Are the Basins of Tui Regio and Hotei Arcus Sites of Former Titanian Seas?

NASA Technical Reports Server (NTRS)

Moore, Jeffrey Morgan; Howard, Alan

2012-01-01

Features observed in the basins of Tui Regio and Hotei Arcus on Titan have attracted the attention of the Cassini-era investigators. At both locations, VIMS observed discrete 5-micron bright approx.500-km wide features described as lobate in shape. Several studies have proposed that these materials are cryo-volcanic flows; in the case of the Hotei Arcus feature this inference was buttressed with SAR RADAR images showing bright and dark patches with lobate margins. We propose an alternative explanation. First we note that all landforms on Titan that are unambiguously identifiable can be explained by exogenic processes (aeolian, fluvial, impact cratering, and mass wasting). Suggestions of endogenically produced cryovolcanic constructs and flows have, without exception, lacked conclusive diagnostic evidence. Recently published topographic profiles across Tui Regio and the lobate feature region north of Hotei Arcus indicate these features appear to occur in large regional basins, at least along the direction of the profiles. SAR images show that the terrains surrounding both 5-micron bright features exhibit fluvial networks that appear to converge and debauch into the probable basins. The 5-micron bright features themselves correspond to fields of discrete radar-bright depressions whose bounding edges are commonly rounded and cumulate in planform in SAR images. These fields of discrete radar-bright depressions strongly resemble fields of features seen at Titan s high latitudes usually attributed to be dry lakes. Thus the combination of (1) the resemblance to high-latitude dry lakes, (2) location in the centers of probable regional depressions, and (3) convergence of fluvial networks are inferred by us to best explain the 5-micron bright regions at Tui Regio and Hotei Arcus as sites of dry seas or at least paleolake clusters. Such equatorial seas, if real, may be evidence of substantially larger inventories of liquid alkanes in Titan s past.

Network simulation using the simulation language for alternate modeling (SLAM 2)

NASA Technical Reports Server (NTRS)

Shen, S.; Morris, D. W.

1983-01-01

The simulation language for alternate modeling (SLAM 2) is a general purpose language that combines network, discrete event, and continuous modeling capabilities in a single language system. The efficacy of the system's network modeling is examined and discussed. Examples are given of the symbolism that is used, and an example problem and model are derived. The results are discussed in terms of the ease of programming, special features, and system limitations. The system offers many features which allow rapid model development and provides an informative standardized output. The system also has limitations which may cause undetected errors and misleading reports unless the user is aware of these programming characteristics.

Hydrophobic-domain-dependent protein-protein interactions mediate the localization of GPAT enzymes to ER subdomains

USDA-ARS?s Scientific Manuscript database

The endoplasmic reticulum (ER) is a dynamic network that consists of numerous regions or subdomains with discrete morphological features and functional properties, including those involved in protein and oil-body formation, anterograde transport of secretory proteins, the exchange of macromolecules ...

Projective Ponzano-Regge spin networks and their symmetries

NASA Astrophysics Data System (ADS)

Aquilanti, Vincenzo; Marzuoli, Annalisa

2018-02-01

We present a novel hierarchical construction of projective spin networks of the Ponzano-Regge type from an assembling of five quadrangles up to the combinatorial 4-simplex compatible with a geometrical realization in Euclidean 4-space. The key ingredients are the projective Desargues configuration and the incidence structure given by its space-dual, on the one hand, and the Biedenharn-Elliott identity for the 6j symbol of SU(2), on the other. The interplay between projective-combinatorial and algebraic features relies on the recoupling theory of angular momenta, an approach to discrete quantum gravity models carried out successfully over the last few decades. The role of Regge symmetry-an intriguing discrete symmetry of the 6j which goes beyond the standard tetrahedral symmetry of this symbol-will be also discussed in brief to highlight its role in providing a natural regularization of projective spin networks that somehow mimics the standard regularization through a q-deformation of SU(2).

Transmembrane domain-dependent protein-protein interactions participate in the localization of GPAT enzymes to ER subdomains

USDA-ARS?s Scientific Manuscript database

The endoplasmic reticulum (ER) is a dynamic network that consists of numerous regions or subdomains with discrete morphological features and functional properties, including those involved in protein and oil-body formation, anterograde transport of secretory proteins, the exchange of macromolecules ...

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

NASA Astrophysics Data System (ADS)

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

2015-10-01

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

Biochemical Network Stochastic Simulator (BioNetS): software for stochastic modeling of biochemical networks.

PubMed

Adalsteinsson, David; McMillen, David; Elston, Timothy C

2004-03-08

Intrinsic fluctuations due to the stochastic nature of biochemical reactions can have large effects on the response of biochemical networks. This is particularly true for pathways that involve transcriptional regulation, where generally there are two copies of each gene and the number of messenger RNA (mRNA) molecules can be small. Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks. We have developed the software package Biochemical Network Stochastic Simulator (BioNetS) for efficiently and accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous) for each chemical species in the network. The discrete variables are simulated using an efficient implementation of the Gillespie algorithm. For the continuous random variables, BioNetS constructs and numerically solves the appropriate chemical Langevin equations. The software package has been developed to scale efficiently with network size, thereby allowing large systems to be studied. BioNetS runs as a BioSpice agent and can be downloaded from http://www.biospice.org. BioNetS also can be run as a stand alone package. All the required files are accessible from http://x.amath.unc.edu/BioNetS. We have developed BioNetS to be a reliable tool for studying the stochastic dynamics of large biochemical networks. Important features of BioNetS are its ability to handle hybrid models that consist of both continuous and discrete random variables and its ability to model cell growth and division. We have verified the accuracy and efficiency of the numerical methods by considering several test systems.

A constrained Delaunay discretization method for adaptively meshing highly discontinuous geological media

NASA Astrophysics Data System (ADS)

Wang, Yang; Ma, Guowei; Ren, Feng; Li, Tuo

2017-12-01

A constrained Delaunay discretization method is developed to generate high-quality doubly adaptive meshes of highly discontinuous geological media. Complex features such as three-dimensional discrete fracture networks (DFNs), tunnels, shafts, slopes, boreholes, water curtains, and drainage systems are taken into account in the mesh generation. The constrained Delaunay triangulation method is used to create adaptive triangular elements on planar fractures. Persson's algorithm (Persson, 2005), based on an analogy between triangular elements and spring networks, is enriched to automatically discretize a planar fracture into mesh points with varying density and smooth-quality gradient. The triangulated planar fractures are treated as planar straight-line graphs (PSLGs) to construct piecewise-linear complex (PLC) for constrained Delaunay tetrahedralization. This guarantees the doubly adaptive characteristic of the resulted mesh: the mesh is adaptive not only along fractures but also in space. The quality of elements is compared with the results from an existing method. It is verified that the present method can generate smoother elements and a better distribution of element aspect ratios. Two numerical simulations are implemented to demonstrate that the present method can be applied to various simulations of complex geological media that contain a large number of discontinuities.

A 181 GOPS AKAZE Accelerator Employing Discrete-Time Cellular Neural Networks for Real-Time Feature Extraction.

PubMed

Jiang, Guangli; Liu, Leibo; Zhu, Wenping; Yin, Shouyi; Wei, Shaojun

2015-09-04

This paper proposes a real-time feature extraction VLSI architecture for high-resolution images based on the accelerated KAZE algorithm. Firstly, a new system architecture is proposed. It increases the system throughput, provides flexibility in image resolution, and offers trade-offs between speed and scaling robustness. The architecture consists of a two-dimensional pipeline array that fully utilizes computational similarities in octaves. Secondly, a substructure (block-serial discrete-time cellular neural network) that can realize a nonlinear filter is proposed. This structure decreases the memory demand through the removal of data dependency. Thirdly, a hardware-friendly descriptor is introduced in order to overcome the hardware design bottleneck through the polar sample pattern; a simplified method to realize rotation invariance is also presented. Finally, the proposed architecture is designed in TSMC 65 nm CMOS technology. The experimental results show a performance of 127 fps in full HD resolution at 200 MHz frequency. The peak performance reaches 181 GOPS and the throughput is double the speed of other state-of-the-art architectures.

Modelling the structural controls of primary kaolinite formation

NASA Astrophysics Data System (ADS)

Tierney, R. L.; Glass, H. J.

2016-09-01

An abundance of kaolinite was formed within the St. Austell outcrop of the Cornubian batholith in Cornwall, southwest England, by the hydrous dissolution of feldspar crystals. The permeability of Cornish granites is low and alteration acts pervasively from discontinuity features, with montmorillonite recognised as an intermediate assemblage in partially kaolinised material. Structural features allowed fluids to channel through the impermeable granite and pervade deep into the rock. Areas of high structural control are hypothesised to link well with areas of advanced alteration. As kaolinisation results in a loss of competence, we present a method of utilising discontinuity orientations from nearby unaltered granites alongside the local tectonic history to calculate strain rates and delineate a discrete fracture network. Simulation of the discrete fracture network is demonstrated through a case study at Higher Moor, where kaolinite is actively extracted from a pit. Reconciliation of fracture connectivity and permeability against measured subsurface data show that higher values of modelled properties match with advanced kaolinisation observed in the field. This suggests that the technique may be applicable across various industries and disciplines.

Features and heterogeneities in growing network models

NASA Astrophysics Data System (ADS)

Ferretti, Luca; Cortelezzi, Michele; Yang, Bin; Marmorini, Giacomo; Bianconi, Ginestra

2012-06-01

Many complex networks from the World Wide Web to biological networks grow taking into account the heterogeneous features of the nodes. The feature of a node might be a discrete quantity such as a classification of a URL document such as personal page, thematic website, news, blog, search engine, social network, etc., or the classification of a gene in a functional module. Moreover the feature of a node can be a continuous variable such as the position of a node in the embedding space. In order to account for these properties, in this paper we provide a generalization of growing network models with preferential attachment that includes the effect of heterogeneous features of the nodes. The main effect of heterogeneity is the emergence of an “effective fitness” for each class of nodes, determining the rate at which nodes acquire new links. The degree distribution exhibits a multiscaling behavior analogous to the the fitness model. This property is robust with respect to variations in the model, as long as links are assigned through effective preferential attachment. Beyond the degree distribution, in this paper we give a full characterization of the other relevant properties of the model. We evaluate the clustering coefficient and show that it disappears for large network size, a property shared with the Barabási-Albert model. Negative degree correlations are also present in this class of models, along with nontrivial mixing patterns among features. We therefore conclude that both small clustering coefficients and disassortative mixing are outcomes of the preferential attachment mechanism in general growing networks.

dfnWorks: A discrete fracture network framework for modeling subsurface flow and transport

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hyman, Jeffrey D.; Karra, Satish; Makedonska, Nataliia

DFNWORKS is a parallelized computational suite to generate three-dimensional discrete fracture networks (DFN) and simulate flow and transport. Developed at Los Alamos National Laboratory over the past five years, it has been used to study flow and transport in fractured media at scales ranging from millimeters to kilometers. The networks are created and meshed using DFNGEN, which combines FRAM (the feature rejection algorithm for meshing) methodology to stochastically generate three-dimensional DFNs with the LaGriT meshing toolbox to create a high-quality computational mesh representation. The representation produces a conforming Delaunay triangulation suitable for high performance computing finite volume solvers in anmore » intrinsically parallel fashion. Flow through the network is simulated in dfnFlow, which utilizes the massively parallel subsurface flow and reactive transport finite volume code PFLOTRAN. A Lagrangian approach to simulating transport through the DFN is adopted within DFNTRANS to determine pathlines and solute transport through the DFN. Example applications of this suite in the areas of nuclear waste repository science, hydraulic fracturing and CO 2 sequestration are also included.« less

dfnWorks: A discrete fracture network framework for modeling subsurface flow and transport

DOE PAGES

Hyman, Jeffrey D.; Karra, Satish; Makedonska, Nataliia; ...

2015-11-01

DFNWORKS is a parallelized computational suite to generate three-dimensional discrete fracture networks (DFN) and simulate flow and transport. Developed at Los Alamos National Laboratory over the past five years, it has been used to study flow and transport in fractured media at scales ranging from millimeters to kilometers. The networks are created and meshed using DFNGEN, which combines FRAM (the feature rejection algorithm for meshing) methodology to stochastically generate three-dimensional DFNs with the LaGriT meshing toolbox to create a high-quality computational mesh representation. The representation produces a conforming Delaunay triangulation suitable for high performance computing finite volume solvers in anmore » intrinsically parallel fashion. Flow through the network is simulated in dfnFlow, which utilizes the massively parallel subsurface flow and reactive transport finite volume code PFLOTRAN. A Lagrangian approach to simulating transport through the DFN is adopted within DFNTRANS to determine pathlines and solute transport through the DFN. Example applications of this suite in the areas of nuclear waste repository science, hydraulic fracturing and CO 2 sequestration are also included.« less

Diagenetic Features in Yellowknife Bay, Gale Crater, Mars: Implications for Substrate Rheology and Potential Gas Release

NASA Technical Reports Server (NTRS)

Kah, L. C.; Stack, K; Siebach, K.; Grotzinger, J.; Summer, D.; Farien, A.; Oehler, D.; Schieber, J.; Leville, R.; Edgar, L;

Comparative analysis of two discretizations of Ricci curvature for complex networks.

PubMed

Samal, Areejit; Sreejith, R P; Gu, Jiao; Liu, Shiping; Saucan, Emil; Jost, Jürgen

2018-06-05

We have performed an empirical comparison of two distinct notions of discrete Ricci curvature for graphs or networks, namely, the Forman-Ricci curvature and Ollivier-Ricci curvature. Importantly, these two discretizations of the Ricci curvature were developed based on different properties of the classical smooth notion, and thus, the two notions shed light on different aspects of network structure and behavior. Nevertheless, our extensive computational analysis in a wide range of both model and real-world networks shows that the two discretizations of Ricci curvature are highly correlated in many networks. Moreover, we show that if one considers the augmented Forman-Ricci curvature which also accounts for the two-dimensional simplicial complexes arising in graphs, the observed correlation between the two discretizations is even higher, especially, in real networks. Besides the potential theoretical implications of these observations, the close relationship between the two discretizations has practical implications whereby Forman-Ricci curvature can be employed in place of Ollivier-Ricci curvature for faster computation in larger real-world networks whenever coarse analysis suffices.

Machine Learning Topological Invariants with Neural Networks

NASA Astrophysics Data System (ADS)

Zhang, Pengfei; Shen, Huitao; Zhai, Hui

2018-02-01

In this Letter we supervisedly train neural networks to distinguish different topological phases in the context of topological band insulators. After training with Hamiltonians of one-dimensional insulators with chiral symmetry, the neural network can predict their topological winding numbers with nearly 100% accuracy, even for Hamiltonians with larger winding numbers that are not included in the training data. These results show a remarkable success that the neural network can capture the global and nonlinear topological features of quantum phases from local inputs. By opening up the neural network, we confirm that the network does learn the discrete version of the winding number formula. We also make a couple of remarks regarding the role of the symmetry and the opposite effect of regularization techniques when applying machine learning to physical systems.

Optimal region of latching activity in an adaptive Potts model for networks of neurons

NASA Astrophysics Data System (ADS)

Abdollah-nia, Mohammad-Farshad; Saeedghalati, Mohammadkarim; Abbassian, Abdolhossein

2012-02-01

In statistical mechanics, the Potts model is a model for interacting spins with more than two discrete states. Neural networks which exhibit features of learning and associative memory can also be modeled by a system of Potts spins. A spontaneous behavior of hopping from one discrete attractor state to another (referred to as latching) has been proposed to be associated with higher cognitive functions. Here we propose a model in which both the stochastic dynamics of Potts models and an adaptive potential function are present. A latching dynamics is observed in a limited region of the noise(temperature)-adaptation parameter space. We hence suggest noise as a fundamental factor in such alternations alongside adaptation. From a dynamical systems point of view, the noise-adaptation alternations may be the underlying mechanism for multi-stability in attractor-based models. An optimality criterion for realistic models is finally inferred.

100 Gb/s optical discrete multi-tone transceivers for intra- and inter-datacenter networks

NASA Astrophysics Data System (ADS)

Okabe, Ryo; Tanaka, Toshiki; Nishihara, Masato; Kai, Yutaka; Takahara, Tomoo; Liu, Bo; Li, Lei; Tao, Zhenning; Rasmussen, Jens C.

2016-03-01

Discrete multi-tone (DMT) technology is an attractive modulation technology for short-reach application due to its high spectral efficiency and simple configuration. In this paper, we first explain the features of DMT technology then discuss the impact of fiber dispersion and chirp on the frequency responses of the DMT signal and the importance in the relationship between chirp and the optical transmission band. Next, we explain our experiments of 100-Gb/s DMT transmission of 10 km in the O-band using directly modulated lasers for low-cost application. In an inter-datacenter network of more than several tens of kilometers, fiber dispersion mainly limits system performance. We also discuss our experiment of 100-Gb/s DMT transmission up to 100 km in the C-band without a dispersion compensator by using vestigial sideband spectrum shaping and nonlinear compensation.

Convergence of Asymptotic Systems of Non-autonomous Neural Network Models with Infinite Distributed Delays

NASA Astrophysics Data System (ADS)

Oliveira, José J.

2017-10-01

In this paper, we investigate the global convergence of solutions of non-autonomous Hopfield neural network models with discrete time-varying delays, infinite distributed delays, and possible unbounded coefficient functions. Instead of using Lyapunov functionals, we explore intrinsic features between the non-autonomous systems and their asymptotic systems to ensure the boundedness and global convergence of the solutions of the studied models. Our results are new and complement known results in the literature. The theoretical analysis is illustrated with some examples and numerical simulations.

Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

PubMed

Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.

Reducing Neuronal Networks to Discrete Dynamics

PubMed Central

Terman, David; Ahn, Sungwoo; Wang, Xueying; Just, Winfried

2008-01-01

We consider a general class of purely inhibitory and excitatory-inhibitory neuronal networks, with a general class of network architectures, and characterize the complex firing patterns that emerge. Our strategy for studying these networks is to first reduce them to a discrete model. In the discrete model, each neuron is represented as a finite number of states and there are rules for how a neuron transitions from one state to another. In this paper, we rigorously demonstrate that the continuous neuronal model can be reduced to the discrete model if the intrinsic and synaptic properties of the cells are chosen appropriately. In a companion paper [1], we analyze the discrete model. PMID:18443649

Neural-Network-Based Robust Optimal Tracking Control for MIMO Discrete-Time Systems With Unknown Uncertainty Using Adaptive Critic Design.

PubMed

Liu, Lei; Wang, Zhanshan; Zhang, Huaguang

2018-04-01

This paper is concerned with the robust optimal tracking control strategy for a class of nonlinear multi-input multi-output discrete-time systems with unknown uncertainty via adaptive critic design (ACD) scheme. The main purpose is to establish an adaptive actor-critic control method, so that the cost function in the procedure of dealing with uncertainty is minimum and the closed-loop system is stable. Based on the neural network approximator, an action network is applied to generate the optimal control signal and a critic network is used to approximate the cost function, respectively. In contrast to the previous methods, the main features of this paper are: 1) the ACD scheme is integrated into the controllers to cope with the uncertainty and 2) a novel cost function, which is not in quadric form, is proposed so that the total cost in the design procedure is reduced. It is proved that the optimal control signals and the tracking errors are uniformly ultimately bounded even when the uncertainty exists. Finally, a numerical simulation is developed to show the effectiveness of the present approach.

Visualization and Hierarchical Analysis of Flow in Discrete Fracture Network Models

NASA Astrophysics Data System (ADS)

Aldrich, G. A.; Gable, C. W.; Painter, S. L.; Makedonska, N.; Hamann, B.; Woodring, J.

2013-12-01

Flow and transport in low permeability fractured rock is primary in interconnected fracture networks. Prediction and characterization of flow and transport in fractured rock has important implications in underground repositories for hazardous materials (eg. nuclear and chemical waste), contaminant migration and remediation, groundwater resource management, and hydrocarbon extraction. We have developed methods to explicitly model flow in discrete fracture networks and track flow paths using passive particle tracking algorithms. Visualization and analysis of particle trajectory through the fracture network is important to understanding fracture connectivity, flow patterns, potential contaminant pathways and fast paths through the network. However, occlusion due to the large number of highly tessellated and intersecting fracture polygons preclude the effective use of traditional visualization methods. We would also like quantitative analysis methods to characterize the trajectory of a large number of particle paths. We have solved these problems by defining a hierarchal flow network representing the topology of particle flow through the fracture network. This approach allows us to analyses the flow and the dynamics of the system as a whole. We are able to easily query the flow network, and use paint-and-link style framework to filter the fracture geometry and particle traces based on the flow analytics. This allows us to greatly reduce occlusion while emphasizing salient features such as the principal transport pathways. Examples are shown that demonstrate the methodology and highlight how use of this new method allows quantitative analysis and characterization of flow and transport in a number of representative fracture networks.

Implementation Strategies for Large-Scale Transport Simulations Using Time Domain Particle Tracking

NASA Astrophysics Data System (ADS)

Painter, S.; Cvetkovic, V.; Mancillas, J.; Selroos, J.

2008-12-01

Time domain particle tracking is an emerging alternative to the conventional random walk particle tracking algorithm. With time domain particle tracking, particles are moved from node to node on one-dimensional pathways defined by streamlines of the groundwater flow field or by discrete subsurface features. The time to complete each deterministic segment is sampled from residence time distributions that include the effects of advection, longitudinal dispersion, a variety of kinetically controlled retention (sorption) processes, linear transformation, and temporal changes in groundwater velocities and sorption parameters. The simulation results in a set of arrival times at a monitoring location that can be post-processed with a kernel method to construct mass discharge (breakthrough) versus time. Implementation strategies differ for discrete flow (fractured media) systems and continuous porous media systems. The implementation strategy also depends on the scale at which hydraulic property heterogeneity is represented in the supporting flow model. For flow models that explicitly represent discrete features (e.g., discrete fracture networks), the sampling of residence times along segments is conceptually straightforward. For continuous porous media, such sampling needs to be related to the Lagrangian velocity field. Analytical or semi-analytical methods may be used to approximate the Lagrangian segment velocity distributions in aquifers with low-to-moderate variability, thereby capturing transport effects of subgrid velocity variability. If variability in hydraulic properties is large, however, Lagrangian velocity distributions are difficult to characterize and numerical simulations are required; in particular, numerical simulations are likely to be required for estimating the velocity integral scale as a basis for advective segment distributions. Aquifers with evolving heterogeneity scales present additional challenges. Large-scale simulations of radionuclide transport at two potential repository sites for high-level radioactive waste will be used to demonstrate the potential of the method. The simulations considered approximately 1000 source locations, multiple radionuclides with contrasting sorption properties, and abrupt changes in groundwater velocity associated with future glacial scenarios. Transport pathways linking the source locations to the accessible environment were extracted from discrete feature flow models that include detailed representations of the repository construction (tunnels, shafts, and emplacement boreholes) embedded in stochastically generated fracture networks. Acknowledgment The authors are grateful to SwRI Advisory Committee for Research, the Swedish Nuclear Fuel and Waste Management Company, and Posiva Oy for financial support.

