also measured at 150 °C (without UV illumination) and found to be ˜18% at 5 ppm. Energy efficient low cost hydrogen sensors can be designed and fabricated with the combination of GaN UV LEDs and ZnO nanostructures.
Directory of Open Access Journals (Sweden)
Manjunath Patel Gowdru Chandrashekarappa
2014-01-01
Full Text Available The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN and genetic algorithm neural network (GA-NN. The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equation derived through real experiments carried out earlier by the same authors. The performances of BPNN and GA-NN models are compared among themselves with those of regression for ten test cases. The results show that both models are capable of making better predictions and the models can be effectively used in shop floor in selection of most influential parameters for the desired outputs.
Mathematics of epidemics on networks from exact to approximate models
Kiss, István Z; Simon, Péter L
2017-01-01
This textbook provides an exciting new addition to the area of network science featuring a stronger and more methodical link of models to their mathematical origin and explains how these relate to each other with special focus on epidemic spread on networks. The content of the book is at the interface of graph theory, stochastic processes and dynamical systems. The authors set out to make a significant contribution to closing the gap between model development and the supporting mathematics. This is done by: Summarising and presenting the state-of-the-art in modeling epidemics on networks with results and readily usable models signposted throughout the book; Presenting different mathematical approaches to formulate exact and solvable models; Identifying the concrete links between approximate models and their rigorous mathematical representation; Presenting a model hierarchy and clearly highlighting the links between model assumptions and model complexity; Providing a reference source for advanced undergraduate...
A stochastic model for the analysis of maximum daily temperature
Sirangelo, B.; Caloiero, T.; Coscarelli, R.; Ferrari, E.
2017-10-01
In this paper, a stochastic model for the analysis of the daily maximum temperature is proposed. First, a deseasonalization procedure based on the truncated Fourier expansion is adopted. Then, the Johnson transformation functions were applied for the data normalization. Finally, the fractionally autoregressive integrated moving average model was used to reproduce both short- and long-memory behavior of the temperature series. The model was applied to the data of the Cosenza gauge (Calabria region) and verified on other four gauges of southern Italy. Through a Monte Carlo simulation procedure based on the proposed model, 105 years of daily maximum temperature have been generated. Among the possible applications of the model, the occurrence probabilities of the annual maximum values have been evaluated. Moreover, the procedure was applied for the estimation of the return periods of long sequences of days with maximum temperature above prefixed thresholds.
Modeling seasonal surface temperature variations in secondary tropical dry forests
Cao, Sen; Sanchez-Azofeifa, Arturo
2017-10-01
Secondary tropical dry forests (TDFs) provide important ecosystem services such as carbon sequestration, biodiversity conservation, and nutrient cycle regulation. However, their biogeophysical processes at the canopy-atmosphere interface remain unknown, limiting our understanding of how this endangered ecosystem influences, and responds to the ongoing global warming. To facilitate future development of conservation policies, this study characterized the seasonal land surface temperature (LST) behavior of three successional stages (early, intermediate, and late) of a TDF, at the Santa Rosa National Park (SRNP), Costa Rica. A total of 38 Landsat-8 Thermal Infrared Sensor (TIRS) data and the Surface Reflectance (SR) product were utilized to model LST time series from July 2013 to July 2016 using a radiative transfer equation (RTE) algorithm. We further related the LST time series to seven vegetation indices which reflect different properties of TDFs, and soil moisture data obtained from a Wireless Sensor Network (WSN). Results showed that the LST in the dry season was 15-20 K higher than in the wet season at SRNP. We found that the early successional stages were about 6-8 K warmer than the intermediate successional stages and were 9-10 K warmer than the late successional stages in the middle of the dry season; meanwhile, a minimum LST difference (0-1 K) was observed at the end of the wet season. Leaf phenology and canopy architecture explained most LST variations in both dry and wet seasons. However, our analysis revealed that it is precipitation that ultimately determines the LST variations through both biogeochemical (leaf phenology) and biogeophysical processes (evapotranspiration) of the plants. Results of this study could help physiological modeling studies in secondary TDFs.