TOUGH-RBSN simulator for hydraulic fracture propagation within fractured media: Model validations against laboratory experiments

NASA Astrophysics Data System (ADS)

Kim, Kunhwi; Rutqvist, Jonny; Nakagawa, Seiji; Birkholzer, Jens

2017-11-01

This paper presents coupled hydro-mechanical modeling of hydraulic fracturing processes in complex fractured media using a discrete fracture network (DFN) approach. The individual physical processes in the fracture propagation are represented by separate program modules: the TOUGH2 code for multiphase flow and mass transport based on the finite volume approach; and the rigid-body-spring network (RBSN) model for mechanical and fracture-damage behavior, which are coupled with each other. Fractures are modeled as discrete features, of which the hydrological properties are evaluated from the fracture deformation and aperture change. The verification of the TOUGH-RBSN code is performed against a 2D analytical model for single hydraulic fracture propagation. Subsequently, modeling capabilities for hydraulic fracturing are demonstrated through simulations of laboratory experiments conducted on rock-analogue (soda-lime glass) samples containing a designed network of pre-existing fractures. Sensitivity analyses are also conducted by changing the modeling parameters, such as viscosity of injected fluid, strength of pre-existing fractures, and confining stress conditions. The hydraulic fracturing characteristics attributed to the modeling parameters are investigated through comparisons of the simulation results.

Modern Workflows for Fracture Rock Hydrogeology

NASA Astrophysics Data System (ADS)

Doe, T.

2015-12-01

Discrete Fracture Network (DFN) is a numerical simulation approach that represents a conducting fracture network using geologically realistic geometries and single-conductor hydraulic and transport properties. In terms of diffusion analogues, equivalent porous media derive from heat conduction in continuous media, while DFN simulation is more similar to electrical flow and diffusion in circuits with discrete pathways. DFN modeling grew out of pioneering work of David Snow in the late 1960s with additional impetus in the 1970's from the development of the development of stochastic approaches for describing of fracture geometric and hydrologic properties. Research in underground test facilities for radioactive waste disposal developed the necessary linkages between characterization technologies and simulation as well as bringing about a hybrid deterministic stochastic approach. Over the past 40 years DFN simulation and characterization methods have moved from the research environment into practical, commercial application. The key geologic, geophysical and hydrologic tools provide the required DFN inputs of conductive fracture intensity, orientation, and transmissivity. Flow logging either using downhole tool or by detailed packer testing identifies the locations of conducting features in boreholes, and image logging provides information on the geology and geometry of the conducting features. Multi-zone monitoring systems isolate the individual conductors, and with subsequent drilling and characterization perturbations help to recognize connectivity and compartmentalization in the fracture network. Tracer tests and core analysis provide critical information on the transport properties especially matrix diffusion unidentified conducting pathways. Well test analyses incorporating flow dimension boundary effects provide further constraint on the conducting geometry of the fracture network.

Application of network methods for understanding evolutionary dynamics in discrete habitats.

PubMed

Greenbaum, Gili; Fefferman, Nina H

2017-06-01

In populations occupying discrete habitat patches, gene flow between habitat patches may form an intricate population structure. In such structures, the evolutionary dynamics resulting from interaction of gene-flow patterns with other evolutionary forces may be exceedingly complex. Several models describing gene flow between discrete habitat patches have been presented in the population-genetics literature; however, these models have usually addressed relatively simple settings of habitable patches and have stopped short of providing general methodologies for addressing nontrivial gene-flow patterns. In the last decades, network theory - a branch of discrete mathematics concerned with complex interactions between discrete elements - has been applied to address several problems in population genetics by modelling gene flow between habitat patches using networks. Here, we present the idea and concepts of modelling complex gene flows in discrete habitats using networks. Our goal is to raise awareness to existing network theory applications in molecular ecology studies, as well as to outline the current and potential contribution of network methods to the understanding of evolutionary dynamics in discrete habitats. We review the main branches of network theory that have been, or that we believe potentially could be, applied to population genetics and molecular ecology research. We address applications to theoretical modelling and to empirical population-genetic studies, and we highlight future directions for extending the integration of network science with molecular ecology. © 2017 John Wiley & Sons Ltd.

Counting and classifying attractors in high dimensional dynamical systems.

PubMed

Bagley, R J; Glass, L

1996-12-07

Randomly connected Boolean networks have been used as mathematical models of neural, genetic, and immune systems. A key quantity of such networks is the number of basins of attraction in the state space. The number of basins of attraction changes as a function of the size of the network, its connectivity and its transition rules. In discrete networks, a simple count of the number of attractors does not reveal the combinatorial structure of the attractors. These points are illustrated in a reexamination of dynamics in a class of random Boolean networks considered previously by Kauffman. We also consider comparisons between dynamics in discrete networks and continuous analogues. A continuous analogue of a discrete network may have a different number of attractors for many different reasons. Some attractors in discrete networks may be associated with unstable dynamics, and several different attractors in a discrete network may be associated with a single attractor in the continuous case. Special problems in determining attractors in continuous systems arise when there is aperiodic dynamics associated with quasiperiodicity of deterministic chaos.

Qualitative dynamics semantics for SBGN process description.

PubMed

Rougny, Adrien; Froidevaux, Christine; Calzone, Laurence; Paulevé, Loïc

2016-06-16

Qualitative dynamics semantics provide a coarse-grain modeling of networks dynamics by abstracting away kinetic parameters. They allow to capture general features of systems dynamics, such as attractors or reachability properties, for which scalable analyses exist. The Systems Biology Graphical Notation Process Description language (SBGN-PD) has become a standard to represent reaction networks. However, no qualitative dynamics semantics taking into account all the main features available in SBGN-PD had been proposed so far. We propose two qualitative dynamics semantics for SBGN-PD reaction networks, namely the general semantics and the stories semantics, that we formalize using asynchronous automata networks. While the general semantics extends standard Boolean semantics of reaction networks by taking into account all the main features of SBGN-PD, the stories semantics allows to model several molecules of a network by a unique variable. The obtained qualitative models can be checked against dynamical properties and therefore validated with respect to biological knowledge. We apply our framework to reason on the qualitative dynamics of a large network (more than 200 nodes) modeling the regulation of the cell cycle by RB/E2F. The proposed semantics provide a direct formalization of SBGN-PD networks in dynamical qualitative models that can be further analyzed using standard tools for discrete models. The dynamics in stories semantics have a lower dimension than the general one and prune multiple behaviors (which can be considered as spurious) by enforcing the mutual exclusiveness between the activity of different nodes of a same story. Overall, the qualitative semantics for SBGN-PD allow to capture efficiently important dynamical features of reaction network models and can be exploited to further refine them.

Determining Regulatory Networks Governing the Differentiation of Embryonic Stem Cells to Pancreatic Lineage

NASA Astrophysics Data System (ADS)

Banerjee, Ipsita

2009-03-01

Knowledge of pathways governing cellular differentiation to specific phenotype will enable generation of desired cell fates by careful alteration of the governing network by adequate manipulation of the cellular environment. With this aim, we have developed a novel method to reconstruct the underlying regulatory architecture of a differentiating cell population from discrete temporal gene expression data. We utilize an inherent feature of biological networks, that of sparsity, in formulating the network reconstruction problem as a bi-level mixed-integer programming problem. The formulation optimizes the network topology at the upper level and the network connectivity strength at the lower level. The method is first validated by in-silico data, before applying it to the complex system of embryonic stem (ES) cell differentiation. This formulation enables efficient identification of the underlying network topology which could accurately predict steps necessary for directing differentiation to subsequent stages. Concurrent experimental verification demonstrated excellent agreement with model prediction.

Adaptive feedback synchronisation of complex dynamical network with discrete-time communications and delayed nodes

NASA Astrophysics Data System (ADS)

Wang, Tong; Ding, Yongsheng; Zhang, Lei; Hao, Kuangrong

2016-08-01

This paper considered the synchronisation of continuous complex dynamical networks with discrete-time communications and delayed nodes. The nodes in the dynamical networks act in the continuous manner, while the communications between nodes are discrete-time; that is, they communicate with others only at discrete time instants. The communication intervals in communication period can be uncertain and variable. By using a piecewise Lyapunov-Krasovskii function to govern the characteristics of the discrete communication instants, we investigate the adaptive feedback synchronisation and a criterion is derived to guarantee the existence of the desired controllers. The globally exponential synchronisation can be achieved by the controllers under the updating laws. Finally, two numerical examples including globally coupled network and nearest-neighbour coupled networks are presented to demonstrate the validity and effectiveness of the proposed control scheme.

Luminance sticker based facial expression recognition using discrete wavelet transform for physically disabled persons.

PubMed

Nagarajan, R; Hariharan, M; Satiyan, M

2012-08-01

Developing tools to assist physically disabled and immobilized people through facial expression is a challenging area of research and has attracted many researchers recently. In this paper, luminance stickers based facial expression recognition is proposed. Recognition of facial expression is carried out by employing Discrete Wavelet Transform (DWT) as a feature extraction method. Different wavelet families with their different orders (db1 to db20, Coif1 to Coif 5 and Sym2 to Sym8) are utilized to investigate their performance in recognizing facial expression and to evaluate their computational time. Standard deviation is computed for the coefficients of first level of wavelet decomposition for every order of wavelet family. This standard deviation is used to form a set of feature vectors for classification. In this study, conventional validation and cross validation are performed to evaluate the efficiency of the suggested feature vectors. Three different classifiers namely Artificial Neural Network (ANN), k-Nearest Neighborhood (kNN) and Linear Discriminant Analysis (LDA) are used to classify a set of eight facial expressions. The experimental results demonstrate that the proposed method gives very promising classification accuracies.

Pulse propagation in discrete excitatory networks of integrate-and-fire neurons.

PubMed

Badel, Laurent; Tonnelier, Arnaud

2004-07-01

We study the propagation of solitary waves in a discrete excitatory network of integrate-and-fire neurons. We show the existence and the stability of a fast wave and a family of slow waves. Fast waves are similar to those already described in continuum networks. Stable slow waves have not been previously reported in purely excitatory networks and their propagation is particular to the discrete nature of the network. The robustness of our results is studied in the presence of noise.

Extraction of texture features with a multiresolution neural network

NASA Astrophysics Data System (ADS)

Lepage, Richard; Laurendeau, Denis; Gagnon, Roger A.

1992-09-01

Texture is an important surface characteristic. Many industrial materials such as wood, textile, or paper are best characterized by their texture. Detection of defaults occurring on such materials or classification for quality control anD matching can be carried out through careful texture analysis. A system for the classification of pieces of wood used in the furniture industry is proposed. This paper is concerned with a neural network implementation of the features extraction and classification components of the proposed system. Texture appears differently depending at which spatial scale it is observed. A complete description of a texture thus implies an analysis at several spatial scales. We propose a compact pyramidal representation of the input image for multiresolution analysis. The feature extraction system is implemented on a multilayer artificial neural network. Each level of the pyramid, which is a representation of the input image at a given spatial resolution scale, is mapped into a layer of the neural network. A full resolution texture image is input at the base of the pyramid and a representation of the texture image at multiple resolutions is generated by the feedforward pyramid structure of the neural network. The receptive field of each neuron at a given pyramid level is preprogrammed as a discrete Gaussian low-pass filter. Meaningful characteristics of the textured image must be extracted if a good resolving power of the classifier must be achieved. Local dominant orientation is the principal feature which is extracted from the textured image. Local edge orientation is computed with a Sobel mask at four orientation angles (multiple of (pi) /4). The resulting intrinsic image, that is, the local dominant orientation image, is fed to the texture classification neural network. The classification network is a three-layer feedforward back-propagation neural network.

Machine learning framework for analysis of transport through complex networks in porous, granular media: A focus on permeability

NASA Astrophysics Data System (ADS)

van der Linden, Joost H.; Narsilio, Guillermo A.; Tordesillas, Antoinette

2016-08-01

We present a data-driven framework to study the relationship between fluid flow at the macroscale and the internal pore structure, across the micro- and mesoscales, in porous, granular media. Sphere packings with varying particle size distribution and confining pressure are generated using the discrete element method. For each sample, a finite element analysis of the fluid flow is performed to compute the permeability. We construct a pore network and a particle contact network to quantify the connectivity of the pores and particles across the mesoscopic spatial scales. Machine learning techniques for feature selection are employed to identify sets of microstructural properties and multiscale complex network features that optimally characterize permeability. We find a linear correlation (in log-log scale) between permeability and the average closeness centrality of the weighted pore network. With the pore network links weighted by the local conductance, the average closeness centrality represents a multiscale measure of efficiency of flow through the pore network in terms of the mean geodesic distance (or shortest path) between all pore bodies in the pore network. Specifically, this study objectively quantifies a hypothesized link between high permeability and efficient shortest paths that thread through relatively large pore bodies connected to each other by high conductance pore throats, embodying connectivity and pore structure.

On detection and visualization techniques for cyber security situation awareness

NASA Astrophysics Data System (ADS)

Yu, Wei; Wei, Shixiao; Shen, Dan; Blowers, Misty; Blasch, Erik P.; Pham, Khanh D.; Chen, Genshe; Zhang, Hanlin; Lu, Chao

2013-05-01

Networking technologies are exponentially increasing to meet worldwide communication requirements. The rapid growth of network technologies and perversity of communications pose serious security issues. In this paper, we aim to developing an integrated network defense system with situation awareness capabilities to present the useful information for human analysts. In particular, we implement a prototypical system that includes both the distributed passive and active network sensors and traffic visualization features, such as 1D, 2D and 3D based network traffic displays. To effectively detect attacks, we also implement algorithms to transform real-world data of IP addresses into images and study the pattern of attacks and use both the discrete wavelet transform (DWT) based scheme and the statistical based scheme to detect attacks. Through an extensive simulation study, our data validate the effectiveness of our implemented defense system.

Non-invasive classification of gas-liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network

NASA Astrophysics Data System (ADS)

Musa Abbagoni, Baba; Yeung, Hoi

2016-08-01

The identification of flow pattern is a key issue in multiphase flow which is encountered in the petrochemical industry. It is difficult to identify the gas-liquid flow regimes objectively with the gas-liquid two-phase flow. This paper presents the feasibility of a clamp-on instrument for an objective flow regime classification of two-phase flow using an ultrasonic Doppler sensor and an artificial neural network, which records and processes the ultrasonic signals reflected from the two-phase flow. Experimental data is obtained on a horizontal test rig with a total pipe length of 21 m and 5.08 cm internal diameter carrying air-water two-phase flow under slug, elongated bubble, stratified-wavy and, stratified flow regimes. Multilayer perceptron neural networks (MLPNNs) are used to develop the classification model. The classifier requires features as an input which is representative of the signals. Ultrasound signal features are extracted by applying both power spectral density (PSD) and discrete wavelet transform (DWT) methods to the flow signals. A classification scheme of ‘1-of-C coding method for classification’ was adopted to classify features extracted into one of four flow regime categories. To improve the performance of the flow regime classifier network, a second level neural network was incorporated by using the output of a first level networks feature as an input feature. The addition of the two network models provided a combined neural network model which has achieved a higher accuracy than single neural network models. Classification accuracies are evaluated in the form of both the PSD and DWT features. The success rates of the two models are: (1) using PSD features, the classifier missed 3 datasets out of 24 test datasets of the classification and scored 87.5% accuracy; (2) with the DWT features, the network misclassified only one data point and it was able to classify the flow patterns up to 95.8% accuracy. This approach has demonstrated the success of a clamp-on ultrasound sensor for flow regime classification that would be possible in industry practice. It is considerably more promising than other techniques as it uses a non-invasive and non-radioactive sensor.

On Extended Dissipativity of Discrete-Time Neural Networks With Time Delay.

PubMed

Feng, Zhiguang; Zheng, Wei Xing

2015-12-01

In this brief, the problem of extended dissipativity analysis for discrete-time neural networks with time-varying delay is investigated. The definition of extended dissipativity of discrete-time neural networks is proposed, which unifies several performance measures, such as the H∞ performance, passivity, l2 - l∞ performance, and dissipativity. By introducing a triple-summable term in Lyapunov function, the reciprocally convex approach is utilized to bound the forward difference of the triple-summable term and then the extended dissipativity criterion for discrete-time neural networks with time-varying delay is established. The derived condition guarantees not only the extended dissipativity but also the stability of the neural networks. Two numerical examples are given to demonstrate the reduced conservatism and effectiveness of the obtained results.

Detection of broken rotor bar faults in induction motor at low load using neural network.

PubMed

Bessam, B; Menacer, A; Boumehraz, M; Cherif, H

2016-09-01

The knowledge of the broken rotor bars characteristic frequencies and amplitudes has a great importance for all related diagnostic methods. The monitoring of motor faults requires a high resolution spectrum to separate different frequency components. The Discrete Fourier Transform (DFT) has been widely used to achieve these requirements. However, at low slip this technique cannot give good results. As a solution for these problems, this paper proposes an efficient technique based on a neural network approach and Hilbert transform (HT) for broken rotor bar diagnosis in induction machines at low load. The Hilbert transform is used to extract the stator current envelope (SCE). Two features are selected from the (SCE) spectrum (the amplitude and frequency of the harmonic). These features will be used as input for neural network. The results obtained are astonishing and it is capable to detect the correct number of broken rotor bars under different load conditions. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Coupled hydromechanical paleoclimate analyses of density-dependant groundwater flow in discretely fractured crystalline rock settings

NASA Astrophysics Data System (ADS)

Normani, S. D.; Sykes, J. F.; Jensen, M. R.

2009-04-01

A high resolution sub-regional scale (84 km2) density-dependent, fracture zone network groundwater flow model with hydromechanical coupling and pseudo-permafrost, was developed from a larger 5734 km2 regional scale groundwater flow model of a Canadian Shield setting in fractured crystalline rock. The objective of the work is to illustrate aspects of regional and sub-regional groundwater flow that are relevant to the long-term performance of a hypothetical nuclear fuel repository. The discrete fracture dual continuum numerical model FRAC3DVS-OPG was used for all simulations. A discrete fracture zone network model delineated from surface features was superimposed onto an 789887 element flow domain mesh. Orthogonal fracture faces (between adjacent finite element grid blocks) were used to best represent the irregular discrete fracture zone network. The crystalline rock between these structural discontinuities was assigned properties characteristic of those reported for the Canadian Shield at the Underground Research Laboratory at Pinawa, Manitoba. Interconnectivity of permeable fracture features is an important pathway for the possibly relatively rapid migration of average water particles and subsequent reduction in residence times. The multiple 121000 year North American continental scale paleoclimate simulations are provided by W.R. Peltier using the University of Toronto Glacial Systems Model (UofT GSM). Values of ice sheet normal stress, and proglacial lake depth from the UofT GSM are applied to the sub-regional model as surface boundary conditions, using a freshwater head equivalent to the normal stress imposed by the ice sheet at its base. Permafrost depth is applied as a permeability reduction to both three-dimensional grid blocks and fractures that lie within the time varying permafrost zone. Two different paleoclimate simulations are applied to the sub-regional model to investigate the effect on the depth of glacial meltwater migration into the subsurface. In addition, different conceptualizations of fracture permeability with depth, and various hydromechanical loading efficiencies are used to investigate glacial meltwater penetration. The importance of density dependent flow, due to pore waters deep in the Canadian Shield with densities of up to 1200 kg/m3 and total dissolved solids concentrations in excess of 300 g/L, is also illustrated. Performance measures used in the assessment include depth of glacial meltwater penetration using a tracer, and mean life expectancy. Consistent with the findings from isotope and geochemical assessments, the analyses support the conclusion that for the discrete fracture zone and matrix properties simulated in this study, glacial meltwaters would not likely impact a deep geologic repository in a crystalline rock setting.

Network Science Research Laboratory (NSRL) Discrete Event Toolkit

DTIC Science & Technology

2016-01-01

ARL-TR-7579 ● JAN 2016 US Army Research Laboratory Network Science Research Laboratory (NSRL) Discrete Event Toolkit by...Laboratory (NSRL) Discrete Event Toolkit by Theron Trout and Andrew J Toth Computational and Information Sciences Directorate, ARL...Research Laboratory (NSRL) Discrete Event Toolkit 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Theron Trout

Acoustic⁻Seismic Mixed Feature Extraction Based on Wavelet Transform for Vehicle Classification in Wireless Sensor Networks.

PubMed

Zhang, Heng; Pan, Zhongming; Zhang, Wenna

2018-06-07

An acoustic⁻seismic mixed feature extraction method based on the wavelet coefficient energy ratio (WCER) of the target signal is proposed in this study for classifying vehicle targets in wireless sensor networks. The signal was decomposed into a set of wavelet coefficients using the à trous algorithm, which is a concise method used to implement the wavelet transform of a discrete signal sequence. After the wavelet coefficients of the target acoustic and seismic signals were obtained, the energy ratio of each layer coefficient was calculated as the feature vector of the target signals. Subsequently, the acoustic and seismic features were merged into an acoustic⁻seismic mixed feature to improve the target classification accuracy after the acoustic and seismic WCER features of the target signal were simplified using the hierarchical clustering method. We selected the support vector machine method for classification and utilized the data acquired from a real-world experiment to validate the proposed method. The calculated results show that the WCER feature extraction method can effectively extract the target features from target signals. Feature simplification can reduce the time consumption of feature extraction and classification, with no effect on the target classification accuracy. The use of acoustic⁻seismic mixed features effectively improved target classification accuracy by approximately 12% compared with either acoustic signal or seismic signal alone.

The application of neural networks to myoelectric signal analysis: a preliminary study.

PubMed

Kelly, M F; Parker, P A; Scott, R N

1990-03-01

Two neural network implementations are applied to myoelectric signal (MES) analysis tasks. The motivation behind this research is to explore more reliable methods of deriving control for multidegree of freedom arm prostheses. A discrete Hopfield network is used to calculate the time series parameters for a moving average MES model. It is demonstrated that the Hopfield network is capable of generating the same time series parameters as those produced by the conventional sequential least squares (SLS) algorithm. Furthermore, it can be extended to applications utilizing larger amounts of data, and possibly to higher order time series models, without significant degradation in computational efficiency. The second neural network implementation involves using a two-layer perceptron for classifying a single site MES based on two features, specifically the first time series parameter, and the signal power. Using these features, the perceptron is trained to distinguish between four separate arm functions. The two-dimensional decision boundaries used by the perceptron classifier are delineated. It is also demonstrated that the perceptron is able to rapidly compensate for variations when new data are incorporated into the training set. This adaptive quality suggests that perceptrons may provide a useful tool for future MES analysis.

A Semiquantitative Framework for Gene Regulatory Networks: Increasing the Time and Quantitative Resolution of Boolean Networks

PubMed Central

Kerkhofs, Johan; Geris, Liesbet

2015-01-01

Boolean models have been instrumental in predicting general features of gene networks and more recently also as explorative tools in specific biological applications. In this study we introduce a basic quantitative and a limited time resolution to a discrete (Boolean) framework. Quantitative resolution is improved through the employ of normalized variables in unison with an additive approach. Increased time resolution stems from the introduction of two distinct priority classes. Through the implementation of a previously published chondrocyte network and T helper cell network, we show that this addition of quantitative and time resolution broadens the scope of biological behaviour that can be captured by the models. Specifically, the quantitative resolution readily allows models to discern qualitative differences in dosage response to growth factors. The limited time resolution, in turn, can influence the reachability of attractors, delineating the likely long term system behaviour. Importantly, the information required for implementation of these features, such as the nature of an interaction, is typically obtainable from the literature. Nonetheless, a trade-off is always present between additional computational cost of this approach and the likelihood of extending the model’s scope. Indeed, in some cases the inclusion of these features does not yield additional insight. This framework, incorporating increased and readily available time and semi-quantitative resolution, can help in substantiating the litmus test of dynamics for gene networks, firstly by excluding unlikely dynamics and secondly by refining falsifiable predictions on qualitative behaviour. PMID:26067297

Analyzing neuronal networks using discrete-time dynamics

NASA Astrophysics Data System (ADS)

Ahn, Sungwoo; Smith, Brian H.; Borisyuk, Alla; Terman, David

2010-05-01

We develop mathematical techniques for analyzing detailed Hodgkin-Huxley like models for excitatory-inhibitory neuronal networks. Our strategy for studying a given network is to first reduce it to a discrete-time dynamical system. The discrete model is considerably easier to analyze, both mathematically and computationally, and parameters in the discrete model correspond directly to parameters in the original system of differential equations. While these networks arise in many important applications, a primary focus of this paper is to better understand mechanisms that underlie temporally dynamic responses in early processing of olfactory sensory information. The models presented here exhibit several properties that have been described for olfactory codes in an insect’s Antennal Lobe. These include transient patterns of synchronization and decorrelation of sensory inputs. By reducing the model to a discrete system, we are able to systematically study how properties of the dynamics, including the complex structure of the transients and attractors, depend on factors related to connectivity and the intrinsic and synaptic properties of cells within the network.

Effect of memory in non-Markovian Boolean networks illustrated with a case study: A cell cycling process

NASA Astrophysics Data System (ADS)

Ebadi, H.; Saeedian, M.; Ausloos, M.; Jafari, G. R.

2016-11-01

The Boolean network is one successful model to investigate discrete complex systems such as the gene interacting phenomenon. The dynamics of a Boolean network, controlled with Boolean functions, is usually considered to be a Markovian (memory-less) process. However, both self-organizing features of biological phenomena and their intelligent nature should raise some doubt about ignoring the history of their time evolution. Here, we extend the Boolean network Markovian approach: we involve the effect of memory on the dynamics. This can be explored by modifying Boolean functions into non-Markovian functions, for example, by investigating the usual non-Markovian threshold function —one of the most applied Boolean functions. By applying the non-Markovian threshold function on the dynamical process of the yeast cell cycle network, we discover a power-law-like memory with a more robust dynamics than the Markovian dynamics.

Stability analysis and synchronization in discrete-time complex networks with delayed coupling

NASA Astrophysics Data System (ADS)

Cheng, Ranran; Peng, Mingshu; Yu, Weibin; Sun, Bo; Yu, Jinchen

2013-12-01

A new network of coupled maps is proposed in which the connections between units involve no delays but the intra-neural communication does, whereas in the work of Atay et al. [Phys. Rev. Lett. 92, 144101 (2004)], the focus is on information processing delayed by the inter-neural communication. We show that the synchronization of the network depends on not only the intrinsic dynamical features and inter-connection topology (characterized by the spectrum of the graph Laplacian) but also the delays and the coupling strength. There are two main findings: (i) the more neighbours, the easier to be synchronized; (ii) odd delays are easier to be synchronized than even ones. In addition, compared with those discussed by Atay et al. [Phys. Rev. Lett. 92, 144101 (2004)], our model has a better synchronizability for regular networks and small-world variants.

Convergence behavior of delayed discrete cellular neural network without periodic coefficients.

PubMed

Wang, Jinling; Jiang, Haijun; Hu, Cheng; Ma, Tianlong

2014-05-01

In this paper, we study convergence behaviors of delayed discrete cellular neural networks without periodic coefficients. Some sufficient conditions are derived to ensure all solutions of delayed discrete cellular neural network without periodic coefficients converge to a periodic function, by applying mathematical analysis techniques and the properties of inequalities. Finally, some examples showing the effectiveness of the provided criterion are given. Copyright © 2014 Elsevier Ltd. All rights reserved.

Polarization transformation as an algorithm for automatic generalization and quality assessment

NASA Astrophysics Data System (ADS)

Qian, Haizhong; Meng, Liqiu

2007-06-01

Since decades it has been a dream of cartographers to computationally mimic the generalization processes in human brains for the derivation of various small-scale target maps or databases from a large-scale source map or database. This paper addresses in a systematic way the polarization transformation (PT) - a new algorithm that serves both the purpose of automatic generalization of discrete features and the quality assurance. By means of PT, two dimensional point clusters or line networks in the Cartesian system can be transformed into a polar coordinate system, which then can be unfolded as a single spectrum line r = f(α), where r and a stand for the polar radius and the polar angle respectively. After the transformation, the original features will correspond to nodes on the spectrum line delimited between 0° and 360° along the horizontal axis, and between the minimum and maximum polar radius along the vertical axis. Since PT is a lossless transformation, it allows a straighforward analysis and comparison of the original and generalized distributions, thus automatic generalization and quality assurance can be down in this way. Examples illustrate that PT algorithm meets with the requirement of generalization of discrete spatial features and is more scientific.

Constructing networks with correlation maximization methods.

PubMed

Mellor, Joseph C; Wu, Jie; Delisi, Charles

2004-01-01

Problems of inference in systems biology are ideally reduced to formulations which can efficiently represent the features of interest. In the case of predicting gene regulation and pathway networks, an important feature which describes connected genes and proteins is the relationship between active and inactive forms, i.e. between the "on" and "off" states of the components. While not optimal at the limits of resolution, these logical relationships between discrete states can often yield good approximations of the behavior in larger complex systems, where exact representation of measurement relationships may be intractable. We explore techniques for extracting binary state variables from measurement of gene expression, and go on to describe robust measures for statistical significance and information that can be applied to many such types of data. We show how statistical strength and information are equivalent criteria in limiting cases, and demonstrate the application of these measures to simple systems of gene regulation.

Molecular Regulation of Lumen Morphogenesis Review

PubMed Central

Datta, Anirban; Bryant, David M.; Mostov, Keith E.

2013-01-01

The asymmetric polarization of cells allows specialized functions to be performed at discrete subcellular locales. Spatiotemporal coordination of polarization between groups of cells allowed the evolution of metazoa. For instance, coordinated apical-basal polarization of epithelial and endothelial cells allows transport of nutrients and metabolites across cell barriers and tissue microenvironments. The defining feature of such tissues is the presence of a central, interconnected luminal network. Although tubular networks are present in seemingly different organ systems, such as the kidney, lung, and blood vessels, common underlying principles govern their formation. Recent studies using in vivo and in vitro models of lumen formation have shed new light on the molecular networks regulating this fundamental process. We here discuss progress in understanding common design principles underpinning de novo lumen formation and expansion. PMID:21300279

Event-driven contrastive divergence for spiking neuromorphic systems.

PubMed

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2013-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.

Event-driven contrastive divergence for spiking neuromorphic systems

PubMed Central

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2014-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality. PMID:24574952

A new cross-correlation algorithm for the analysis of "in vitro" neuronal network activity aimed at pharmacological studies.

PubMed

Biffi, E; Menegon, A; Regalia, G; Maida, S; Ferrigno, G; Pedrocchi, A

2011-08-15

Modern drug discovery for Central Nervous System pathologies has recently focused its attention to in vitro neuronal networks as models for the study of neuronal activities. Micro Electrode Arrays (MEAs), a widely recognized tool for pharmacological investigations, enable the simultaneous study of the spiking activity of discrete regions of a neuronal culture, providing an insight into the dynamics of networks. Taking advantage of MEAs features and making the most of the cross-correlation analysis to assess internal parameters of a neuronal system, we provide an efficient method for the evaluation of comprehensive neuronal network activity. We developed an intra network burst correlation algorithm, we evaluated its sensitivity and we explored its potential use in pharmacological studies. Our results demonstrate the high sensitivity of this algorithm and the efficacy of this methodology in pharmacological dose-response studies, with the advantage of analyzing the effect of drugs on the comprehensive correlative properties of integrated neuronal networks. Copyright © 2011 Elsevier B.V. All rights reserved.

Bayesian Network Webserver: a comprehensive tool for biological network modeling.

PubMed

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.

Architecture for Cognitive Networking within NASAs Future Space Communications Infrastructure

NASA Technical Reports Server (NTRS)

Clark, Gilbert J., III; Eddy, Wesley M.; Johnson, Sandra K.; Barnes, James; Brooks, David

2016-01-01

Future space mission concepts and designs pose many networking challenges for command, telemetry, and science data applications with diverse end-to-end data delivery needs. For future end-to-end architecture designs, a key challenge is meeting expected application quality of service requirements for multiple simultaneous mission data flows with options to use diverse onboard local data buses, commercial ground networks, and multiple satellite relay constellations in LEO, MEO, GEO, or even deep space relay links. Effectively utilizing a complex network topology requires orchestration and direction that spans the many discrete, individually addressable computer systems, which cause them to act in concert to achieve the overall network goals. The system must be intelligent enough to not only function under nominal conditions, but also adapt to unexpected situations, and reorganize or adapt to perform roles not originally intended for the system or explicitly programmed. This paper describes architecture features of cognitive networking within the future NASA space communications infrastructure, and interacting with the legacy systems and infrastructure in the meantime. The paper begins by discussing the need for increased automation, including inter-system collaboration. This discussion motivates the features of an architecture including cognitive networking for future missions and relays, interoperating with both existing endpoint-based networking models and emerging information-centric models. From this basis, we discuss progress on a proof-of-concept implementation of this architecture as a cognitive networking on-orbit application on the SCaN Testbed attached to the International Space Station.

General method to find the attractors of discrete dynamic models of biological systems.

PubMed

Gan, Xiao; Albert, Réka

2018-04-01

Analyzing the long-term behaviors (attractors) of dynamic models of biological networks can provide valuable insight. We propose a general method that can find the attractors of multilevel discrete dynamical systems by extending a method that finds the attractors of a Boolean network model. The previous method is based on finding stable motifs, subgraphs whose nodes' states can stabilize on their own. We extend the framework from binary states to any finite discrete levels by creating a virtual node for each level of a multilevel node, and describing each virtual node with a quasi-Boolean function. We then create an expanded representation of the multilevel network, find multilevel stable motifs and oscillating motifs, and identify attractors by successive network reduction. In this way, we find both fixed point attractors and complex attractors. We implemented an algorithm, which we test and validate on representative synthetic networks and on published multilevel models of biological networks. Despite its primary motivation to analyze biological networks, our motif-based method is general and can be applied to any finite discrete dynamical system.

General method to find the attractors of discrete dynamic models of biological systems

NASA Astrophysics Data System (ADS)

Gan, Xiao; Albert, Réka

2018-04-01

Analyzing the long-term behaviors (attractors) of dynamic models of biological networks can provide valuable insight. We propose a general method that can find the attractors of multilevel discrete dynamical systems by extending a method that finds the attractors of a Boolean network model. The previous method is based on finding stable motifs, subgraphs whose nodes' states can stabilize on their own. We extend the framework from binary states to any finite discrete levels by creating a virtual node for each level of a multilevel node, and describing each virtual node with a quasi-Boolean function. We then create an expanded representation of the multilevel network, find multilevel stable motifs and oscillating motifs, and identify attractors by successive network reduction. In this way, we find both fixed point attractors and complex attractors. We implemented an algorithm, which we test and validate on representative synthetic networks and on published multilevel models of biological networks. Despite its primary motivation to analyze biological networks, our motif-based method is general and can be applied to any finite discrete dynamical system.

Global exponential stability of BAM neural networks with time-varying delays: The discrete-time case

NASA Astrophysics Data System (ADS)

Raja, R.; Marshal Anthoni, S.

2011-02-01

This paper deals with the problem of stability analysis for a class of discrete-time bidirectional associative memory (BAM) neural networks with time-varying delays. By employing the Lyapunov functional and linear matrix inequality (LMI) approach, a new sufficient conditions is proposed for the global exponential stability of discrete-time BAM neural networks. The proposed LMI based results can be easily checked by LMI control toolbox. Moreover, an example is also provided to demonstrate the effectiveness of the proposed method.

Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform.

PubMed

Ashraf, Rehan; Ahmed, Mudassar; Jabbar, Sohail; Khalid, Shehzad; Ahmad, Awais; Din, Sadia; Jeon, Gwangil

2018-01-25

Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is a open research problem. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visually appearance. The color, shape and texture are the examples of low-level image features. The feature plays a significant role in image processing. The powerful representation of an image is known as feature vector and feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. In this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique will be better. The effectiveness of the CBIR approach is fundamentally based on feature extraction. In image processing errands like object recognition and image retrieval feature descriptor is an immense among the most essential step. The main idea of CBIR is that it can search related images to an image passed as query from a dataset got by using distance metrics. The proposed method is explained for image retrieval constructed on YCbCr color with canny edge histogram and discrete wavelet transform. The combination of edge of histogram and discrete wavelet transform increase the performance of image retrieval framework for content based search. The execution of different wavelets is additionally contrasted with discover the suitability of specific wavelet work for image retrieval. The proposed algorithm is prepared and tried to implement for Wang image database. For Image Retrieval Purpose, Artificial Neural Networks (ANN) is used and applied on standard dataset in CBIR domain. The execution of the recommended descriptors is assessed by computing both Precision and Recall values and compared with different other proposed methods with demonstrate the predominance of our method. The efficiency and effectiveness of the proposed approach outperforms the existing research in term of average precision and recall values.

Using Discrete Event Simulation to Model Attacker Interactions with Cyber and Physical Security Systems

DOE PAGES

Perkins, Casey; Muller, George

2015-10-08

The number of connections between physical and cyber security systems is rapidly increasing due to centralized control from automated and remotely connected means. As the number of interfaces between systems continues to grow, the interactions and interdependencies between them cannot be ignored. Historically, physical and cyber vulnerability assessments have been performed independently. This independent evaluation omits important aspects of the integrated system, where the impacts resulting from malicious or opportunistic attacks are not easily known or understood. Here, we describe a discrete event simulation model that uses information about integrated physical and cyber security systems, attacker characteristics and simple responsemore » rules to identify key safeguards that limit an attacker's likelihood of success. Key features of the proposed model include comprehensive data generation to support a variety of sophisticated analyses, and full parameterization of safeguard performance characteristics and attacker behaviours to evaluate a range of scenarios. Lastly, we also describe the core data requirements and the network of networks that serves as the underlying simulation structure.« less

Learning may need only a few bits of synaptic precision

NASA Astrophysics Data System (ADS)

Baldassi, Carlo; Gerace, Federica; Lucibello, Carlo; Saglietti, Luca; Zecchina, Riccardo

2016-05-01

Learning in neural networks poses peculiar challenges when using discretized rather then continuous synaptic states. The choice of discrete synapses is motivated by biological reasoning and experiments, and possibly by hardware implementation considerations as well. In this paper we extend a previous large deviations analysis which unveiled the existence of peculiar dense regions in the space of synaptic states which accounts for the possibility of learning efficiently in networks with binary synapses. We extend the analysis to synapses with multiple states and generally more plausible biological features. The results clearly indicate that the overall qualitative picture is unchanged with respect to the binary case, and very robust to variation of the details of the model. We also provide quantitative results which suggest that the advantages of increasing the synaptic precision (i.e., the number of internal synaptic states) rapidly vanish after the first few bits, and therefore that, for practical applications, only few bits may be needed for near-optimal performance, consistent with recent biological findings. Finally, we demonstrate how the theoretical analysis can be exploited to design efficient algorithmic search strategies.

Using Discrete Event Simulation to Model Attacker Interactions with Cyber and Physical Security Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perkins, Casey; Muller, George

The number of connections between physical and cyber security systems is rapidly increasing due to centralized control from automated and remotely connected means. As the number of interfaces between systems continues to grow, the interactions and interdependencies between them cannot be ignored. Historically, physical and cyber vulnerability assessments have been performed independently. This independent evaluation omits important aspects of the integrated system, where the impacts resulting from malicious or opportunistic attacks are not easily known or understood. Here, we describe a discrete event simulation model that uses information about integrated physical and cyber security systems, attacker characteristics and simple responsemore » rules to identify key safeguards that limit an attacker's likelihood of success. Key features of the proposed model include comprehensive data generation to support a variety of sophisticated analyses, and full parameterization of safeguard performance characteristics and attacker behaviours to evaluate a range of scenarios. Lastly, we also describe the core data requirements and the network of networks that serves as the underlying simulation structure.« less

Detection of Interictal Discharges With Convolutional Neural Networks Using Discrete Ordered Multichannel Intracranial EEG.

PubMed

Antoniades, Andreas; Spyrou, Loukianos; Martin-Lopez, David; Valentin, Antonio; Alarcon, Gonzalo; Sanei, Saeid; Cheong Took, Clive

2017-12-01

Detection algorithms for electroencephalography (EEG) data, especially in the field of interictal epileptiform discharge (IED) detection, have traditionally employed handcrafted features, which utilized specific characteristics of neural responses. Although these algorithms achieve high accuracy, mere detection of an IED holds little clinical significance. In this paper, we consider deep learning for epileptic subjects to accommodate automatic feature generation from intracranial EEG data, while also providing clinical insight. Convolutional neural networks are trained in a subject independent fashion to demonstrate how meaningful features are automatically learned in a hierarchical process. We illustrate how the convolved filters in the deepest layers provide insight toward the different types of IEDs within the group, as confirmed by our expert clinicians. The morphology of the IEDs found in filters can help evaluate the treatment of a patient. To improve the learning of the deep model, moderately different score classes are utilized as opposed to binary IED and non-IED labels. The resulting model achieves state-of-the-art classification performance and is also invariant to time differences between the IEDs. This paper suggests that deep learning is suitable for automatic feature generation from intracranial EEG data, while also providing insight into the data.

Robustness of Radiomic Features in [11C]Choline and [18F]FDG PET/CT Imaging of Nasopharyngeal Carcinoma: Impact of Segmentation and Discretization.

PubMed

Lu, Lijun; Lv, Wenbing; Jiang, Jun; Ma, Jianhua; Feng, Qianjin; Rahmim, Arman; Chen, Wufan

2016-12-01

Radiomic features are increasingly utilized to evaluate tumor heterogeneity in PET imaging and to enable enhanced prediction of therapy response and outcome. An important ingredient to success in translation of radiomic features to clinical reality is to quantify and ascertain their robustness. In the present work, we studied the impact of segmentation and discretization on 88 radiomic features in 2-deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]FDG) and [ 11 C]methyl-choline ([ 11 C]choline) positron emission tomography/X-ray computed tomography (PET/CT) imaging of nasopharyngeal carcinoma. Forty patients underwent [ 18 F]FDG PET/CT scans. Of these, nine patients were imaged on a different day utilizing [ 11 C]choline PET/CT. Tumors were delineated using reference manual segmentation by the consensus of three expert physicians, using 41, 50, and 70 % maximum standardized uptake value (SUV max ) threshold with background correction, Nestle's method, and watershed and region growing methods, and then discretized with fixed bin size (0.05, 0.1, 0.2, 0.5, and 1) in units of SUV. A total of 88 features, including 21 first-order intensity features, 10 shape features, and 57 second- and higher-order textural features, were extracted from the tumors. The robustness of the features was evaluated via the intraclass correlation coefficient (ICC) for seven kinds of segmentation methods (involving all 88 features) and five kinds of discretization bin size (involving the 57 second- and higher-order features). Forty-four (50 %) and 55 (63 %) features depicted ICC ≥0.8 with respect to segmentation as obtained from [ 18 F]FDG and [ 11 C]choline, respectively. Thirteen (23 %) and 12 (21 %) features showed ICC ≥0.8 with respect to discretization as obtained from [ 18 F]FDG and [ 11 C]choline, respectively. Six features were obtained from both [ 18 F]FDG and [ 11 C]choline having ICC ≥0.8 for both segmentation and discretization, five of which were gray-level co-occurrence matrix (GLCM) features (SumEntropy, Entropy, DifEntropy, Homogeneity1, and Homogeneity2) and one of which was an neighborhood gray-tone different matrix (NGTDM) feature (Coarseness). Discretization generated larger effects on features than segmentation in both tracers. Features extracted from [ 11 C]choline were more robust than [ 18 F]FDG for segmentation. Discretization had very similar effects on features extracted from both tracers.