Subsurface temperatures in Denmark – measurements and modelling
DEFF Research Database (Denmark)
Balling, N.; Poulsen, Søren Erbs; Bording, Thue Sylvester
Information of subsurface temperature distribution in the Danish area originates from direct measurements in boreholes and from indirect theoretical modelling. “Point observations” of varying quality are available as industrially measured “Bottom Hole Temperatures” from deep exploration boreholes......, and accurate continuous equilibrium temperature logging has been carried out in a number of accessible deep boreholes. A regional distribution of subsurface temperatures is obtained by combining measurements and 3D numerical modelling in which the heat equation is solved. Modelling results are constrained...
Multi-mode clustering model for hierarchical wireless sensor networks
Hu, Xiangdong; Li, Yongfu; Xu, Huifen
2017-03-01
The topology management, i.e., clusters maintenance, of wireless sensor networks (WSNs) is still a challenge due to its numerous nodes, diverse application scenarios and limited resources as well as complex dynamics. To address this issue, a multi-mode clustering model (M2 CM) is proposed to maintain the clusters for hierarchical WSNs in this study. In particular, unlike the traditional time-trigger model based on the whole-network and periodic style, the M2 CM is proposed based on the local and event-trigger operations. In addition, an adaptive local maintenance algorithm is designed for the broken clusters in the WSNs using the spatial-temporal demand changes accordingly. Numerical experiments are performed using the NS2 network simulation platform. Results validate the effectiveness of the proposed model with respect to the network maintenance costs, node energy consumption and transmitted data as well as the network lifetime.
An extended differentiated optical services model for WDM optical networks
Ouyang, Yong; Zeng, Qingji; Wei, Wei
2004-04-01
The need to provide QoS-guaranteed services in the WDM optical networks is becoming increasingly important because of a variety of candidate client networks (e.g., IP, ATM, SONET/SDH) and the requirement for QoS-delivery within the transport layers. This article addresses the QoS problem and presents a framework of QoS provisioning in the WDM optical network. We first characterize the QoS problem in the WDM optical network by comparing with that in the traditional networks. Then we propose a QoS service model in the optical domain called extended differentiated optical services (E-DoS) model based on a set of optical parameters that captures the quality, the reliability and the priority of an optical connection. Each component of the E-DoS model has been analyzed in detail in this article.
Exponential random graph models for networks with community structure.
Fronczak, Piotr; Fronczak, Agata; Bujok, Maksymilian
2013-09-01
Although the community structure organization is an important characteristic of real-world networks, most of the traditional network models fail to reproduce the feature. Therefore, the models are useless as benchmark graphs for testing community detection algorithms. They are also inadequate to predict various properties of real networks. With this paper we intend to fill the gap. We develop an exponential random graph approach to networks with community structure. To this end we mainly built upon the idea of blockmodels. We consider both the classical blockmodel and its degree-corrected counterpart and study many of their properties analytically. We show that in the degree-corrected blockmodel, node degrees display an interesting scaling property, which is reminiscent of what is observed in real-world fractal networks. A short description of Monte Carlo simulations of the models is also given in the hope of being useful to others working in the field.
Dynamic swelling behavior of interpenetrating polymer networks in response to temperature and pH.
Slaughter, Brandon V; Blanchard, Aaron T; Maass, Katie F; Peppas, Nicholas A
2015-06-20
Temperature responsive hydrogels based on ionic polymers exhibit swelling transitions in aqueous solutions as a function of shifting pH and ionic strength, in addition to temperature. Applying these hydrogels to useful applications, particularly for biomedical purposes such as drug delivery and regenerative medicine, is critically dependent on understanding the hydrogel solution responses as a function of all three parameters together. In this work, interpenetrating polymer network (IPN) hydrogels of polyacrylamide and poly(acrylic acid) were formulated over a broad range of synthesis variables using a fractional factorial design, and were examined for equilibrium temperature responsive swelling in a variety of solution conditions. Due to the acidic nature of these IPN hydrogels, usable upper critical solution temperature (UCST) responses for this system occur in mildly acidic environments. Responses were characterized in terms of maximum equilibrium swelling and temperature-triggered swelling using turbidity and gravimetric measurements. Additionally, synthesis parameters critical to achieving optimal overall swelling, temperature-triggered swelling, and sigmoidal temperature transitions for this IPN system were analyzed based on the fractional factorial design used to formulate these hydrogels.
Dynamic modeling of temperature change in outdoor operated tubular photobioreactors.
Androga, Dominic Deo; Uyar, Basar; Koku, Harun; Eroglu, Inci
2017-07-01
In this study, a one-dimensional transient model was developed to analyze the temperature variation of tubular photobioreactors operated outdoors and the validity of the model was tested by comparing the predictions of the model with the experimental data. The model included the effects of convection and radiative heat exchange on the reactor temperature throughout the day. The temperatures in the reactors increased with increasing solar radiation and air temperatures, and the predicted reactor temperatures corresponded well to the measured experimental values. The heat transferred to the reactor was mainly through radiation: the radiative heat absorbed by the reactor medium, ground radiation, air radiation, and solar (direct and diffuse) radiation, while heat loss was mainly through the heat transfer to the cooling water and forced convection. The amount of heat transferred by reflected radiation and metabolic activities of the bacteria and pump work was negligible. Counter-current cooling was more effective in controlling reactor temperature than co-current cooling. The model developed identifies major heat transfer mechanisms in outdoor operated tubular photobioreactors, and accurately predicts temperature changes in these systems. This is useful in determining cooling duty under transient conditions and scaling up photobioreactors. The photobioreactor design and the thermal modeling were carried out and experimental results obtained for the case study of photofermentative hydrogen production by Rhodobacter capsulatus, but the approach is applicable to photobiological systems that are to be operated under outdoor conditions with significant cooling demands.
A temperature-responsive network links cell shape and virulence traits in a primary fungal pathogen.
Directory of Open Access Journals (Sweden)
Sinem Beyhan
2013-07-01
Full Text Available Survival at host temperature is a critical trait for pathogenic microbes of humans. Thermally dimorphic fungal pathogens, including Histoplasma capsulatum, are soil fungi that undergo dramatic changes in cell shape and virulence gene expression in response to host temperature. How these organisms link changes in temperature to both morphologic development and expression of virulence traits is unknown. Here we elucidate a temperature-responsive transcriptional network in H. capsulatum, which switches from a filamentous form in the environment to a pathogenic yeast form at body temperature. The circuit is driven by three highly conserved factors, Ryp1, Ryp2, and Ryp3, that are required for yeast-phase growth at 37°C. Ryp factors belong to distinct families of proteins that control developmental transitions in fungi: Ryp1 is a member of the WOPR family of transcription factors, and Ryp2 and Ryp3 are both members of the Velvet family of proteins whose molecular function is unknown. Here we provide the first evidence that these WOPR and Velvet proteins interact, and that Velvet proteins associate with DNA to drive gene expression. Using genome-wide chromatin immunoprecipitation studies, we determine that Ryp1, Ryp2, and Ryp3 associate with a large common set of genomic loci that includes known virulence genes, indicating that the Ryp factors directly control genes required for pathogenicity in addition to their role in regulating cell morphology. We further dissect the Ryp regulatory circuit by determining that a fourth transcription factor, which we name Ryp4, is required for yeast-phase growth and gene expression, associates with DNA, and displays interdependent regulation with Ryp1, Ryp2, and Ryp3. Finally, we define cis-acting motifs that recruit the Ryp factors to their interwoven network of temperature-responsive target genes. Taken together, our results reveal a positive feedback circuit that directs a broad transcriptional switch between
Constraints and entropy in a model of network evolution
Tee, Philip; Wakeman, Ian; Parisis, George; Dawes, Jonathan; Kiss, István Z.