Computer-Aided Diagnosis System for Alzheimer's Disease Using Different Discrete Transform Techniques.

PubMed

Dessouky, Mohamed M; Elrashidy, Mohamed A; Taha, Taha E; Abdelkader, Hatem M

2016-05-01

The different discrete transform techniques such as discrete cosine transform (DCT), discrete sine transform (DST), discrete wavelet transform (DWT), and mel-scale frequency cepstral coefficients (MFCCs) are powerful feature extraction techniques. This article presents a proposed computer-aided diagnosis (CAD) system for extracting the most effective and significant features of Alzheimer's disease (AD) using these different discrete transform techniques and MFCC techniques. Linear support vector machine has been used as a classifier in this article. Experimental results conclude that the proposed CAD system using MFCC technique for AD recognition has a great improvement for the system performance with small number of significant extracted features, as compared with the CAD system based on DCT, DST, DWT, and the hybrid combination methods of the different transform techniques. © The Author(s) 2015.

The Flow Dimension and Aquifer Heterogeneity: Field evidence and Numerical Analyses

NASA Astrophysics Data System (ADS)

Walker, D. D.; Cello, P. A.; Valocchi, A. J.; Roberts, R. M.; Loftis, B.

2008-12-01

The Generalized Radial Flow approach to hydraulic test interpretation infers the flow dimension to describe the geometry of the flow field during a hydraulic test. Noninteger values of the flow dimension often are inferred for tests in highly heterogeneous aquifers, yet subsequent modeling studies typically ignore the flow dimension. Monte Carlo analyses of detailed numerical models of aquifer tests examine the flow dimension for several stochastic models of heterogeneous transmissivity, T(x). These include multivariate lognormal, fractional Brownian motion, a site percolation network, and discrete linear features with lengths distributed as power-law. The behavior of the simulated flow dimensions are compared to the flow dimensions observed for multiple aquifer tests in a fractured dolomite aquifer in the Great Lakes region of North America. The combination of multiple hydraulic tests, observed fracture patterns, and the Monte Carlo results are used to screen models of heterogeneity and their parameters for subsequent groundwater flow modeling. The comparison shows that discrete linear features with lengths distributed as a power-law appear to be the most consistent with observations of the flow dimension in fractured dolomite aquifers.

Exploring high dimensional data with Butterfly: a novel classification algorithm based on discrete dynamical systems.

PubMed

Geraci, Joseph; Dharsee, Moyez; Nuin, Paulo; Haslehurst, Alexandria; Koti, Madhuri; Feilotter, Harriet E; Evans, Ken

2014-03-01

We introduce a novel method for visualizing high dimensional data via a discrete dynamical system. This method provides a 2D representation of the relationship between subjects according to a set of variables without geometric projections, transformed axes or principal components. The algorithm exploits a memory-type mechanism inherent in a certain class of discrete dynamical systems collectively referred to as the chaos game that are closely related to iterative function systems. The goal of the algorithm was to create a human readable representation of high dimensional patient data that was capable of detecting unrevealed subclusters of patients from within anticipated classifications. This provides a mechanism to further pursue a more personalized exploration of pathology when used with medical data. For clustering and classification protocols, the dynamical system portion of the algorithm is designed to come after some feature selection filter and before some model evaluation (e.g. clustering accuracy) protocol. In the version given here, a univariate features selection step is performed (in practice more complex feature selection methods are used), a discrete dynamical system is driven by this reduced set of variables (which results in a set of 2D cluster models), these models are evaluated for their accuracy (according to a user-defined binary classification) and finally a visual representation of the top classification models are returned. Thus, in addition to the visualization component, this methodology can be used for both supervised and unsupervised machine learning as the top performing models are returned in the protocol we describe here. Butterfly, the algorithm we introduce and provide working code for, uses a discrete dynamical system to classify high dimensional data and provide a 2D representation of the relationship between subjects. We report results on three datasets (two in the article; one in the appendix) including a public lung cancer dataset that comes along with the included Butterfly R package. In the included R script, a univariate feature selection method is used for the dimension reduction step, but in the future we wish to use a more powerful multivariate feature reduction method based on neural networks (Kriesel, 2007). A script written in R (designed to run on R studio) accompanies this article that implements this algorithm and is available at http://butterflygeraci.codeplex.com/. For details on the R package or for help installing the software refer to the accompanying document, Supporting Material and Appendix.

Discrete dynamic modeling of cellular signaling networks.

PubMed

Albert, Réka; Wang, Rui-Sheng

2009-01-01

Understanding signal transduction in cellular systems is a central issue in systems biology. Numerous experiments from different laboratories generate an abundance of individual components and causal interactions mediating environmental and developmental signals. However, for many signal transduction systems there is insufficient information on the overall structure and the molecular mechanisms involved in the signaling network. Moreover, lack of kinetic and temporal information makes it difficult to construct quantitative models of signal transduction pathways. Discrete dynamic modeling, combined with network analysis, provides an effective way to integrate fragmentary knowledge of regulatory interactions into a predictive mathematical model which is able to describe the time evolution of the system without the requirement for kinetic parameters. This chapter introduces the fundamental concepts of discrete dynamic modeling, particularly focusing on Boolean dynamic models. We describe this method step-by-step in the context of cellular signaling networks. Several variants of Boolean dynamic models including threshold Boolean networks and piecewise linear systems are also covered, followed by two examples of successful application of discrete dynamic modeling in cell biology.

Discrete-time bidirectional associative memory neural networks with variable delays

NASA Astrophysics Data System (ADS)

Liang, variable delays [rapid communication] J.; Cao, J.; Ho, D. W. C.

2005-02-01

Based on the linear matrix inequality (LMI), some sufficient conditions are presented in this Letter for the existence, uniqueness and global exponential stability of the equilibrium point of discrete-time bidirectional associative memory (BAM) neural networks with variable delays. Some of the stability criteria obtained in this Letter are delay-dependent, and some of them are delay-independent, they are less conservative than the ones reported so far in the literature. Furthermore, the results provide one more set of easily verified criteria for determining the exponential stability of discrete-time BAM neural networks.

Fluid and flexible minds: Intelligence reflects synchrony in the brain’s intrinsic network architecture

PubMed Central

Ferguson, Michael A.; Anderson, Jeffrey S.; Spreng, R. Nathan

2017-01-01

Human intelligence has been conceptualized as a complex system of dissociable cognitive processes, yet studies investigating the neural basis of intelligence have typically emphasized the contributions of discrete brain regions or, more recently, of specific networks of functionally connected regions. Here we take a broader, systems perspective in order to investigate whether intelligence is an emergent property of synchrony within the brain’s intrinsic network architecture. Using a large sample of resting-state fMRI and cognitive data (n = 830), we report that the synchrony of functional interactions within and across distributed brain networks reliably predicts fluid and flexible intellectual functioning. By adopting a whole-brain, systems-level approach, we were able to reliably predict individual differences in human intelligence by characterizing features of the brain’s intrinsic network architecture. These findings hold promise for the eventual development of neural markers to predict changes in intellectual function that are associated with neurodevelopment, normal aging, and brain disease.

Tensor networks from kinematic space

DOE PAGES

Czech, Bartlomiej; Lamprou, Lampros; McCandlish, Samuel; ...

2016-07-20

We point out that the MERA network for the ground state of a 1+1-dimensional conformal field theory has the same structural features as kinematic space — the geometry of CFT intervals. In holographic theories kinematic space becomes identified with the space of bulk geodesics studied in integral geometry. We argue that in these settings MERA is best viewed as a discretization of the space of bulk geodesics rather than of the bulk geometry itself. As a test of this kinematic proposal, we compare the MERA representation of the thermofield-double state with the space of geodesics in the two-sided BTZ geometry,more » obtaining a detailed agreement which includes the entwinement sector. In conclusion, we discuss how the kinematic proposal can be extended to excited states by generalizing MERA to a broader class of compression networks.« less

Immediate effects of EVA midsole resilience and upper shoe structure on running biomechanics: a machine learning approach

PubMed Central

Gavião Neto, Wilson P.; Roveri, Maria Isabel; Oliveira, Wagner R.

2017-01-01

Background Resilience of midsole material and the upper structure of the shoe are conceptual characteristics that can interfere in running biomechanics patterns. Artificial intelligence techniques can capture features from the entire waveform, adding new perspective for biomechanical analysis. This study tested the influence of shoe midsole resilience and upper structure on running kinematics and kinetics of non-professional runners by using feature selection, information gain, and artificial neural network analysis. Methods Twenty-seven experienced male runners (63 ± 44 km/week run) ran in four-shoe design that combined two resilience-cushioning materials (low and high) and two uppers (minimalist and structured). Kinematic data was acquired by six infrared cameras at 300 Hz, and ground reaction forces were acquired by two force plates at 1,200 Hz. We conducted a Machine Learning analysis to identify features from the complete kinematic and kinetic time series and from 42 discrete variables that had better discriminate the four shoes studied. For that analysis, we built an input data matrix of dimensions 1,080 (10 trials × 4 shoes × 27 subjects) × 1,254 (3 joints × 3 planes of movement × 101 data points + 3 vectors forces × 101 data points + 42 discrete calculated kinetic and kinematic features). Results The applied feature selection by information gain and artificial neural networks successfully differentiated the two resilience materials using 200(16%) biomechanical variables with an accuracy of 84.8% by detecting alterations of running biomechanics, and the two upper structures with an accuracy of 93.9%. Discussion The discrimination of midsole resilience resulted in lower accuracy levels than did the discrimination of the shoe uppers. In both cases, the ground reaction forces were among the 25 most relevant features. The resilience of the cushioning material caused significant effects on initial heel impact, while the effects of different uppers were distributed along the stance phase of running. Biomechanical changes due to shoe midsole resilience seemed to be subject-dependent, while those due to upper structure seemed to be subject-independent. PMID:28265506

Immediate effects of EVA midsole resilience and upper shoe structure on running biomechanics: a machine learning approach.

PubMed

Onodera, Andrea N; Gavião Neto, Wilson P; Roveri, Maria Isabel; Oliveira, Wagner R; Sacco, Isabel Cn

2017-01-01

Resilience of midsole material and the upper structure of the shoe are conceptual characteristics that can interfere in running biomechanics patterns. Artificial intelligence techniques can capture features from the entire waveform, adding new perspective for biomechanical analysis. This study tested the influence of shoe midsole resilience and upper structure on running kinematics and kinetics of non-professional runners by using feature selection, information gain, and artificial neural network analysis. Twenty-seven experienced male runners (63 ± 44 km/week run) ran in four-shoe design that combined two resilience-cushioning materials (low and high) and two uppers (minimalist and structured). Kinematic data was acquired by six infrared cameras at 300 Hz, and ground reaction forces were acquired by two force plates at 1,200 Hz. We conducted a Machine Learning analysis to identify features from the complete kinematic and kinetic time series and from 42 discrete variables that had better discriminate the four shoes studied. For that analysis, we built an input data matrix of dimensions 1,080 (10 trials × 4 shoes × 27 subjects) × 1,254 (3 joints × 3 planes of movement × 101 data points + 3 vectors forces × 101 data points + 42 discrete calculated kinetic and kinematic features). The applied feature selection by information gain and artificial neural networks successfully differentiated the two resilience materials using 200(16%) biomechanical variables with an accuracy of 84.8% by detecting alterations of running biomechanics, and the two upper structures with an accuracy of 93.9%. The discrimination of midsole resilience resulted in lower accuracy levels than did the discrimination of the shoe uppers. In both cases, the ground reaction forces were among the 25 most relevant features. The resilience of the cushioning material caused significant effects on initial heel impact, while the effects of different uppers were distributed along the stance phase of running. Biomechanical changes due to shoe midsole resilience seemed to be subject-dependent, while those due to upper structure seemed to be subject-independent.

Wavelet feature extraction for reliable discrimination between high explosive and chemical/biological artillery

NASA Astrophysics Data System (ADS)

Hohil, Myron E.; Desai, Sachi V.; Bass, Henry E.; Chambers, Jim

2005-03-01

Feature extraction methods based on the discrete wavelet transform and multiresolution analysis are used to develop a robust classification algorithm that reliably discriminates between conventional and simulated chemical/biological artillery rounds via acoustic signals produced during detonation. Distinct characteristics arise within the different airburst signatures because high explosive warheads emphasize concussive and shrapnel effects, while chemical/biological warheads are designed to disperse their contents over large areas, therefore employing a slower burning, less intense explosive to mix and spread their contents. The ensuing blast waves are readily characterized by variations in the corresponding peak pressure and rise time of the blast, differences in the ratio of positive pressure amplitude to the negative amplitude, and variations in the overall duration of the resulting waveform. Unique attributes can also be identified that depend upon the properties of the gun tube, projectile speed at the muzzle, and the explosive burn rates of the warhead. In this work, the discrete wavelet transform is used to extract the predominant components of these characteristics from air burst signatures at ranges exceeding 2km. Highly reliable discrimination is achieved with a feedforward neural network classifier trained on a feature space derived from the distribution of wavelet coefficients and higher frequency details found within different levels of the multiresolution decomposition.

Design and fabrication of a stringer stiffened discrete-tube actively cooled panel for a hypersonic aircraft

NASA Technical Reports Server (NTRS)

Anthony, F. M.; Halenbrook, R. G.

1981-01-01

A 0.61 x 1.22 m (2 x 4 ft) test panel was fabricated and delivered to the Langley Research Center for assessment of the thermal and structural features of the optimized panel design. The panel concept incorporated an aluminum alloy surface panel actively cooled by a network of discrete, parallel, redundant, counterflow passage interconnected with appropriate manifolding, and assembled by adhesive bonding. The cooled skin was stiffened with a mechanically fastened conventional substructure of stringers and frames. A 40 water/60 glycol solution was the coolant. Low pressure leak testing, radiography, holography and infrared scanning were applied at various stages of fabrication to assess integrity and uniformity. By nondestructively inspecting selected specimens which were subsequently tested to destruction, it was possible to refine inspection standards as applied to this cooled panel design.

Discrete Dynamics Model for the Speract-Activated Ca2+ Signaling Network Relevant to Sperm Motility

PubMed Central

Espinal, Jesús; Aldana, Maximino; Guerrero, Adán; Wood, Christopher

2011-01-01

Understanding how spermatozoa approach the egg is a central biological issue. Recently a considerable amount of experimental evidence has accumulated on the relation between oscillations in intracellular calcium ion concentration ([Ca]) in the sea urchin sperm flagellum, triggered by peptides secreted from the egg, and sperm motility. Determination of the structure and dynamics of the signaling pathway leading to these oscillations is a fundamental problem. However, a biochemically based formulation for the comprehension of the molecular mechanisms operating in the axoneme as a response to external stimulus is still lacking. Based on experiments on the S. purpuratus sea urchin spermatozoa, we propose a signaling network model where nodes are discrete variables corresponding to the pathway elements and the signal transmission takes place at discrete time intervals according to logical rules. The validity of this model is corroborated by reproducing previous empirically determined signaling features. Prompted by the model predictions we performed experiments which identified novel characteristics of the signaling pathway. We uncovered the role of a high voltage-activated channel as a regulator of the delay in the onset of fluctuations after activation of the signaling cascade. This delay time has recently been shown to be an important regulatory factor for sea urchin sperm reorientation. Another finding is the participation of a voltage-dependent calcium-activated channel in the determination of the period of the fluctuations. Furthermore, by analyzing the spread of network perturbations we find that it operates in a dynamically critical regime. Our work demonstrates that a coarse-grained approach to the dynamics of the signaling pathway is capable of revealing regulatory sperm navigation elements and provides insight, in terms of criticality, on the concurrence of the high robustness and adaptability that the reproduction processes are predicted to have developed throughout evolution. PMID:21857937

Hybrid discrete-time neural networks.

PubMed

Cao, Hongjun; Ibarz, Borja

2010-11-13

Hybrid dynamical systems combine evolution equations with state transitions. When the evolution equations are discrete-time (also called map-based), the result is a hybrid discrete-time system. A class of biological neural network models that has recently received some attention falls within this category: map-based neuron models connected by means of fast threshold modulation (FTM). FTM is a connection scheme that aims to mimic the switching dynamics of a neuron subject to synaptic inputs. The dynamic equations of the neuron adopt different forms according to the state (either firing or not firing) and type (excitatory or inhibitory) of their presynaptic neighbours. Therefore, the mathematical model of one such network is a combination of discrete-time evolution equations with transitions between states, constituting a hybrid discrete-time (map-based) neural network. In this paper, we review previous work within the context of these models, exemplifying useful techniques to analyse them. Typical map-based neuron models are low-dimensional and amenable to phase-plane analysis. In bursting models, fast-slow decomposition can be used to reduce dimensionality further, so that the dynamics of a pair of connected neurons can be easily understood. We also discuss a model that includes electrical synapses in addition to chemical synapses with FTM. Furthermore, we describe how master stability functions can predict the stability of synchronized states in these networks. The main results are extended to larger map-based neural networks.

GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework.

PubMed

Deng, Lei; Jiao, Peng; Pei, Jing; Wu, Zhenzhi; Li, Guoqi

2018-04-01

Although deep neural networks (DNNs) are being a revolutionary power to open up the AI era, the notoriously huge hardware overhead has challenged their applications. Recently, several binary and ternary networks, in which the costly multiply-accumulate operations can be replaced by accumulations or even binary logic operations, make the on-chip training of DNNs quite promising. Therefore there is a pressing need to build an architecture that could subsume these networks under a unified framework that achieves both higher performance and less overhead. To this end, two fundamental issues are yet to be addressed. The first one is how to implement the back propagation when neuronal activations are discrete. The second one is how to remove the full-precision hidden weights in the training phase to break the bottlenecks of memory/computation consumption. To address the first issue, we present a multi-step neuronal activation discretization method and a derivative approximation technique that enable the implementing the back propagation algorithm on discrete DNNs. While for the second issue, we propose a discrete state transition (DST) methodology to constrain the weights in a discrete space without saving the hidden weights. Through this way, we build a unified framework that subsumes the binary or ternary networks as its special cases, and under which a heuristic algorithm is provided at the website https://github.com/AcrossV/Gated-XNOR. More particularly, we find that when both the weights and activations become ternary values, the DNNs can be reduced to sparse binary networks, termed as gated XNOR networks (GXNOR-Nets) since only the event of non-zero weight and non-zero activation enables the control gate to start the XNOR logic operations in the original binary networks. This promises the event-driven hardware design for efficient mobile intelligence. We achieve advanced performance compared with state-of-the-art algorithms. Furthermore, the computational sparsity and the number of states in the discrete space can be flexibly modified to make it suitable for various hardware platforms. Copyright © 2018 Elsevier Ltd. All rights reserved.

Mammogram classification scheme using 2D-discrete wavelet and local binary pattern for detection of breast cancer

NASA Astrophysics Data System (ADS)

Adi Putra, Januar

2018-04-01

In this paper, we propose a new mammogram classification scheme to classify the breast tissues as normal or abnormal. Feature matrix is generated using Local Binary Pattern to all the detailed coefficients from 2D-DWT of the region of interest (ROI) of a mammogram. Feature selection is done by selecting the relevant features that affect the classification. Feature selection is used to reduce the dimensionality of data and features that are not relevant, in this paper the F-test and Ttest will be performed to the results of the feature extraction dataset to reduce and select the relevant feature. The best features are used in a Neural Network classifier for classification. In this research we use MIAS and DDSM database. In addition to the suggested scheme, the competent schemes are also simulated for comparative analysis. It is observed that the proposed scheme has a better say with respect to accuracy, specificity and sensitivity. Based on experiments, the performance of the proposed scheme can produce high accuracy that is 92.71%, while the lowest accuracy obtained is 77.08%.

The Predictive Capability of Conditioned Simulation of Discrete Fracture Networks using Structural and Hydraulic Data from the ONKALO Underground Research Facility, Finland

NASA Astrophysics Data System (ADS)

Williams, T. R. N.; Baxter, S.; Hartley, L.; Appleyard, P.; Koskinen, L.; Vanhanarkaus, O.; Selroos, J. O.; Munier, R.

2017-12-01

Discrete fracture network (DFN) models provide a natural analysis framework for rock conditions where flow is predominately through a series of connected discrete features. Mechanistic models to predict the structural patterns of networks are generally intractable due to inherent uncertainties (e.g. deformation history) and as such fracture characterisation typically involves empirical descriptions of fracture statistics for location, intensity, orientation, size, aperture etc. from analyses of field data. These DFN models are used to make probabilistic predictions of likely flow or solute transport conditions for a range of applications in underground resource and construction projects. However, there are many instances when the volumes in which predictions are most valuable are close to data sources. For example, in the disposal of hazardous materials such as radioactive waste, accurate predictions of flow-rates and network connectivity around disposal areas are required for long-term safety evaluation. The problem at hand is thus: how can probabilistic predictions be conditioned on local-scale measurements? This presentation demonstrates conditioning of a DFN model based on the current structural and hydraulic characterisation of the Demonstration Area at the ONKALO underground research facility. The conditioned realisations honour (to a required level of similarity) the locations, orientations and trace lengths of fractures mapped on the surfaces of the nearby ONKALO tunnels and pilot drillholes. Other data used as constraints include measurements from hydraulic injection tests performed in pilot drillholes and inflows to the subsequently reamed experimental deposition holes. Numerical simulations using this suite of conditioned DFN models provides a series of prediction-outcome exercises detailing the reliability of the DFN model to make local-scale predictions of measured geometric and hydraulic properties of the fracture system; and provides an understanding of the reduction in uncertainty in model predictions for conditioned DFN models honouring different aspects of this data.

Effect of Heterogeneous Interest Similarity on the Spread of Information in Mobile Social Networks

NASA Astrophysics Data System (ADS)

Zhao, Narisa; Sui, Guoqin; Yang, Fan

2018-06-01

Mobile social networks (MSNs) are important platforms for spreading news. The fact that individuals usually forward information aligned with their own interests inevitably changes the dynamics of information spread. Thereby, first we present a theoretical model based on the discrete Markov chain and mean field theory to evaluate the effect of interest similarity on the information spread in MSNs. Meanwhile, individuals' interests are heterogeneous and vary with time. These two features result in interest shift behavior, and both features are considered in our model. A leveraging simulation demonstrates the accuracy of our model. Moreover, the basic reproduction number R0 is determined. Further extensive numerical analyses based on the model indicate that interest similarity has a critical impact on information spread at the early spreading stage. Specifically, the information always spreads more quickly and widely if the interest similarity between an individual and the information is higher. Finally, five actual data sets from Sina Weibo illustrate the validity of the model.