2017-11-01
Barabási-Albert's "Scale Free" model is the starting point for much of the accepted theory of the evolution of real world communication networks. Careful comparison of the theory with a wide range of real world networks, however, indicates that the model is in some cases, only a rough approximation to the dynamical evolution of real networks. In particular, the exponent γ of the power law distribution of degree is predicted by the model to be exactly 3, whereas in a number of real world networks it has values between 1.2 and 2.9. In addition, the degree distributions of real networks exhibit cut offs at high node degree, which indicates the existence of maximal node degrees for these networks. In this paper we propose a simple extension to the "Scale Free" model, which offers better agreement with the experimental data. This improvement is satisfying, but the model still does not explain why the attachment probabilities should favor high degree nodes, or indeed how constraints arrive in non-physical networks. Using recent advances in the analysis of the entropy of graphs at the node level we propose a first principles derivation for the "Scale Free" and "constraints" model from thermodynamic principles, and demonstrate that both preferential attachment and constraints could arise as a natural consequence of the second law of thermodynamics.
Implicit methods for qualitative modeling of gene regulatory networks.
Garg, Abhishek; Mohanram, Kartik; De Micheli, Giovanni; Xenarios, Ioannis
2012-01-01
Advancements in high-throughput technologies to measure increasingly complex biological phenomena at the genomic level are rapidly changing the face of biological research from the single-gene single-protein experimental approach to studying the behavior of a gene in the context of the entire genome (and proteome). This shift in research methodologies has resulted in a new field of network biology that deals with modeling cellular behavior in terms of network structures such as signaling pathways and gene regulatory networks. In these networks, different biological entities such as genes, proteins, and metabolites interact with each other, giving rise to a dynamical system. Even though there exists a mature field of dynamical systems theory to model such network structures, some technical challenges are unique to biology such as the inability to measure precise kinetic information on gene-gene or gene-protein interactions and the need to model increasingly large networks comprising thousands of nodes. These challenges have renewed interest in developing new computational techniques for modeling complex biological systems. This chapter presents a modeling framework based on Boolean algebra and finite-state machines that are reminiscent of the approach used for digital circuit synthesis and simulation in the field of very-large-scale integration (VLSI). The proposed formalism enables a common mathematical framework to develop computational techniques for modeling different aspects of the regulatory networks such as steady-state behavior, stochasticity, and gene perturbation experiments.
Stochastic S-system modeling of gene regulatory network.
Chowdhury, Ahsan Raja; Chetty, Madhu; Evans, Rob
2015-10-01
Microarray gene expression data can provide insights into biological processes at a system-wide level and is commonly used for reverse engineering gene regulatory networks (GRN). Due to the amalgamation of noise from different sources, microarray expression profiles become inherently noisy leading to significant impact on the GRN reconstruction process. Microarray replicates (both biological and technical), generated to increase the reliability of data obtained under noisy conditions, have limited influence in enhancing the accuracy of reconstruction . Therefore, instead of the conventional GRN modeling approaches which are deterministic, stochastic techniques are becoming increasingly necessary for inferring GRN from noisy microarray data. In this paper, we propose a new stochastic GRN model by investigating incorporation of various standard noise measurements in the deterministic S-system model. Experimental evaluations performed for varying sizes of synthetic network, representing different stochastic processes, demonstrate the effect of noise on the accuracy of genetic network modeling and the significance of stochastic modeling for GRN reconstruction . The proposed stochastic model is subsequently applied to infer the regulations among genes in two real life networks: (1) the well-studied IRMA network, a real-life in-vivo synthetic network constructed within the Saccharomyces cerevisiae yeast, and (2) the SOS DNA repair network in Escherichia coli.
Metrics for evaluating performance and uncertainty of Bayesian network models
Bruce G. Marcot
2012-01-01
This paper presents a selected set of existing and new metrics for gauging Bayesian network model performance and uncertainty. Selected existing and new metrics are discussed for conducting model sensitivity analysis (variance reduction, entropy reduction, case file simulation); evaluating scenarios (influence analysis); depicting model complexity (numbers of model...
Piotrowski, Adam P.; Osuch, Marzena; Napiorkowski, Maciej J.; Rowinski, Pawel M.; Napiorkowski, Jaroslaw J.