A bounding-based solution approach for the continuous arc covering problem

NASA Astrophysics Data System (ADS)

Wei, Ran; Murray, Alan T.; Batta, Rajan

2014-04-01

Road segments, telecommunication wiring, water and sewer pipelines, canals and the like are important features of the urban environment. They are often conceived of and represented as network-based arcs. As a result of the usefulness and significance of arc-based features, there is a need to site facilities along arcs to serve demand. Examples of such facilities include surveillance equipment, cellular towers, refueling centers and emergency response stations, with the intent of being economically efficient as well as providing good service along the arcs. While this amounts to a continuous location problem by nature, various discretizations are generally relied upon to solve such problems. The result is potential for representation errors that negatively impact analysis and decision making. This paper develops a solution approach for the continuous arc covering problem that theoretically eliminates representation errors. The developed approach is applied to optimally place acoustic sensors and cellular base stations along a road network. The results demonstrate the effectiveness of this approach for ameliorating any error and uncertainty in the modeling process.

Multinomial Bayesian learning for modeling classical and nonclassical receptive field properties.

PubMed

Hosoya, Haruo

2012-08-01

We study the interplay of Bayesian inference and natural image learning in a hierarchical vision system, in relation to the response properties of early visual cortex. We particularly focus on a Bayesian network with multinomial variables that can represent discrete feature spaces similar to hypercolumns combining minicolumns, enforce sparsity of activation to learn efficient representations, and explain divisive normalization. We demonstrate that maximal-likelihood learning using sampling-based Bayesian inference gives rise to classical receptive field properties similar to V1 simple cells and V2 cells, while inference performed on the trained network yields nonclassical context-dependent response properties such as cross-orientation suppression and filling in. Comparison with known physiological properties reveals some qualitative and quantitative similarities.

Conditions for extinction events in chemical reaction networks with discrete state spaces.

PubMed

Johnston, Matthew D; Anderson, David F; Craciun, Gheorghe; Brijder, Robert

2018-05-01

We study chemical reaction networks with discrete state spaces and present sufficient conditions on the structure of the network that guarantee the system exhibits an extinction event. The conditions we derive involve creating a modified chemical reaction network called a domination-expanded reaction network and then checking properties of this network. Unlike previous results, our analysis allows algorithmic implementation via systems of equalities and inequalities and suggests sequences of reactions which may lead to extinction events. We apply the results to several networks including an EnvZ-OmpR signaling pathway in Escherichia coli.

Filtering of Discrete-Time Switched Neural Networks Ensuring Exponential Dissipative and $l_{2}$ - $l_{\\infty }$ Performances.

PubMed

Choi, Hyun Duck; Ahn, Choon Ki; Karimi, Hamid Reza; Lim, Myo Taeg

2017-10-01

This paper studies delay-dependent exponential dissipative and l 2 - l ∞ filtering problems for discrete-time switched neural networks (DSNNs) including time-delayed states. By introducing a novel discrete-time inequality, which is a discrete-time version of the continuous-time Wirtinger-type inequality, we establish new sets of linear matrix inequality (LMI) criteria such that discrete-time filtering error systems are exponentially stable with guaranteed performances in the exponential dissipative and l 2 - l ∞ senses. The design of the desired exponential dissipative and l 2 - l ∞ filters for DSNNs can be achieved by solving the proposed sets of LMI conditions. Via numerical simulation results, we show the validity of the desired discrete-time filter design approach.

Compensatory neurofuzzy model for discrete data classification in biomedical

NASA Astrophysics Data System (ADS)

Ceylan, Rahime

2015-03-01

Biomedical data is separated to two main sections: signals and discrete data. So, studies in this area are about biomedical signal classification or biomedical discrete data classification. There are artificial intelligence models which are relevant to classification of ECG, EMG or EEG signals. In same way, in literature, many models exist for classification of discrete data taken as value of samples which can be results of blood analysis or biopsy in medical process. Each algorithm could not achieve high accuracy rate on classification of signal and discrete data. In this study, compensatory neurofuzzy network model is presented for classification of discrete data in biomedical pattern recognition area. The compensatory neurofuzzy network has a hybrid and binary classifier. In this system, the parameters of fuzzy systems are updated by backpropagation algorithm. The realized classifier model is conducted to two benchmark datasets (Wisconsin Breast Cancer dataset and Pima Indian Diabetes dataset). Experimental studies show that compensatory neurofuzzy network model achieved 96.11% accuracy rate in classification of breast cancer dataset and 69.08% accuracy rate was obtained in experiments made on diabetes dataset with only 10 iterations.

The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis

NASA Astrophysics Data System (ADS)

Leijenaar, Ralph T. H.; Nalbantov, Georgi; Carvalho, Sara; van Elmpt, Wouter J. C.; Troost, Esther G. C.; Boellaard, Ronald; Aerts, Hugo J. W. L.; Gillies, Robert J.; Lambin, Philippe

2015-08-01

FDG-PET-derived textural features describing intra-tumor heterogeneity are increasingly investigated as imaging biomarkers. As part of the process of quantifying heterogeneity, image intensities (SUVs) are typically resampled into a reduced number of discrete bins. We focused on the implications of the manner in which this discretization is implemented. Two methods were evaluated: (1) RD, dividing the SUV range into D equally spaced bins, where the intensity resolution (i.e. bin size) varies per image; and (2) RB, maintaining a constant intensity resolution B. Clinical feasibility was assessed on 35 lung cancer patients, imaged before and in the second week of radiotherapy. Forty-four textural features were determined for different D and B for both imaging time points. Feature values depended on the intensity resolution and out of both assessed methods, RB was shown to allow for a meaningful inter- and intra-patient comparison of feature values. Overall, patients ranked differently according to feature values-which was used as a surrogate for textural feature interpretation-between both discretization methods. Our study shows that the manner of SUV discretization has a crucial effect on the resulting textural features and the interpretation thereof, emphasizing the importance of standardized methodology in tumor texture analysis.

Dendritic Connectivity, Heterogeneity, and Scaling in Urban Stormwater Networks: Implications for Socio-Hydrology

NASA Astrophysics Data System (ADS)

Mejia, A.; Jovanovic, T.; Hale, R. L.; Gironas, J. A.

2017-12-01

Urban stormwater networks (USNs) are unique dendritic (tree-like) structures that combine both artificial (e.g., swales and pipes) and natural (e.g., streams and wetlands) components. They are central to stream ecosystem structure and function in urban watersheds. The emphasis of conventional stormwater management, however, has been on localized, temporal impacts (e.g., changes to hydrographs at discrete locations), and the performance of individual stormwater control measures. This is the case even though control measures are implemented to prevent impacts on the USN. We develop a modeling approach to retrospectively study hydrological fluxes and states in USNs and apply the model to an urban watershed in Scottsdale, Arizona, USA. Using outputs from the model, we analyze over space and time the network properties of dendritic connectivity, heterogeneity, and scaling. Results show that as the network growth over time, due to increasing urbanization, it tends to become more homogenous in terms of topological features but increasingly heterogeneous in terms of dynamic features. We further use the modeling results to address socio-hydrological implications for USNs. We find that the adoption over time of evolving management strategies (e.g., widespread implementation of vegetated swales and retention ponds versus pipes) may be locally beneficial to the USN but benefits may not propagate systematically through the network. The latter can be reinforced by sudden, perhaps unintended, changes to the overall dendritic connectivity.

Hydraulic tomography of discrete networks of conduits and fractures in a karstic aquifer by using a deterministic inversion algorithm

NASA Astrophysics Data System (ADS)

Fischer, P.; Jardani, A.; Lecoq, N.

2018-02-01

In this paper, we present a novel inverse modeling method called Discrete Network Deterministic Inversion (DNDI) for mapping the geometry and property of the discrete network of conduits and fractures in the karstified aquifers. The DNDI algorithm is based on a coupled discrete-continuum concept to simulate numerically water flows in a model and a deterministic optimization algorithm to invert a set of observed piezometric data recorded during multiple pumping tests. In this method, the model is partioned in subspaces piloted by a set of parameters (matrix transmissivity, and geometry and equivalent transmissivity of the conduits) that are considered as unknown. In this way, the deterministic optimization process can iteratively correct the geometry of the network and the values of the properties, until it converges to a global network geometry in a solution model able to reproduce the set of data. An uncertainty analysis of this result can be performed from the maps of posterior uncertainties on the network geometry or on the property values. This method has been successfully tested for three different theoretical and simplified study cases with hydraulic responses data generated from hypothetical karstic models with an increasing complexity of the network geometry, and of the matrix heterogeneity.

Knowledge network model of the energy consumption in discrete manufacturing system

NASA Astrophysics Data System (ADS)

Xu, Binzi; Wang, Yan; Ji, Zhicheng

2017-07-01

Discrete manufacturing system generates a large amount of data and information because of the development of information technology. Hence, a management mechanism is urgently required. In order to incorporate knowledge generated from manufacturing data and production experience, a knowledge network model of the energy consumption in the discrete manufacturing system was put forward based on knowledge network theory and multi-granularity modular ontology technology. This model could provide a standard representation for concepts, terms and their relationships, which could be understood by both human and computer. Besides, the formal description of energy consumption knowledge elements (ECKEs) in the knowledge network was also given. Finally, an application example was used to verify the feasibility of the proposed method.

DISCRETE EVENT SIMULATION OF OPTICAL SWITCH MATRIX PERFORMANCE IN COMPUTER NETWORKS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Imam, Neena; Poole, Stephen W

2013-01-01

In this paper, we present application of a Discrete Event Simulator (DES) for performance modeling of optical switching devices in computer networks. Network simulators are valuable tools in situations where one cannot investigate the system directly. This situation may arise if the system under study does not exist yet or the cost of studying the system directly is prohibitive. Most available network simulators are based on the paradigm of discrete-event-based simulation. As computer networks become increasingly larger and more complex, sophisticated DES tool chains have become available for both commercial and academic research. Some well-known simulators are NS2, NS3, OPNET,more » and OMNEST. For this research, we have applied OMNEST for the purpose of simulating multi-wavelength performance of optical switch matrices in computer interconnection networks. Our results suggest that the application of DES to computer interconnection networks provides valuable insight in device performance and aids in topology and system optimization.« less

Aboveground Biomass Estimation Using Reconstructed Feature of Airborne Discrete-Return LIDAR by Auto-Encoder Neural Network

NASA Astrophysics Data System (ADS)

Li, T.; Wang, Z.; Peng, J.

2018-04-01

Aboveground biomass (AGB) estimation is critical for quantifying carbon stocks and essential for evaluating carbon cycle. In recent years, airborne LiDAR shows its great ability for highly-precision AGB estimation. Most of the researches estimate AGB by the feature metrics extracted from the canopy height distribution of the point cloud which calculated based on precise digital terrain model (DTM). However, if forest canopy density is high, the probability of the LiDAR signal penetrating the canopy is lower, resulting in ground points is not enough to establish DTM. Then the distribution of forest canopy height is imprecise and some critical feature metrics which have a strong correlation with biomass such as percentiles, maximums, means and standard deviations of canopy point cloud can hardly be extracted correctly. In order to address this issue, we propose a strategy of first reconstructing LiDAR feature metrics through Auto-Encoder neural network and then using the reconstructed feature metrics to estimate AGB. To assess the prediction ability of the reconstructed feature metrics, both original and reconstructed feature metrics were regressed against field-observed AGB using the multiple stepwise regression (MS) and the partial least squares regression (PLS) respectively. The results showed that the estimation model using reconstructed feature metrics improved R2 by 5.44 %, 18.09 %, decreased RMSE value by 10.06 %, 22.13 % and reduced RMSEcv by 10.00 %, 21.70 % for AGB, respectively. Therefore, reconstructing LiDAR point feature metrics has potential for addressing AGB estimation challenge in dense canopy area.

A discrete mesoscopic particle model of the mechanics of a multi-constituent arterial wall.

PubMed

Witthoft, Alexandra; Yazdani, Alireza; Peng, Zhangli; Bellini, Chiara; Humphrey, Jay D; Karniadakis, George Em

2016-01-01

Blood vessels have unique properties that allow them to function together within a complex, self-regulating network. The contractile capacity of the wall combined with complex mechanical properties of the extracellular matrix enables vessels to adapt to changes in haemodynamic loading. Homogenized phenomenological and multi-constituent, structurally motivated continuum models have successfully captured these mechanical properties, but truly describing intricate microstructural details of the arterial wall may require a discrete framework. Such an approach would facilitate modelling interactions between or the separation of layers of the wall and would offer the advantage of seamless integration with discrete models of complex blood flow. We present a discrete particle model of a multi-constituent, nonlinearly elastic, anisotropic arterial wall, which we develop using the dissipative particle dynamics method. Mimicking basic features of the microstructure of the arterial wall, the model comprises an elastin matrix having isotropic nonlinear elastic properties plus anisotropic fibre reinforcement that represents the stiffer collagen fibres of the wall. These collagen fibres are distributed evenly and are oriented in four directions, symmetric to the vessel axis. Experimental results from biaxial mechanical tests of an artery are used for model validation, and a delamination test is simulated to demonstrate the new capabilities of the model. © 2016 The Author(s).

AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection

PubMed Central

Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying

2015-01-01

Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes’ status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors’ detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability. PMID:26193280

Optical CDMA components requirements

NASA Astrophysics Data System (ADS)

Chan, James K.

1998-08-01

Optical CDMA is a complementary multiple access technology to WDMA. Optical CDMA potentially provides a large number of virtual optical channels for IXC, LEC and CLEC or supports a large number of high-speed users in LAN. In a network, it provides asynchronous, multi-rate, multi-user communication with network scalability, re-configurability (bandwidth on demand), and network security (provided by inherent CDMA coding). However, optical CDMA technology is less mature in comparison to WDMA. The components requirements are also different from WDMA. We have demonstrated a video transport/switching system over a distance of 40 Km using discrete optical components in our laboratory. We are currently pursuing PIC implementation. In this paper, we will describe the optical CDMA concept/features, the demonstration system, and the requirements of some critical optical components such as broadband optical source, broadband optical amplifier, spectral spreading/de- spreading, and fixed/programmable mask.

Stochastic Dual Algorithm for Voltage Regulation in Distribution Networks with Discrete Loads: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall-Anese, Emiliano; Zhou, Xinyang; Liu, Zhiyuan

This paper considers distribution networks with distributed energy resources and discrete-rate loads, and designs an incentive-based algorithm that allows the network operator and the customers to pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. Four major challenges include: (1) the non-convexity from discrete decision variables, (2) the non-convexity due to a Stackelberg game structure, (3) unavailable private information from customers, and (4) different update frequency from two types of devices. In this paper, we first make convex relaxation for discrete variables, then reformulate the non-convex structure into a convex optimization problem together withmore » pricing/reward signal design, and propose a distributed stochastic dual algorithm for solving the reformulated problem while restoring feasible power rates for discrete devices. By doing so, we are able to statistically achieve the solution of the reformulated problem without exposure of any private information from customers. Stability of the proposed schemes is analytically established and numerically corroborated.« less

A method of power analysis based on piecewise discrete Fourier transform

NASA Astrophysics Data System (ADS)

Xin, Miaomiao; Zhang, Yanchi; Xie, Da

2018-04-01

The paper analyzes the existing feature extraction methods. The characteristics of discrete Fourier transform and piecewise aggregation approximation are analyzed. Combining with the advantages of the two methods, a new piecewise discrete Fourier transform is proposed. And the method is used to analyze the lighting power of a large customer in this paper. The time series feature maps of four different cases are compared with the original data, discrete Fourier transform, piecewise aggregation approximation and piecewise discrete Fourier transform. This new method can reflect both the overall trend of electricity change and its internal changes in electrical analysis.

On the Probability of Error and Stochastic Resonance in Discrete Memoryless Channels

DTIC Science & Technology

2013-12-01

Information - Driven Doppler Shift Estimation and Compensation Methods for Underwater Wireless Sensor Networks ”, which is to analyze and develop... underwater wireless sensor networks . We formulated an analytic relationship that relates the average probability of error to the systems parameters, the...thesis, we studied the performance of Discrete Memoryless Channels (DMC), arising in the context of cooperative underwater wireless sensor networks

A Control Simulation Method of High-Speed Trains on Railway Network with Irregular Influence

NASA Astrophysics Data System (ADS)

Yang, Li-Xing; Li, Xiang; Li, Ke-Ping

2011-09-01

Based on the discrete time method, an effective movement control model is designed for a group of highspeed trains on a rail network. The purpose of the model is to investigate the specific traffic characteristics of high-speed trains under the interruption of stochastic irregular events. In the model, the high-speed rail traffic system is supposed to be equipped with the moving-block signalling system to guarantee maximum traversing capacity of the railway. To keep the safety of trains' movements, some operational strategies are proposed to control the movements of trains in the model, including traction operation, braking operation, and entering-station operation. The numerical simulations show that the designed model can well describe the movements of high-speed trains on the rail network. The research results can provide the useful information not only for investigating the propagation features of relevant delays under the irregular disturbance but also for rerouting and rescheduling trains on the rail network.

Different-Level Simultaneous Minimization Scheme for Fault Tolerance of Redundant Manipulator Aided with Discrete-Time Recurrent Neural Network

PubMed Central

Jin, Long; Liao, Bolin; Liu, Mei; Xiao, Lin; Guo, Dongsheng; Yan, Xiaogang

2017-01-01

By incorporating the physical constraints in joint space, a different-level simultaneous minimization scheme, which takes both the robot kinematics and robot dynamics into account, is presented and investigated for fault-tolerant motion planning of redundant manipulator in this paper. The scheme is reformulated as a quadratic program (QP) with equality and bound constraints, which is then solved by a discrete-time recurrent neural network. Simulative verifications based on a six-link planar redundant robot manipulator substantiate the efficacy and accuracy of the presented acceleration fault-tolerant scheme, the resultant QP and the corresponding discrete-time recurrent neural network. PMID:28955217

Combinatorial Reliability and Repair

DTIC Science & Technology

1992-07-01

Press, Oxford, 1987. [2] G. Gordon and L. Traldi, Generalized activities and the Tutte polynomial, Discrete Math . 85 (1990), 167-176. [3] A. B. Huseby, A...Chromatic polynomials and network reliability, Discrete Math . 67 (1987), 57-79. [7] A. Satayanarayana and R. K. Wood, A linear-time algorithm for comput- ing...K-terminal reliability in series-parallel networks, SIAM J. Comput. 14 (1985), 818-832. [8] L. Traldi, Generalized activities and K-terminal reliability, Discrete Math . 96 (1991), 131-149. 4

Voting procedures from the perspective of theory of neural networks

NASA Astrophysics Data System (ADS)

Suleimenov, Ibragim; Panchenko, Sergey; Gabrielyan, Oleg; Pak, Ivan

2016-11-01

It is shown that voting procedure in any authority can be treated as Hopfield neural network analogue. It was revealed that weight coefficients of neural network which has discrete outputs -1 and 1 can be replaced by coefficients of a discrete set (-1, 0, 1). This gives us the opportunity to qualitatively analyze the voting procedure on the basis of limited data about mutual influence of members. It also proves that result of voting procedure is actually taken by network formed by voting members.

Exponential H(infinity) synchronization of general discrete-time chaotic neural networks with or without time delays.

PubMed

Qi, Donglian; Liu, Meiqin; Qiu, Meikang; Zhang, Senlin

2010-08-01

This brief studies exponential H(infinity) synchronization of a class of general discrete-time chaotic neural networks with external disturbance. On the basis of the drive-response concept and H(infinity) control theory, and using Lyapunov-Krasovskii (or Lyapunov) functional, state feedback controllers are established to not only guarantee exponential stable synchronization between two general chaotic neural networks with or without time delays, but also reduce the effect of external disturbance on the synchronization error to a minimal H(infinity) norm constraint. The proposed controllers can be obtained by solving the convex optimization problems represented by linear matrix inequalities. Most discrete-time chaotic systems with or without time delays, such as Hopfield neural networks, cellular neural networks, bidirectional associative memory networks, recurrent multilayer perceptrons, Cohen-Grossberg neural networks, Chua's circuits, etc., can be transformed into this general chaotic neural network to be H(infinity) synchronization controller designed in a unified way. Finally, some illustrated examples with their simulations have been utilized to demonstrate the effectiveness of the proposed methods.

Discrete cloud structure on Neptune

NASA Technical Reports Server (NTRS)

Hammel, H. B.

1989-01-01

Recent CCD imaging data for the discrete cloud structure of Neptune shows that while cloud features at CH4-band wavelengths are manifest in the southern hemisphere, they have not been encountered in the northern hemisphere since 1986. A literature search has shown the reflected CH4-band light from the planet to have come from a single discrete feature at least twice in the last 10 years. Disk-integrated photometry derived from the imaging has demonstrated that a bright cloud feature was responsible for the observed 8900 A diurnal variation in 1986 and 1987.

An effective biometric discretization approach to extract highly discriminative, informative, and privacy-protective binary representation

NASA Astrophysics Data System (ADS)

Lim, Meng-Hui; Teoh, Andrew Beng Jin

2011-12-01

Biometric discretization derives a binary string for each user based on an ordered set of biometric features. This representative string ought to be discriminative, informative, and privacy protective when it is employed as a cryptographic key in various security applications upon error correction. However, it is commonly believed that satisfying the first and the second criteria simultaneously is not feasible, and a tradeoff between them is always definite. In this article, we propose an effective fixed bit allocation-based discretization approach which involves discriminative feature extraction, discriminative feature selection, unsupervised quantization (quantization that does not utilize class information), and linearly separable subcode (LSSC)-based encoding to fulfill all the ideal properties of a binary representation extracted for cryptographic applications. In addition, we examine a number of discriminative feature-selection measures for discretization and identify the proper way of setting an important feature-selection parameter. Encouraging experimental results vindicate the feasibility of our approach.

Learning Time-Varying Coverage Functions

PubMed Central

Du, Nan; Liang, Yingyu; Balcan, Maria-Florina; Song, Le

2015-01-01

Coverage functions are an important class of discrete functions that capture the law of diminishing returns arising naturally from applications in social network analysis, machine learning, and algorithmic game theory. In this paper, we propose a new problem of learning time-varying coverage functions, and develop a novel parametrization of these functions using random features. Based on the connection between time-varying coverage functions and counting processes, we also propose an efficient parameter learning algorithm based on likelihood maximization, and provide a sample complexity analysis. We applied our algorithm to the influence function estimation problem in information diffusion in social networks, and show that with few assumptions about the diffusion processes, our algorithm is able to estimate influence significantly more accurately than existing approaches on both synthetic and real world data. PMID:25960624

Learning Time-Varying Coverage Functions.

PubMed

Du, Nan; Liang, Yingyu; Balcan, Maria-Florina; Song, Le

2014-12-08

Coverage functions are an important class of discrete functions that capture the law of diminishing returns arising naturally from applications in social network analysis, machine learning, and algorithmic game theory. In this paper, we propose a new problem of learning time-varying coverage functions, and develop a novel parametrization of these functions using random features. Based on the connection between time-varying coverage functions and counting processes, we also propose an efficient parameter learning algorithm based on likelihood maximization, and provide a sample complexity analysis. We applied our algorithm to the influence function estimation problem in information diffusion in social networks, and show that with few assumptions about the diffusion processes, our algorithm is able to estimate influence significantly more accurately than existing approaches on both synthetic and real world data.

The Affective Core of Emotion: Linking Pleasure, Subjective Well-Being, and Optimal Metastability in the Brain

PubMed Central

Kringelbach, Morten L.; Berridge, Kent C.

2017-01-01

Arguably, emotion is always valenced—either pleasant or unpleasant—and dependent on the pleasure system. This system serves adaptive evolutionary functions; relying on separable wanting, liking, and learning neural mechanisms mediated by mesocorticolimbic networks driving pleasure cycles with appetitive, consummatory, and satiation phases. Liking is generated in a small set of discrete hedonic hotspots and coldspots, while wanting is linked to dopamine and to larger distributed brain networks. Breakdown of the pleasure system can lead to anhedonia and other features of affective disorders. Eudaimonia and well-being are difficult to study empirically, yet whole-brain computational models could offer novel insights (e.g., routes to eudaimonia such as caregiving of infants or music) potentially linking eudaimonia to optimal metastability in the pleasure system. PMID:28943891

Cluster synchronization transmission of different external signals in discrete uncertain network

NASA Astrophysics Data System (ADS)

Li, Chengren; Lü, Ling; Chen, Liansong; Hong, Yixuan; Zhou, Shuang; Yang, Yiming

2018-07-01

We research cluster synchronization transmissions of different external signals in discrete uncertain network. Based on the Lyapunov theorem, the network controller and the identification law of uncertain adjustment parameter are designed, and they are efficiently used to achieve the cluster synchronization and the identification of uncertain adjustment parameter. In our technical scheme, the network nodes in each cluster and the transmitted external signal can be different, and they allow the presence of uncertain parameters in the network. Especially, we are free to choose the clustering topologies, the cluster number and the node number in each cluster.

INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Estimating Topology of Discrete Dynamical Networks

NASA Astrophysics Data System (ADS)

Guo, Shu-Juan; Fu, Xin-Chu

2010-07-01

In this paper, by applying Lasalle's invariance principle and some results about the trace of a matrix, we propose a method for estimating the topological structure of a discrete dynamical network based on the dynamical evolution of the network. The network concerned can be directed or undirected, weighted or unweighted, and the local dynamics of each node can be nonidentical. The connections among the nodes can be all unknown or partially known. Finally, two examples, including a Hénon map and a central network, are illustrated to verify the theoretical results.

BioNSi: A Discrete Biological Network Simulator Tool.

PubMed

Rubinstein, Amir; Bracha, Noga; Rudner, Liat; Zucker, Noga; Sloin, Hadas E; Chor, Benny

2016-08-05

Modeling and simulation of biological networks is an effective and widely used research methodology. The Biological Network Simulator (BioNSi) is a tool for modeling biological networks and simulating their discrete-time dynamics, implemented as a Cytoscape App. BioNSi includes a visual representation of the network that enables researchers to construct, set the parameters, and observe network behavior under various conditions. To construct a network instance in BioNSi, only partial, qualitative biological data suffices. The tool is aimed for use by experimental biologists and requires no prior computational or mathematical expertise. BioNSi is freely available at http://bionsi.wix.com/bionsi , where a complete user guide and a step-by-step manual can also be found.

Discrete-time Quantum Walks via Interchange Framework and Memory in Quantum Evolution

NASA Astrophysics Data System (ADS)

Dimcovic, Zlatko

One of the newer and rapidly developing approaches in quantum computing is based on "quantum walks," which are quantum processes on discrete space that evolve in either discrete or continuous time and are characterized by mixing of components at each step. The idea emerged in analogy with the classical random walks and stochastic techniques, but these unitary processes are very different even as they have intriguing similarities. This thesis is concerned with study of discrete-time quantum walks. The original motivation from classical Markov chains required for discrete-time quantum walks that one adds an auxiliary Hilbert space, unrelated to the one in which the system evolves, in order to be able to mix components in that space and then take the evolution steps accordingly (based on the state in that space). This additional, "coin," space is very often an internal degree of freedom like spin. We have introduced a general framework for construction of discrete-time quantum walks in a close analogy with the classical random walks with memory that is rather different from the standard "coin" approach. In this method there is no need to bring in a different degree of freedom, while the full state of the system is still described in the direct product of spaces (of states). The state can be thought of as an arrow pointing from the previous to the current site in the evolution, representing the one-step memory. The next step is then controlled by a single local operator assigned to each site in the space, acting quite like a scattering operator. This allows us to probe and solve some problems of interest that have not had successful approaches with "coined" walks. We construct and solve a walk on the binary tree, a structure of great interest but until our result without an explicit discrete time quantum walk, due to difficulties in managing coin spaces necessary in the standard approach. Beyond algorithmic interests, the model based on memory allows one to explore effects of history on the quantum evolution and the subtle emergence of classical features as "memory" is explicitly kept for additional steps. We construct and solve a walk with an additional correlation step, finding interesting new features. On the other hand, the fact that the evolution is driven entirely by a local operator, not involving additional spaces, enables us to choose the Fourier transform as an operator completely controlling the evolution. This in turn allows us to combine the quantum walk approach with Fourier transform based techniques, something decidedly not possible in classical computational physics. We are developing a formalism for building networks manageable by walks constructed with this framework, based on the surprising efficiency of our framework in discovering internals of a simple network that we so far solved. Finally, in line with our expectation that the field of quantum walks can take cues from the rich history of development of the classical stochastic techniques, we establish starting points for the work on non-Abelian quantum walks, with a particular quantum-walk analog of the classical "card shuffling," the walk on the permutation group. In summary, this thesis presents a new framework for construction of discrete time quantum walks, employing and exploring memoried nature of unitary evolution. It is applied to fully solving the problems of: A walk on the binary tree and exploration of the quantum-to-classical transition with increased correlation length (history). It is then used for simple network discovery, and to lay the groundwork for analysis of complex networks, based on combined power of efficient exploration of the Hilbert space (as a walk mixing components) and Fourier transformation (since we can choose this for the evolution operator). We hope to establish this as a general technique as its power would be unmatched by any approaches available in the classical computing. We also looked at the promising and challenging prospect of walks on non-Abelian structures by setting up the problem of "quantum card shuffling," a quantum walk on the permutation group. Relation to other work is thoroughly discussed throughout, along with examination of the context of our work and overviews of our current and future work.

Feature Extraction of Event-Related Potentials Using Wavelets: An Application to Human Performance Monitoring

NASA Technical Reports Server (NTRS)

Trejo, Leonard J.; Shensa, Mark J.; Remington, Roger W. (Technical Monitor)

1998-01-01

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many f ree parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation,-, algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring

NASA Technical Reports Server (NTRS)

Trejo, L. J.; Shensa, M. J.

1999-01-01

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Copyright 1999 Academic Press.

Tensor network state correspondence and holography

NASA Astrophysics Data System (ADS)

Singh, Sukhwinder

2018-01-01

In recent years, tensor network states have emerged as a very useful conceptual and simulation framework to study quantum many-body systems at low energies. In this paper, we describe a particular way in which any given tensor network can be viewed as a representation of two different quantum many-body states. The two quantum many-body states are said to correspond to each other by means of the tensor network. We apply this "tensor network state correspondence"—a correspondence between quantum many-body states mediated by tensor networks as we describe—to the multi-scale entanglement renormalization ansatz (MERA) representation of ground states of one dimensional (1D) quantum many-body systems. Since the MERA is a 2D hyperbolic tensor network (the extra dimension is identified as the length scale of the 1D system), the two quantum many-body states obtained from the MERA, via tensor network state correspondence, are seen to live in the bulk and on the boundary of a discrete hyperbolic geometry. The bulk state so obtained from a MERA exhibits interesting features, some of which caricature known features of the holographic correspondence of String theory. We show how (i) the bulk state admits a description in terms of "holographic screens", (ii) the conformal field theory data associated with a critical ground state can be obtained from the corresponding bulk state, in particular, how pointlike boundary operators are identified with extended bulk operators. (iii) We also present numerical results to illustrate that bulk states, dual to ground states of several critical spin chains, have exponentially decaying correlations, and that the bulk correlation length generally decreases with increase in central charge for these spin chains.

Discrete-time BAM neural networks with variable delays

NASA Astrophysics Data System (ADS)

Liu, Xin-Ge; Tang, Mei-Lan; Martin, Ralph; Liu, Xin-Bi

2007-07-01

This Letter deals with the global exponential stability of discrete-time bidirectional associative memory (BAM) neural networks with variable delays. Using a Lyapunov functional, and linear matrix inequality techniques (LMI), we derive a new delay-dependent exponential stability criterion for BAM neural networks with variable delays. As this criterion has no extra constraints on the variable delay functions, it can be applied to quite general BAM neural networks with a broad range of time delay functions. It is also easy to use in practice. An example is provided to illustrate the theoretical development.

Discrete Dynamics Lab

NASA Astrophysics Data System (ADS)

Wuensche, Andrew

DDLab is interactive graphics software for creating, visualizing, and analyzing many aspects of Cellular Automata, Random Boolean Networks, and Discrete Dynamical Networks in general and studying their behavior, both from the time-series perspective — space-time patterns, and from the state-space perspective — attractor basins. DDLab is relevant to research, applications, and education in the fields of complexity, self-organization, emergent phenomena, chaos, collision-based computing, neural networks, content addressable memory, genetic regulatory networks, dynamical encryption, generative art and music, and the study of the abstract mathematical/physical/dynamical phenomena in their own right.

Orienting in Virtual Environments: How Are Surface Features and Environmental Geometry Weighted in an Orientation Task?

ERIC Educational Resources Information Center

Kelly, Debbie M.; Bischof, Walter F.

2008-01-01

We investigated how human adults orient in enclosed virtual environments, when discrete landmark information is not available and participants have to rely on geometric and featural information on the environmental surfaces. In contrast to earlier studies, where, for women, the featural information from discrete landmarks overshadowed the encoding…

Nano-iron Tracer Test for Characterizing Preferential Flow Path in Fractured Rock

NASA Astrophysics Data System (ADS)

Chia, Y.; Chuang, P. Y.

2015-12-01

Deterministic description of the discrete features interpreted from site characterization is desirable for developing a discrete fracture network conceptual model. It is often difficult, however, to delineate preferential flow path through a network of discrete fractures in the field. A preliminary cross-borehole nano-iron tracer test was conducted to characterize the preferential flow path in fractured shale bedrock at a hydrogeological research station. Prior to the test, heat-pulse flowmeter measurements were performed to detect permeable fracture zones at both the injection well and the observation well. While a few fracture zones are found permeable, most are not really permeable. Chemical reduction method was used to synthesize nano zero-valent iron particles with a diameter of 50~150 nm. The conductivity of nano-iron solution is about 3100 μs/cm. The recorded fluid conductivity shows the arrival of nano-iron solution in the observation well 11.5 minutes after it was released from the injection well. The magnetism of zero-valent iron enables it to be absorbed on magnet array designed to locate the depth of incoming tracer. We found nearly all of absorbed iron on the magnet array in the observation well were distributed near the most permeable fracture zone. The test results revealed a preferential flow path through a permeable fracture zone between the injection well and the observation well. The estimated hydraulic conductivity of the connected fracture is 2.2 × 10-3 m/s. This preliminary study indicated that nano-iron tracer test has the potential to characterize preferential flow path in fractured rock.

Distributed rewiring model for complex networking: The effect of local rewiring rules on final structural properties.

PubMed

López Chavira, Magali Alexander; Marcelín-Jiménez, Ricardo

2017-01-01

The study of complex networks has become an important subject over the last decades. It has been shown that these structures have special features, such as their diameter, or their average path length, which in turn are the explanation of some functional properties in a system such as its fault tolerance, its fragility before attacks, or the ability to support routing procedures. In the present work, we study some of the forces that help a network to evolve to the point where structural properties are settled. Although our work is mainly focused on the possibility of applying our ideas to Information and Communication Technologies systems, we consider that our results may contribute to understanding different scenarios where complex networks have become an important modeling tool. Using a discrete event simulator, we get each node to discover the shortcuts that may connect it with regions away from its local environment. Based on this partial knowledge, each node can rewire some of its links, which allows modifying the topology of the entire underlying graph to achieve new structural properties. We proposed a distributed rewiring model that creates networks with features similar to those found in complex networks. Although each node acts in a distributed way and seeking to reduce only the trajectories of its packets, we observed a decrease of diameter and an increase in clustering coefficient in the global structure compared to the initial graph. Furthermore, we can find different final structures depending on slight changes in the local rewiring rules.

Simulating Operation of a Complex Sensor Network

NASA Technical Reports Server (NTRS)

Jennings, Esther; Clare, Loren; Woo, Simon

2008-01-01

Simulation Tool for ASCTA Microsensor Network Architecture (STAMiNA) ["ASCTA" denotes the Advanced Sensors Collaborative Technology Alliance.] is a computer program for evaluating conceptual sensor networks deployed over terrain to provide military situational awareness. This or a similar program is needed because of the complexity of interactions among such diverse phenomena as sensing and communication portions of a network, deployment of sensor nodes, effects of terrain, data-fusion algorithms, and threat characteristics. STAMiNA is built upon a commercial network-simulator engine, with extensions to include both sensing and communication models in a discrete-event simulation environment. Users can define (1) a mission environment, including terrain features; (2) objects to be sensed; (3) placements and modalities of sensors, abilities of sensors to sense objects of various types, and sensor false alarm rates; (4) trajectories of threatening objects; (5) means of dissemination and fusion of data; and (6) various network configurations. By use of STAMiNA, one can simulate detection of targets through sensing, dissemination of information by various wireless communication subsystems under various scenarios, and fusion of information, incorporating such metrics as target-detection probabilities, false-alarm rates, and communication loads, and capturing effects of terrain and threat.

Tactical Communications Network Modelling and Reliability Analysis: An Overview

DTIC Science & Technology

1991-11-01

Transactions on Reliability. Vol 31 (1982), pp 359-361. [62] B. N. Clark and C. L. Colbourn. "Unit Disk Graphs", Discrete Math ., Vol 86 (1990), pp 165-177. [63...C. L. Colbourn, "Network Resiliance". SIAM Journal of Algebra and Discrete Math . Vol 8. (1987), pp 404-409. [64] W. H. Debany, P. K. Varshney, and C...34Bibliography on Dominatinn in Graphs and Some Basic Definitions of Domination Parameters". Discrete Math .. Vol 86 (1990). pp 257-277. [76] C. L. Hwang. F. A

Stability Analysis of Continuous-Time and Discrete-Time Quaternion-Valued Neural Networks With Linear Threshold Neurons.

PubMed

Chen, Xiaofeng; Song, Qiankun; Li, Zhongshan; Zhao, Zhenjiang; Liu, Yurong

2018-07-01

This paper addresses the problem of stability for continuous-time and discrete-time quaternion-valued neural networks (QVNNs) with linear threshold neurons. Applying the semidiscretization technique to the continuous-time QVNNs, the discrete-time analogs are obtained, which preserve the dynamical characteristics of their continuous-time counterparts. Via the plural decomposition method of quaternion, homeomorphic mapping theorem, as well as Lyapunov theorem, some sufficient conditions on the existence, uniqueness, and global asymptotical stability of the equilibrium point are derived for the continuous-time QVNNs and their discrete-time analogs, respectively. Furthermore, a uniform sufficient condition on the existence, uniqueness, and global asymptotical stability of the equilibrium point is obtained for both continuous-time QVNNs and their discrete-time version. Finally, two numerical examples are provided to substantiate the effectiveness of the proposed results.

Modular architecture for robotics and teleoperation

DOEpatents

Anderson, Robert J.

1996-12-03

Systems and methods for modularization and discretization of real-time robot, telerobot and teleoperation systems using passive, network based control laws. Modules consist of network one-ports and two-ports. Wave variables and position information are passed between modules. The behavior of each module is decomposed into uncoupled linear-time-invariant, and coupled, nonlinear memoryless elements and then are separately discretized.

Global exponential periodicity and stability of discrete-time complex-valued recurrent neural networks with time-delays.

PubMed

Hu, Jin; Wang, Jun

2015-06-01

In recent years, complex-valued recurrent neural networks have been developed and analysed in-depth in view of that they have good modelling performance for some applications involving complex-valued elements. In implementing continuous-time dynamical systems for simulation or computational purposes, it is quite necessary to utilize a discrete-time model which is an analogue of the continuous-time system. In this paper, we analyse a discrete-time complex-valued recurrent neural network model and obtain the sufficient conditions on its global exponential periodicity and exponential stability. Simulation results of several numerical examples are delineated to illustrate the theoretical results and an application on associative memory is also given. Copyright © 2015 Elsevier Ltd. All rights reserved.

Optimal Non-Invasive Fault Classification Model for Packaged Ceramic Tile Quality Monitoring Using MMW Imaging

NASA Astrophysics Data System (ADS)

Agarwal, Smriti; Singh, Dharmendra

2016-04-01

Millimeter wave (MMW) frequency has emerged as an efficient tool for different stand-off imaging applications. In this paper, we have dealt with a novel MMW imaging application, i.e., non-invasive packaged goods quality estimation for industrial quality monitoring applications. An active MMW imaging radar operating at 60 GHz has been ingeniously designed for concealed fault estimation. Ceramic tiles covered with commonly used packaging cardboard were used as concealed targets for undercover fault classification. A comparison of computer vision-based state-of-the-art feature extraction techniques, viz, discrete Fourier transform (DFT), wavelet transform (WT), principal component analysis (PCA), gray level co-occurrence texture (GLCM), and histogram of oriented gradient (HOG) has been done with respect to their efficient and differentiable feature vector generation capability for undercover target fault classification. An extensive number of experiments were performed with different ceramic tile fault configurations, viz., vertical crack, horizontal crack, random crack, diagonal crack along with the non-faulty tiles. Further, an independent algorithm validation was done demonstrating classification accuracy: 80, 86.67, 73.33, and 93.33 % for DFT, WT, PCA, GLCM, and HOG feature-based artificial neural network (ANN) classifier models, respectively. Classification results show good capability for HOG feature extraction technique towards non-destructive quality inspection with appreciably low false alarm as compared to other techniques. Thereby, a robust and optimal image feature-based neural network classification model has been proposed for non-invasive, automatic fault monitoring for a financially and commercially competent industrial growth.

Exponential stability preservation in semi-discretisations of BAM networks with nonlinear impulses

NASA Astrophysics Data System (ADS)

Mohamad, Sannay; Gopalsamy, K.

2009-01-01

This paper demonstrates the reliability of a discrete-time analogue in preserving the exponential convergence of a bidirectional associative memory (BAM) network that is subject to nonlinear impulses. The analogue derived from a semi-discretisation technique with the value of the time-step fixed is treated as a discrete-time dynamical system while its exponential convergence towards an equilibrium state is studied. Thereby, a family of sufficiency conditions governing the network parameters and the impulse magnitude and frequency is obtained for the convergence. As special cases, one can obtain from our results, those corresponding to the non-impulsive discrete-time BAM networks and also those corresponding to continuous-time (impulsive and non-impulsive) systems. A relation between the Lyapunov exponent of the non-impulsive system and that of the impulsive system involving the size of the impulses and the inter-impulse intervals is obtained.

Discrete gravity on random tensor network and holographic Rényi entropy

NASA Astrophysics Data System (ADS)

Han, Muxin; Huang, Shilin

2017-11-01

In this paper we apply the discrete gravity and Regge calculus to tensor networks and Anti-de Sitter/conformal field theory (AdS/CFT) correspondence. We construct the boundary many-body quantum state |Ψ〉 using random tensor networks as the holographic mapping, applied to the Wheeler-deWitt wave function of bulk Euclidean discrete gravity in 3 dimensions. The entanglement Rényi entropy of |Ψ〉 is shown to holographically relate to the on-shell action of Einstein gravity on a branch cover bulk manifold. The resulting Rényi entropy S n of |Ψ〉 approximates with high precision the Rényi entropy of ground state in 2-dimensional conformal field theory (CFT). In particular it reproduces the correct n dependence. Our results develop the framework of realizing the AdS3/CFT2 correspondence on random tensor networks, and provide a new proposal to approximate the CFT ground state.

Assessment of uncertainty in discrete fracture network modeling using probabilistic distribution method.

PubMed

Wei, Yaqiang; Dong, Yanhui; Yeh, Tian-Chyi J; Li, Xiao; Wang, Liheng; Zha, Yuanyuan

2017-11-01

There have been widespread concerns about solute transport problems in fractured media, e.g. the disposal of high-level radioactive waste in geological fractured rocks. Numerical simulation of particle tracking is gradually being employed to address these issues. Traditional predictions of radioactive waste transport using discrete fracture network (DFN) models often consider one particular realization of the fracture distribution based on fracture statistic features. This significantly underestimates the uncertainty of the risk of radioactive waste deposit evaluation. To adequately assess the uncertainty during the DFN modeling in a potential site for the disposal of high-level radioactive waste, this paper utilized the probabilistic distribution method (PDM). The method was applied to evaluate the risk of nuclear waste deposit in Beishan, China. Moreover, the impact of the number of realizations on the simulation results was analyzed. In particular, the differences between the modeling results of one realization and multiple realizations were demonstrated. Probabilistic distributions of 20 realizations at different times were also obtained. The results showed that the employed PDM can be used to describe the ranges of the contaminant particle transport. The high-possibility contaminated areas near the release point were more concentrated than the farther areas after 5E6 days, which was 25,400 m 2 .