2014-03-01
Nature-inspired metaheuristics found various applications in different fields of science, including the problem of artificial neural networks (ANN) training. However, very versatile opinions regarding the performance of metaheuristics applied to ANN training may be found in the literature. Both nature-inspired metaheuristics and ANNs are widely applied to various geophysical and environmental problems. Among them the water temperature forecasting in a natural river, especially in colder climate zones where the seasonality plays important role, is of great importance, as water temperature has strong impact on aquatic life and chemistry. As the impact of possible future climate change on water temperature is not trivial, models are needed to allow projection of streamwater temperature based on simple hydro-meteorological variables. In this paper the detailed comparison of the performance of nature-inspired optimization methods and Levenberg-Marquardt (LM) algorithm in ANNs training is performed, based on the case study of water temperature forecasting in a natural stream, namely Biala Tarnowska river in southern Poland. Over 50 variants of 22 various metaheuristics, including a large number of Differential Evolution, as well as some Particle Swarm Optimization, Evolution Strategies, multialgorithms and Direct Search methods are compared with LM algorithm on ANN training for the described case study. The impact of population size and some control parameters of particular metaheuristics on the ANN training performance are verified. It is found that despite widely claimed large improvement in nature-inspired methods during last years, the vast majority of them are still outperformed by LM algorithm on the selected problem. The only methods that, based on this case study, seem competitive to LM algorithm in terms of the final performance (but not speed) are Differential Evolution algorithms that benefit from the concept of Global and Local neighborhood-based mutation
System-level Modeling of Wireless Integrated Sensor Networks
DEFF Research Database (Denmark)
Virk, Kashif M.; Hansen, Knud; Madsen, Jan
2005-01-01
Wireless integrated sensor networks have emerged as a promising infrastructure for a new generation of monitoring and tracking applications. In order to efficiently utilize the extremely limited resources of wireless sensor nodes, accurate modeling of the key aspects of wireless sensor networks...... is necessary so that system-level design decisions can be made about the hardware and the software (applications and real-time operating system) architecture of sensor nodes. In this paper, we present a SystemC-based abstract modeling framework that enables system-level modeling of sensor network behavior...... by modeling the applications, real-time operating system, sensors, processor, and radio transceiver at the sensor node level and environmental phenomena, including radio signal propagation, at the sensor network level. We demonstrate the potential of our modeling framework by simulating and analyzing a small...
Agent Based Modeling on Organizational Dynamics of Terrorist Network
Directory of Open Access Journals (Sweden)
Bo Li
2015-01-01
Full Text Available 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 are developed for modeling the hybrid relational structure and complex operational processes, respectively. To intuitively elucidate this method, the agent based modeling is used to simulate the terrorist network and test the performance in diverse scenarios. Based on the experimental results, we show how the changes of operational environments affect the development of terrorist organization in terms of its recovery and capacity to perform future tasks. The potential strategies are also discussed, which can be used to restrain the activities of terrorists.
Towards a Social Networks Model for Online Learning & Performance
Chung, Kon Shing Kenneth; Paredes, Walter Christian
2015-01-01
In this study, we develop a theoretical model to investigate the association between social network properties, "content richness" (CR) in academic learning discourse, and performance. CR is the extent to which one contributes content that is meaningful, insightful and constructive to aid learning and by social network properties we…
Energy Model of Networks-on-Chip and a Bus
Wolkotte, P.T.; Smit, Gerardus Johannes Maria; Kavaldjiev, N.K.; Becker, Jens E.; Becker, Jürgen; Nurmi, J.; Takala, J.; Hamalainen, T.D.
2005-01-01
A Network-on-Chip (NoC) is an energy-efficient onchip communication architecture for Multi-Processor Systemon-Chip (MPSoC) architectures. In earlier papers we proposed two Network-on-Chip architectures based on packet-switching and circuit-switching. In this paper we derive an energy model for both
Modeling and optimization of an electric power distribution network ...
African Journals Online (AJOL)
EDNEPP) was solved by a mixed binary integer programming (MBIP) formulation of the network, where the steady-state operation of the network was modelled with non-linear mathematical expressions. The non-linear terms are linearized, using ...
Modelling flow dynamics in water distribution networks using ...
African Journals Online (AJOL)
Computational approaches can be used to detect leakages in water distribution networks. One such approach is the Artificial Neural Networks (ANNs) technique. The advantage of ANNs is that they are robust and can be used to model complex linear and non-linear systems without making implicit assumptions. ANNs can ...