Numerical modeling of fluid flow in a fault zone: a case of study from Majella Mountain (Italy).

NASA Astrophysics Data System (ADS)

Romano, Valentina; Battaglia, Maurizio; Bigi, Sabina; De'Haven Hyman, Jeffrey; Valocchi, Albert J.

2017-04-01

The study of fluid flow in fractured rocks plays a key role in reservoir management, including CO2 sequestration and waste isolation. We present a numerical model of fluid flow in a fault zone, based on field data acquired in Majella Mountain, in the Central Apennines (Italy). This fault zone is considered a good analogue for the massive presence of fluid migration in the form of tar. Faults are mechanical features and cause permeability heterogeneities in the upper crust, so they strongly influence fluid flow. The distribution of the main components (core, damage zone) can lead the fault zone to act as a conduit, a barrier, or a combined conduit-barrier system. We integrated existing information and our own structural surveys of the area to better identify the major fault features (e.g., type of fractures, statistical properties, geometrical and petro-physical characteristics). In our model the damage zones of the fault are described as discretely fractured medium, while the core of the fault as a porous one. Our model utilizes the dfnWorks code, a parallelized computational suite, developed at Los Alamos National Laboratory (LANL), that generates three dimensional Discrete Fracture Network (DFN) of the damage zones of the fault and characterizes its hydraulic parameters. The challenge of the study is the coupling between the discrete domain of the damage zones and the continuum one of the core. The field investigations and the basic computational workflow will be described, along with preliminary results of fluid flow simulation at the scale of the fault.

Networked Guidance and Control for Mobile Multi-Agent Systems: A Multi-terminal (Network) Information Theoretic Approach

DTIC Science & Technology

2012-01-19

time , i.e., the state of the system is the input delayed by one time unit. In contrast with classical approaches, here the control action must be a...Transactions on Automatic Control , Vol. 56, No. 9, September 2011, Pages 2013-2025 Consider a first order linear time -invariant discrete time system driven by...1, January 2010, Pages 175-179 Consider a discrete- time networked control system , in which the controller has direct access to noisy

Robust feature detection and local classification for surfaces based on moment analysis.

PubMed

Clarenz, Ulrich; Rumpf, Martin; Telea, Alexandru

2004-01-01

The stable local classification of discrete surfaces with respect to features such as edges and corners or concave and convex regions, respectively, is as quite difficult as well as indispensable for many surface processing applications. Usually, the feature detection is done via a local curvature analysis. If concerned with large triangular and irregular grids, e.g., generated via a marching cube algorithm, the detectors are tedious to treat and a robust classification is hard to achieve. Here, a local classification method on surfaces is presented which avoids the evaluation of discretized curvature quantities. Moreover, it provides an indicator for smoothness of a given discrete surface and comes together with a built-in multiscale. The proposed classification tool is based on local zero and first moments on the discrete surface. The corresponding integral quantities are stable to compute and they give less noisy results compared to discrete curvature quantities. The stencil width for the integration of the moments turns out to be the scale parameter. Prospective surface processing applications are the segmentation on surfaces, surface comparison, and matching and surface modeling. Here, a method for feature preserving fairing of surfaces is discussed to underline the applicability of the presented approach.

Deployment-based lifetime optimization for linear wireless sensor networks considering both retransmission and discrete power control.

PubMed

Li, Ruiying; Ma, Wenting; Huang, Ning; Kang, Rui

2017-01-01

A sophisticated method for node deployment can efficiently reduce the energy consumption of a Wireless Sensor Network (WSN) and prolong the corresponding network lifetime. Pioneers have proposed many node deployment based lifetime optimization methods for WSNs, however, the retransmission mechanism and the discrete power control strategy, which are widely used in practice and have large effect on the network energy consumption, are often neglected and assumed as a continuous one, respectively, in the previous studies. In this paper, both retransmission and discrete power control are considered together, and a more realistic energy-consumption-based network lifetime model for linear WSNs is provided. Using this model, we then propose a generic deployment-based optimization model that maximizes network lifetime under coverage, connectivity and transmission rate success constraints. The more accurate lifetime evaluation conduces to a longer optimal network lifetime in the realistic situation. To illustrate the effectiveness of our method, both one-tiered and two-tiered uniformly and non-uniformly distributed linear WSNs are optimized in our case studies, and the comparisons between our optimal results and those based on relatively inaccurate lifetime evaluation show the advantage of our method when investigating WSN lifetime optimization problems.

47 CFR 76.1508 - Network non-duplication.

Code of Federal Regulations, 2010 CFR

2010-10-01

... within unincorporated areas and including single, discrete unincorporated areas). Any provision of § 76... unincorporated communities within unincorporated areas and including single, discrete unincorporated areas). ...

A weighted communicability measure applied to complex brain networks

PubMed Central

Crofts, Jonathan J.; Higham, Desmond J.

2009-01-01

Recent advances in experimental neuroscience allow non-invasive studies of the white matter tracts in the human central nervous system, thus making available cutting-edge brain anatomical data describing these global connectivity patterns. Through magnetic resonance imaging, this non-invasive technique is able to infer a snapshot of the cortical network within the living human brain. Here, we report on the initial success of a new weighted network communicability measure in distinguishing local and global differences between diseased patients and controls. This approach builds on recent advances in network science, where an underlying connectivity structure is used as a means to measure the ease with which information can flow between nodes. One advantage of our method is that it deals directly with the real-valued connectivity data, thereby avoiding the need to discretize the corresponding adjacency matrix, i.e. to round weights up to 1 or down to 0, depending upon some threshold value. Experimental results indicate that the new approach is able to extract biologically relevant features that are not immediately apparent from the raw connectivity data. PMID:19141429

Maximal aggregation of polynomial dynamical systems

PubMed Central

Cardelli, Luca; Tschaikowski, Max

2017-01-01

Ordinary differential equations (ODEs) with polynomial derivatives are a fundamental tool for understanding the dynamics of systems across many branches of science, but our ability to gain mechanistic insight and effectively conduct numerical evaluations is critically hindered when dealing with large models. Here we propose an aggregation technique that rests on two notions of equivalence relating ODE variables whenever they have the same solution (backward criterion) or if a self-consistent system can be written for describing the evolution of sums of variables in the same equivalence class (forward criterion). A key feature of our proposal is to encode a polynomial ODE system into a finitary structure akin to a formal chemical reaction network. This enables the development of a discrete algorithm to efficiently compute the largest equivalence, building on approaches rooted in computer science to minimize basic models of computation through iterative partition refinements. The physical interpretability of the aggregation is shown on polynomial ODE systems for biochemical reaction networks, gene regulatory networks, and evolutionary game theory. PMID:28878023

H∞ output tracking control of discrete-time nonlinear systems via standard neural network models.

PubMed

Liu, Meiqin; Zhang, Senlin; Chen, Haiyang; Sheng, Weihua

2014-10-01

This brief proposes an output tracking control for a class of discrete-time nonlinear systems with disturbances. A standard neural network model is used to represent discrete-time nonlinear systems whose nonlinearity satisfies the sector conditions. H∞ control performance for the closed-loop system including the standard neural network model, the reference model, and state feedback controller is analyzed using Lyapunov-Krasovskii stability theorem and linear matrix inequality (LMI) approach. The H∞ controller, of which the parameters are obtained by solving LMIs, guarantees that the output of the closed-loop system closely tracks the output of a given reference model well, and reduces the influence of disturbances on the tracking error. Three numerical examples are provided to show the effectiveness of the proposed H∞ output tracking design approach.

CGBayesNets: Conditional Gaussian Bayesian Network Learning and Inference with Mixed Discrete and Continuous Data

PubMed Central

Weiss, Scott T.

2014-01-01

Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com. PMID:24922310

CGBayesNets: conditional Gaussian Bayesian network learning and inference with mixed discrete and continuous data.

PubMed

McGeachie, Michael J; Chang, Hsun-Hsien; Weiss, Scott T

2014-06-01

Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com.

A New Local Bipolar Autoassociative Memory Based on External Inputs of Discrete Recurrent Neural Networks With Time Delay.

PubMed

Zhou, Caigen; Zeng, Xiaoqin; Luo, Chaomin; Zhang, Huaguang

In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation function. The weight parameters of neural networks are acquired by a set of inequalities without the learning procedure. The global exponential stability criteria are established to ensure the accuracy of the restored patterns by considering time delays and external inputs. The proposed methodology is capable of effectively overcoming spurious memory patterns and achieving memory capacity. The effectiveness, robustness, and fault-tolerant capability are validated by simulated experiments.In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation function. The weight parameters of neural networks are acquired by a set of inequalities without the learning procedure. The global exponential stability criteria are established to ensure the accuracy of the restored patterns by considering time delays and external inputs. The proposed methodology is capable of effectively overcoming spurious memory patterns and achieving memory capacity. The effectiveness, robustness, and fault-tolerant capability are validated by simulated experiments.

Parallel discrete event simulation using shared memory

NASA Technical Reports Server (NTRS)

Reed, Daniel A.; Malony, Allen D.; Mccredie, Bradley D.

1988-01-01

With traditional event-list techniques, evaluating a detailed discrete-event simulation-model can often require hours or even days of computation time. By eliminating the event list and maintaining only sufficient synchronization to ensure causality, parallel simulation can potentially provide speedups that are linear in the numbers of processors. A set of shared-memory experiments, using the Chandy-Misra distributed-simulation algorithm, to simulate networks of queues is presented. Parameters of the study include queueing network topology and routing probabilities, number of processors, and assignment of network nodes to processors. These experiments show that Chandy-Misra distributed simulation is a questionable alternative to sequential-simulation of most queueing network models.

Analysis hierarchical model for discrete event systems

NASA Astrophysics Data System (ADS)

Ciortea, E. M.

2015-11-01

The This paper presents the hierarchical model based on discrete event network for robotic systems. Based on the hierarchical approach, Petri network is analysed as a network of the highest conceptual level and the lowest level of local control. For modelling and control of complex robotic systems using extended Petri nets. Such a system is structured, controlled and analysed in this paper by using Visual Object Net ++ package that is relatively simple and easy to use, and the results are shown as representations easy to interpret. The hierarchical structure of the robotic system is implemented on computers analysed using specialized programs. Implementation of hierarchical model discrete event systems, as a real-time operating system on a computer network connected via a serial bus is possible, where each computer is dedicated to local and Petri model of a subsystem global robotic system. Since Petri models are simplified to apply general computers, analysis, modelling, complex manufacturing systems control can be achieved using Petri nets. Discrete event systems is a pragmatic tool for modelling industrial systems. For system modelling using Petri nets because we have our system where discrete event. To highlight the auxiliary time Petri model using transport stream divided into hierarchical levels and sections are analysed successively. Proposed robotic system simulation using timed Petri, offers the opportunity to view the robotic time. Application of goods or robotic and transmission times obtained by measuring spot is obtained graphics showing the average time for transport activity, using the parameters sets of finished products. individually.

Permeability of three-dimensional rock masses containing geomechanically-grown anisotropic fracture networks

NASA Astrophysics Data System (ADS)

Thomas, R. N.; Ebigbo, A.; Paluszny, A.; Zimmerman, R. W.

2016-12-01

The macroscopic permeability of 3D anisotropic geomechanically-generated fractured rock masses is investigated. The explicitly computed permeabilities are compared to the predictions of classical inclusion-based effective medium theories, and to the permeability of networks of randomly oriented and stochastically generated fractures. Stochastically generated fracture networks lack features that arise from fracture interaction, such as non-planarity, and termination of fractures upon intersection. Recent discrete fracture network studies include heuristic rules that introduce these features to some extent. In this work, fractures grow and extend under tension from a finite set of initial flaws. The finite element method is used to compute displacements, and modal stress intensity factors are computed around each fracture tip using the interaction integral accumulated over a set of virtual discs. Fracture apertures emerge as a result of simulations that honour the constraints of stress equilibrium and mass conservation. The macroscopic permeabilities are explicitly calculated by solving the local cubic law in the fractures, on an element-by-element basis, coupled to Darcy's law in the matrix. The permeabilities are then compared to the estimates given by the symmetric and asymmetric versions of the self-consistent approximation, which, for randomly fractured volumes, were previously demonstrated to be most accurate of the inclusion-based effective medium methods (Ebigbo et al., Transport in Porous Media, 2016). The permeabilities of several dozen geomechanical networks are computed as a function of density and in situ stresses. For anisotropic networks, we find that the asymmetric and symmetric self-consistent methods overestimate the effective permeability in the direction of the dominant fracture set. Effective permeabilities that are more strongly dependent on the connectivity of two or more fracture sets are more accurately captured by the effective medium models.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Altazi, B; Fernandez, D; Zhang, G

Purpose: Site-specific investigations of the role of Radiomics in cancer diagnosis and therapy are needed. We report of the reproducibility of quantitative image features over different discrete voxel levels in PET/CT images of cervical cancer. Methods: Our dataset consisted of the pretreatment PET/CT scans from a cohort of 76 patients diagnosed with cervical cancer, FIGO stage IB-IVA, age range 31–76 years, treated with external beam radiation therapy to a dose range between 45–50.4 Gy (median dose: 45 Gy), concurrent cisplatin chemotherapy and MRI-based Brachytherapy to a dose of 20–30 Gy (median total dose: 28 Gy). Two board certified radiation oncologistsmore » delineated Metabolic Tumor volume (MTV) for each patient. Radiomics features were extracted based on 32, 64, 128 and 256 discretization levels (DL). The 64 level was chosen to be the reference DL. Features were calculated based on Co-occurrence (COM), Gray Level Size Zone (GLSZM) and Run-Length (RLM) matrices. Mean Percentage Differences (Δ) of features for discrete levels were determined. Normality distribution of Δ was tested using Kolomogorov - Smirnov test. Bland-Altman test was used to investigate differences between feature values measured on different DL. The mean, standard deviation and upper/lower value limits for each pair of DL were calculated. Interclass Correlation Coefficient (ICC) analysis was performed to examine the reliability of repeated measures within the context of the test re-test format. Results: 3 global and 5 regional features out of 48 features showed distribution not significantly different from a normal one. The reproducible features passed the normality test. Only 5 reproducible results were reliable, ICC range 0.7 – 0.99. Conclusion: Most of the radiomics features tested showed sensitivity to voxel level discretization between (32 – 256). Only 4 GLSZM, 3 COM and 1 RLM showed insensitivity towards mentioned discrete levels.« less

Automatic emotional expression analysis from eye area

NASA Astrophysics Data System (ADS)

Akkoç, Betül; Arslan, Ahmet

2015-02-01

Eyes play an important role in expressing emotions in nonverbal communication. In the present study, emotional expression classification was performed based on the features that were automatically extracted from the eye area. Fırst, the face area and the eye area were automatically extracted from the captured image. Afterwards, the parameters to be used for the analysis through discrete wavelet transformation were obtained from the eye area. Using these parameters, emotional expression analysis was performed through artificial intelligence techniques. As the result of the experimental studies, 6 universal emotions consisting of expressions of happiness, sadness, surprise, disgust, anger and fear were classified at a success rate of 84% using artificial neural networks.

Simulation of two-phase flow in horizontal fracture networks with numerical manifold method

NASA Astrophysics Data System (ADS)

Ma, G. W.; Wang, H. D.; Fan, L. F.; Wang, B.

2017-10-01

The paper presents simulation of two-phase flow in discrete fracture networks with numerical manifold method (NMM). Each phase of fluids is considered to be confined within the assumed discrete interfaces in the present method. The homogeneous model is modified to approach the mixed fluids. A new mathematical cover formation for fracture intersection is proposed to satisfy the mass conservation. NMM simulations of two-phase flow in a single fracture, intersection, and fracture network are illustrated graphically and validated by the analytical method or the finite element method. Results show that the motion status of discrete interface significantly depends on the ratio of mobility of two fluids rather than the value of the mobility. The variation of fluid velocity in each fracture segment and the driven fluid content are also influenced by the ratio of mobility. The advantages of NMM in the simulation of two-phase flow in a fracture network are demonstrated in the present study, which can be further developed for practical engineering applications.

Efficient feature selection using a hybrid algorithm for the task of epileptic seizure detection

NASA Astrophysics Data System (ADS)

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

2014-07-01

Feature selection is a very important aspect in the field of machine learning. It entails the search of an optimal subset from a very large data set with high dimensional feature space. Apart from eliminating redundant features and reducing computational cost, a good selection of feature also leads to higher prediction and classification accuracy. In this paper, an efficient feature selection technique is introduced in the task of epileptic seizure detection. The raw data are electroencephalography (EEG) signals. Using discrete wavelet transform, the biomedical signals were decomposed into several sets of wavelet coefficients. To reduce the dimension of these wavelet coefficients, a feature selection method that combines the strength of both filter and wrapper methods is proposed. Principal component analysis (PCA) is used as part of the filter method. As for wrapper method, the evolutionary harmony search (HS) algorithm is employed. This metaheuristic method aims at finding the best discriminating set of features from the original data. The obtained features were then used as input for an automated classifier, namely wavelet neural networks (WNNs). The WNNs model was trained to perform a binary classification task, that is, to determine whether a given EEG signal was normal or epileptic. For comparison purposes, different sets of features were also used as input. Simulation results showed that the WNNs that used the features chosen by the hybrid algorithm achieved the highest overall classification accuracy.

Characterizing Cancer Drug Response and Biological Correlates: A Geometric Network Approach.

PubMed

Pouryahya, Maryam; Oh, Jung Hun; Mathews, James C; Deasy, Joseph O; Tannenbaum, Allen R

2018-04-23

In the present work, we apply a geometric network approach to study common biological features of anticancer drug response. We use for this purpose the panel of 60 human cell lines (NCI-60) provided by the National Cancer Institute. Our study suggests that mathematical tools for network-based analysis can provide novel insights into drug response and cancer biology. We adopted a discrete notion of Ricci curvature to measure, via a link between Ricci curvature and network robustness established by the theory of optimal mass transport, the robustness of biological networks constructed with a pre-treatment gene expression dataset and coupled the results with the GI50 response of the cell lines to the drugs. Based on the resulting drug response ranking, we assessed the impact of genes that are likely associated with individual drug response. For genes identified as important, we performed a gene ontology enrichment analysis using a curated bioinformatics database which resulted in biological processes associated with drug response across cell lines and tissue types which are plausible from the point of view of the biological literature. These results demonstrate the potential of using the mathematical network analysis in assessing drug response and in identifying relevant genomic biomarkers and biological processes for precision medicine.

Transport in simple networks described by an integrable discrete nonlinear Schrödinger equation.

PubMed

Nakamura, K; Sobirov, Z A; Matrasulov, D U; Sawada, S

2011-08-01

We elucidate the case in which the Ablowitz-Ladik (AL)-type discrete nonlinear Schrödinger equation (NLSE) on simple networks (e.g., star graphs and tree graphs) becomes completely integrable just as in the case of a simple one-dimensional (1D) discrete chain. The strength of cubic nonlinearity is different from bond to bond, and networks are assumed to have at least two semi-infinite bonds with one of them working as an incoming bond. The present work is a nontrivial extension of our preceding one [Sobirov et al., Phys. Rev. E 81, 066602 (2010)] on the continuum NLSE to the discrete case. We find (1) the solution on each bond is a part of the universal (bond-independent) AL soliton solution on the 1D discrete chain, but it is multiplied by the inverse of the square root of bond-dependent nonlinearity; (2) nonlinearities at individual bonds around each vertex must satisfy a sum rule; and (3) under findings 1 and 2, there exist an infinite number of constants of motion. As a practical issue, with the use of an AL soliton injected through the incoming bond, we obtain transmission probabilities inversely proportional to the strength of nonlinearity on the outgoing bonds.

Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states

PubMed Central

Jang, Sumin; Choubey, Sandeep; Furchtgott, Leon; Zou, Ling-Nan; Doyle, Adele; Menon, Vilas; Loew, Ethan B; Krostag, Anne-Rachel; Martinez, Refugio A; Madisen, Linda; Levi, Boaz P; Ramanathan, Sharad

2017-01-01

The complexity of gene regulatory networks that lead multipotent cells to acquire different cell fates makes a quantitative understanding of differentiation challenging. Using a statistical framework to analyze single-cell transcriptomics data, we infer the gene expression dynamics of early mouse embryonic stem (mES) cell differentiation, uncovering discrete transitions across nine cell states. We validate the predicted transitions across discrete states using flow cytometry. Moreover, using live-cell microscopy, we show that individual cells undergo abrupt transitions from a naïve to primed pluripotent state. Using the inferred discrete cell states to build a probabilistic model for the underlying gene regulatory network, we further predict and experimentally verify that these states have unique response to perturbations, thus defining them functionally. Our study provides a framework to infer the dynamics of differentiation from single cell transcriptomics data and to build predictive models of the gene regulatory networks that drive the sequence of cell fate decisions during development. DOI: http://dx.doi.org/10.7554/eLife.20487.001 PMID:28296635

Coupled intertwiner dynamics: A toy model for coupling matter to spin foam models

NASA Astrophysics Data System (ADS)

Steinhaus, Sebastian

2015-09-01

The universal coupling of matter and gravity is one of the most important features of general relativity. In quantum gravity, in particular spin foams, matter couplings have been defined in the past, yet the mutual dynamics, in particular if matter and gravity are strongly coupled, are hardly explored, which is related to the definition of both matter and gravitational degrees of freedom on the discretization. However, extracting these mutual dynamics is crucial in testing the viability of the spin foam approach and also establishing connections to other discrete approaches such as lattice gauge theories. Therefore, we introduce a simple two-dimensional toy model for Yang-Mills coupled to spin foams, namely an Ising model coupled to so-called intertwiner models defined for SU (2 )k. The two systems are coupled by choosing the Ising coupling constant to depend on spin labels of the background, as these are interpreted as the edge lengths of the discretization. We coarse grain this toy model via tensor network renormalization and uncover an interesting dynamics: the Ising phase transition temperature turns out to be sensitive to the background configurations and conversely, the Ising model can induce phase transitions in the background. Moreover, we observe a strong coupling of both systems if close to both phase transitions.

Intelligent postoperative morbidity prediction of heart disease using artificial intelligence techniques.

PubMed

Hsieh, Nan-Chen; Hung, Lun-Ping; Shih, Chun-Che; Keh, Huan-Chao; Chan, Chien-Hui

2012-06-01

Endovascular aneurysm repair (EVAR) is an advanced minimally invasive surgical technology that is helpful for reducing patients' recovery time, postoperative morbidity and mortality. This study proposes an ensemble model to predict postoperative morbidity after EVAR. The ensemble model was developed using a training set of consecutive patients who underwent EVAR between 2000 and 2009. All data required for prediction modeling, including patient demographics, preoperative, co-morbidities, and complication as outcome variables, was collected prospectively and entered into a clinical database. A discretization approach was used to categorize numerical values into informative feature space. Then, the Bayesian network (BN), artificial neural network (ANN), and support vector machine (SVM) were adopted as base models, and stacking combined multiple models. The research outcomes consisted of an ensemble model to predict postoperative morbidity after EVAR, the occurrence of postoperative complications prospectively recorded, and the causal effect knowledge by BNs with Markov blanket concept.

QML-AiNet: An immune network approach to learning qualitative differential equation models

PubMed Central

Pang, Wei; Coghill, George M.

2015-01-01

In this paper, we explore the application of Opt-AiNet, an immune network approach for search and optimisation problems, to learning qualitative models in the form of qualitative differential equations. The Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed system QML-AiNet. The potential of QML-AiNet to address the scalability and multimodal search space issues of qualitative model learning has been investigated. More importantly, to further improve the efficiency of QML-AiNet, we also modify the mutation operator according to the features of discrete qualitative model space. Experimental results show that the performance of QML-AiNet is comparable to QML-CLONALG, a QML system using the clonal selection algorithm (CLONALG). More importantly, QML-AiNet with the modified mutation operator can significantly improve the scalability of QML and is much more efficient than QML-CLONALG. PMID:25648212

QML-AiNet: An immune network approach to learning qualitative differential equation models.

PubMed

Pang, Wei; Coghill, George M

2015-02-01

In this paper, we explore the application of Opt-AiNet, an immune network approach for search and optimisation problems, to learning qualitative models in the form of qualitative differential equations. The Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed system QML-AiNet. The potential of QML-AiNet to address the scalability and multimodal search space issues of qualitative model learning has been investigated. More importantly, to further improve the efficiency of QML-AiNet, we also modify the mutation operator according to the features of discrete qualitative model space. Experimental results show that the performance of QML-AiNet is comparable to QML-CLONALG, a QML system using the clonal selection algorithm (CLONALG). More importantly, QML-AiNet with the modified mutation operator can significantly improve the scalability of QML and is much more efficient than QML-CLONALG.

Hybrid stochastic simplifications for multiscale gene networks.

PubMed

Crudu, Alina; Debussche, Arnaud; Radulescu, Ovidiu

2009-09-07

Stochastic simulation of gene networks by Markov processes has important applications in molecular biology. The complexity of exact simulation algorithms scales with the number of discrete jumps to be performed. Approximate schemes reduce the computational time by reducing the number of simulated discrete events. Also, answering important questions about the relation between network topology and intrinsic noise generation and propagation should be based on general mathematical results. These general results are difficult to obtain for exact models. We propose a unified framework for hybrid simplifications of Markov models of multiscale stochastic gene networks dynamics. We discuss several possible hybrid simplifications, and provide algorithms to obtain them from pure jump processes. In hybrid simplifications, some components are discrete and evolve by jumps, while other components are continuous. Hybrid simplifications are obtained by partial Kramers-Moyal expansion [1-3] which is equivalent to the application of the central limit theorem to a sub-model. By averaging and variable aggregation we drastically reduce simulation time and eliminate non-critical reactions. Hybrid and averaged simplifications can be used for more effective simulation algorithms and for obtaining general design principles relating noise to topology and time scales. The simplified models reproduce with good accuracy the stochastic properties of the gene networks, including waiting times in intermittence phenomena, fluctuation amplitudes and stationary distributions. The methods are illustrated on several gene network examples. Hybrid simplifications can be used for onion-like (multi-layered) approaches to multi-scale biochemical systems, in which various descriptions are used at various scales. Sets of discrete and continuous variables are treated with different methods and are coupled together in a physically justified approach.

Automatic detection of anomalies in screening mammograms

PubMed Central

2013-01-01

Background Diagnostic performance in breast screening programs may be influenced by the prior probability of disease. Since breast cancer incidence is roughly half a percent in the general population there is a large probability that the screening exam will be normal. That factor may contribute to false negatives. Screening programs typically exhibit about 83% sensitivity and 91% specificity. This investigation was undertaken to determine if a system could be developed to pre-sort screening-images into normal and suspicious bins based on their likelihood to contain disease. Wavelets were investigated as a method to parse the image data, potentially removing confounding information. The development of a classification system based on features extracted from wavelet transformed mammograms is reported. Methods In the multi-step procedure images were processed using 2D discrete wavelet transforms to create a set of maps at different size scales. Next, statistical features were computed from each map, and a subset of these features was the input for a concerted-effort set of naïve Bayesian classifiers. The classifier network was constructed to calculate the probability that the parent mammography image contained an abnormality. The abnormalities were not identified, nor were they regionalized. The algorithm was tested on two publicly available databases: the Digital Database for Screening Mammography (DDSM) and the Mammographic Images Analysis Society’s database (MIAS). These databases contain radiologist-verified images and feature common abnormalities including: spiculations, masses, geometric deformations and fibroid tissues. Results The classifier-network designs tested achieved sensitivities and specificities sufficient to be potentially useful in a clinical setting. This first series of tests identified networks with 100% sensitivity and up to 79% specificity for abnormalities. This performance significantly exceeds the mean sensitivity reported in literature for the unaided human expert. Conclusions Classifiers based on wavelet-derived features proved to be highly sensitive to a range of pathologies, as a result Type II errors were nearly eliminated. Pre-sorting the images changed the prior probability in the sorted database from 37% to 74%. PMID:24330643

A Noachian/Hesperian Hiatus and Erosive Reactivation of Martian Valley Networks

NASA Technical Reports Server (NTRS)

Irwin, R. P., III.; Maxwell, T. A.; Howard, A. D.; Craddock, R. A.; Moore, J. M.

2005-01-01

Despite new evidence for persistent flow and sedimentation on early Mars, it remains unclear whether valley networks were active over long geologic timescales (10(exp 5)-10(exp 8) yr), or if flows were persistent only during multiple discrete episodes of moderate (approx. 10(exp 4) yr) to short (<10 yr) duration. Understanding the long-term stability/variability of valley network hydrology would provide an important control on paleoclimate and groundwater models. Here we describe geologic evidence for a hiatus in highland valley network activity while the fretted terrain formed, followed by a discrete reactivation of persistent (but possibly variable) erosive flows. Additional information is included in the original extended abstract.

A computational approach to extinction events in chemical reaction networks with discrete state spaces.

PubMed

Johnston, Matthew D

2017-12-01

Recent work of Johnston et al. has produced sufficient conditions on the structure of a chemical reaction network which guarantee that the corresponding discrete state space system exhibits an extinction event. The conditions consist of a series of systems of equalities and inequalities on the edges of a modified reaction network called a domination-expanded reaction network. In this paper, we present a computational implementation of these conditions written in Python and apply the program on examples drawn from the biochemical literature. We also run the program on 458 models from the European Bioinformatics Institute's BioModels Database and report our results. Copyright © 2017 Elsevier Inc. All rights reserved.

Complex Quantum Network Manifolds in Dimension d > 2 are Scale-Free

NASA Astrophysics Data System (ADS)

Bianconi, Ginestra; Rahmede, Christoph

2015-09-01

In quantum gravity, several approaches have been proposed until now for the quantum description of discrete geometries. These theoretical frameworks include loop quantum gravity, causal dynamical triangulations, causal sets, quantum graphity, and energetic spin networks. Most of these approaches describe discrete spaces as homogeneous network manifolds. Here we define Complex Quantum Network Manifolds (CQNM) describing the evolution of quantum network states, and constructed from growing simplicial complexes of dimension . We show that in d = 2 CQNM are homogeneous networks while for d > 2 they are scale-free i.e. they are characterized by large inhomogeneities of degrees like most complex networks. From the self-organized evolution of CQNM quantum statistics emerge spontaneously. Here we define the generalized degrees associated with the -faces of the -dimensional CQNMs, and we show that the statistics of these generalized degrees can either follow Fermi-Dirac, Boltzmann or Bose-Einstein distributions depending on the dimension of the -faces.

Geometry segmentation of voxelized representations of heterogeneous microstructures using betweenness centrality

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yuan, Rui; Singh, Sudhanshu S.; Chawla, Nikhilesh

2016-08-15

We present a robust method for automating removal of “segregation artifacts” in segmented tomographic images of three-dimensional heterogeneous microstructures. The objective of this method is to accurately identify and separate discrete features in composite materials where limitations in imaging resolution lead to spurious connections near close contacts. The method utilizes betweenness centrality, a measure of the importance of a node in the connectivity of a graph network, to identify voxels that create artificial bridges between otherwise distinct geometric features. To facilitate automation of the algorithm, we develop a relative centrality metric to allow for the selection of a threshold criterionmore » that is not sensitive to inclusion size or shape. As a demonstration of the effectiveness of the algorithm, we report on the segmentation of a 3D reconstruction of a SiC particle reinforced aluminum alloy, imaged by X-ray synchrotron tomography.« less

Laban Movement Analysis towards Behavior Patterns

NASA Astrophysics Data System (ADS)

Santos, Luís; Dias, Jorge

This work presents a study about the use of Laban Movement Analysis (LMA) as a robust tool to describe human basic behavior patterns, to be applied in human-machine interaction. LMA is a language used to describe and annotate dancing movements and is divided in components [1]: Body, Space, Shape and Effort. Despite its general framework is widely used in physical and mental therapy [2], it has found little application in the engineering domain. Rett J. [3] proposed to implement LMA using Bayesian Networks. However LMA component models have not yet been fully implemented. A study on how to approach behavior using LMA is presented. Behavior is a complex feature and movement chain, but we believe that most basic behavior primitives can be discretized in simple features. Correctly identifying Laban parameters and the movements the authors feel that good patterns can be found within a specific set of basic behavior semantics.

Observer-Based Discrete-Time Nonnegative Edge Synchronization of Networked Systems.

PubMed

Su, Housheng; Wu, Han; Chen, Xia

2017-10-01

This paper studies the multi-input and multi-output discrete-time nonnegative edge synchronization of networked systems based on neighbors' output information. The communication relationship among the edges of networked systems is modeled by well-known line graph. Two observer-based edge synchronization algorithms are designed, for which some necessary and sufficient synchronization conditions are derived. Moreover, some computable sufficient synchronization conditions are obtained, in which the feedback matrix and the observer matrix are computed by solving the linear programming problems. We finally design several simulation examples to demonstrate the validity of the given nonnegative edge synchronization algorithms.

LMI-based approach to stability analysis for fractional-order neural networks with discrete and distributed delays

NASA Astrophysics Data System (ADS)

Zhang, Hai; Ye, Renyu; Liu, Song; Cao, Jinde; Alsaedi, Ahmad; Li, Xiaodi

2018-02-01

This paper is concerned with the asymptotic stability of the Riemann-Liouville fractional-order neural networks with discrete and distributed delays. By constructing a suitable Lyapunov functional, two sufficient conditions are derived to ensure that the addressed neural network is asymptotically stable. The presented stability criteria are described in terms of the linear matrix inequalities. The advantage of the proposed method is that one may avoid calculating the fractional-order derivative of the Lyapunov functional. Finally, a numerical example is given to show the validity and feasibility of the theoretical results.

Improved result on stability analysis of discrete stochastic neural networks with time delay

NASA Astrophysics Data System (ADS)

Wu, Zhengguang; Su, Hongye; Chu, Jian; Zhou, Wuneng

2009-04-01

This Letter investigates the problem of exponential stability for discrete stochastic time-delay neural networks. By defining a novel Lyapunov functional, an improved delay-dependent exponential stability criterion is established in terms of linear matrix inequality (LMI) approach. Meanwhile, the computational complexity of the newly established stability condition is reduced because less variables are involved. Numerical example is given to illustrate the effectiveness and the benefits of the proposed method.

Discrete Biogeography Based Optimization for Feature Selection in Molecular Signatures.

PubMed

Liu, Bo; Tian, Meihong; Zhang, Chunhua; Li, Xiangtao

2015-04-01

Biomarker discovery from high-dimensional data is a complex task in the development of efficient cancer diagnoses and classification. However, these data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in the field of bioinformatics. In this paper, a discrete biogeography based optimization is proposed to select the good subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the fisher-markov selector is used to choose fixed number of gene data. Secondly, to make biogeography based optimization suitable for the feature selection problem; discrete migration model and discrete mutation model are proposed to balance the exploration and exploitation ability. Then, discrete biogeography based optimization, as we called DBBO, is proposed by integrating discrete migration model and discrete mutation model. Finally, the DBBO method is used for feature selection, and three classifiers are used as the classifier with the 10 fold cross-validation method. In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on four breast cancer dataset benchmarks. Comparison with genetic algorithm, particle swarm optimization, differential evolution algorithm and hybrid biogeography based optimization, experimental results demonstrate that the proposed method is better or at least comparable with previous method from literature when considering the quality of the solutions obtained. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Autonomous control of production networks using a pheromone approach

NASA Astrophysics Data System (ADS)

Armbruster, D.; de Beer, C.; Freitag, M.; Jagalski, T.; Ringhofer, C.

2006-04-01

The flow of parts through a production network is usually pre-planned by a central control system. Such central control fails in presence of highly fluctuating demand and/or unforeseen disturbances. To manage such dynamic networks according to low work-in-progress and short throughput times, an autonomous control approach is proposed. Autonomous control means a decentralized routing of the autonomous parts themselves. The parts’ decisions base on backward propagated information about the throughput times of finished parts for different routes. So, routes with shorter throughput times attract parts to use this route again. This process can be compared to ants leaving pheromones on their way to communicate with following ants. The paper focuses on a mathematical description of such autonomously controlled production networks. A fluid model with limited service rates in a general network topology is derived and compared to a discrete-event simulation model. Whereas the discrete-event simulation of production networks is straightforward, the formulation of the addressed scenario in terms of a fluid model is challenging. Here it is shown, how several problems in a fluid model formulation (e.g. discontinuities) can be handled mathematically. Finally, some simulation results for the pheromone-based control with both the discrete-event simulation model and the fluid model are presented for a time-dependent influx.

Discrete-time neural network for fast solving large linear L1 estimation problems and its application to image restoration.

PubMed

Xia, Youshen; Sun, Changyin; Zheng, Wei Xing

2012-05-01

There is growing interest in solving linear L1 estimation problems for sparsity of the solution and robustness against non-Gaussian noise. This paper proposes a discrete-time neural network which can calculate large linear L1 estimation problems fast. The proposed neural network has a fixed computational step length and is proved to be globally convergent to an optimal solution. Then, the proposed neural network is efficiently applied to image restoration. Numerical results show that the proposed neural network is not only efficient in solving degenerate problems resulting from the nonunique solutions of the linear L1 estimation problems but also needs much less computational time than the related algorithms in solving both linear L1 estimation and image restoration problems.

Nonlinear Maps for Design of Discrete Time Models of Neuronal Network Dynamics

DTIC Science & Technology

2016-02-29

Performance/Technic~ 02-01-2016- 02-29-2016 4. TITLE AND SUBTITLE Sa. CONTRACT NUMBER Nonlinear Maps for Design of Discrete -Time Models of Neuronal...neuronal model in the form of difference equations that generates neuronal states in discrete moments of time. In this approach, time step can be made...propose to use modern DSP ideas to develop new efficient approaches to the design of such discrete -time models for studies of large-scale neuronal

Nonlinear Maps for Design of Discrete-Time Models of Neuronal Network Dynamics

DTIC Science & Technology

2016-03-31

2016 Performance/Technic~ 03-01-2016- 03-31-2016 4. TITLE AND SUBTITLE Sa. CONTRACT NUMBER Nonlinear Maps for Design of Discrete -Time Models of...simulations is to design a neuronal model in the form of difference equations that generates neuronal states in discrete moments of time. In this...responsive tiring patterns. We propose to use modern DSP ideas to develop new efficient approaches to the design of such discrete -time models for

sEMG feature evaluation for identification of elbow angle resolution in graded arm movement.

PubMed

Castro, Maria Claudia F; Colombini, Esther L; Aquino, Plinio T; Arjunan, Sridhar P; Kumar, Dinesh K

2014-11-25

Automatic and accurate identification of elbow angle from surface electromyogram (sEMG) is essential for myoelectric controlled upper limb exoskeleton systems. This requires appropriate selection of sEMG features, and identifying the limitations of such a system.This study has demonstrated that it is possible to identify three discrete positions of the elbow; full extension, right angle, and mid-way point, with window size of only 200 milliseconds. It was seen that while most features were suitable for this purpose, Power Spectral Density Averages (PSD-Av) performed best. The system correctly classified the sEMG against the elbow angle for 100% cases when only two discrete positions (full extension and elbow at right angle) were considered, while correct classification was 89% when there were three discrete positions. However, sEMG was unable to accurately determine the elbow position when five discrete angles were considered. It was also observed that there was no difference for extension or flexion phases.

Cellular Signaling Networks Function as Generalized Wiener-Kolmogorov Filters to Suppress Noise

NASA Astrophysics Data System (ADS)

Hinczewski, Michael; Thirumalai, D.

2014-10-01

Cellular signaling involves the transmission of environmental information through cascades of stochastic biochemical reactions, inevitably introducing noise that compromises signal fidelity. Each stage of the cascade often takes the form of a kinase-phosphatase push-pull network, a basic unit of signaling pathways whose malfunction is linked with a host of cancers. We show that this ubiquitous enzymatic network motif effectively behaves as a Wiener-Kolmogorov optimal noise filter. Using concepts from umbral calculus, we generalize the linear Wiener-Kolmogorov theory, originally introduced in the context of communication and control engineering, to take nonlinear signal transduction and discrete molecule populations into account. This allows us to derive rigorous constraints for efficient noise reduction in this biochemical system. Our mathematical formalism yields bounds on filter performance in cases important to cellular function—such as ultrasensitive response to stimuli. We highlight features of the system relevant for optimizing filter efficiency, encoded in a single, measurable, dimensionless parameter. Our theory, which describes noise control in a large class of signal transduction networks, is also useful both for the design of synthetic biochemical signaling pathways and the manipulation of pathways through experimental probes such as oscillatory input.

Generation of Complex Karstic Conduit Networks with a Hydro-chemical Model

NASA Astrophysics Data System (ADS)

De Rooij, R.; Graham, W. D.

2016-12-01

The discrete-continuum approach is very well suited to simulate flow and solute transport within karst aquifers. Using this approach, discrete one-dimensional conduits are embedded within a three-dimensional continuum representative of the porous limestone matrix. Typically, however, little is known about the geometry of the karstic conduit network. As such the discrete-continuum approach is rarely used for practical applications. It may be argued, however, that the uncertainty associated with the geometry of the network could be handled by modeling an ensemble of possible karst conduit networks within a stochastic framework. We propose to generate stochastically realistic karst conduit networks by simulating the widening of conduits as caused by the dissolution of limestone over geological relevant timescales. We illustrate that advanced numerical techniques permit to solve the non-linear and coupled hydro-chemical processes efficiently, such that relatively large and complex networks can be generated in acceptable time frames. Instead of specifying flow boundary conditions on conduit cells to recharge the network as is typically done in classical speleogenesis models, we specify an effective rainfall rate over the land surface and let model physics determine the amount of water entering the network. This is advantageous since the amount of water entering the network is extremely difficult to reconstruct, whereas the effective rainfall rate may be quantified using paleoclimatic data. Furthermore, we show that poorly known flow conditions may be constrained by requiring a realistic flow field. Using our speleogenesis model we have investigated factors that influence the geometry of simulated conduit networks. We illustrate that our model generates typical branchwork, network and anastomotic conduit systems. Flow, solute transport and water ages in karst aquifers are simulated using a few illustrative networks.

Hybrid discrete/continuum algorithms for stochastic reaction networks

DOE PAGES

Safta, Cosmin; Sargsyan, Khachik; Debusschere, Bert; ...

2014-10-22

Direct solutions of the Chemical Master Equation (CME) governing Stochastic Reaction Networks (SRNs) are generally prohibitively expensive due to excessive numbers of possible discrete states in such systems. To enhance computational efficiency we develop a hybrid approach where the evolution of states with low molecule counts is treated with the discrete CME model while that of states with large molecule counts is modeled by the continuum Fokker-Planck equation. The Fokker-Planck equation is discretized using a 2nd order finite volume approach with appropriate treatment of flux components to avoid negative probability values. The numerical construction at the interface between the discretemore » and continuum regions implements the transfer of probability reaction by reaction according to the stoichiometry of the system. As a result, the performance of this novel hybrid approach is explored for a two-species circadian model with computational efficiency gains of about one order of magnitude.« less

Hybrid stochastic simplifications for multiscale gene networks

PubMed Central

Crudu, Alina; Debussche, Arnaud; Radulescu, Ovidiu

2009-01-01

Background Stochastic simulation of gene networks by Markov processes has important applications in molecular biology. The complexity of exact simulation algorithms scales with the number of discrete jumps to be performed. Approximate schemes reduce the computational time by reducing the number of simulated discrete events. Also, answering important questions about the relation between network topology and intrinsic noise generation and propagation should be based on general mathematical results. These general results are difficult to obtain for exact models. Results We propose a unified framework for hybrid simplifications of Markov models of multiscale stochastic gene networks dynamics. We discuss several possible hybrid simplifications, and provide algorithms to obtain them from pure jump processes. In hybrid simplifications, some components are discrete and evolve by jumps, while other components are continuous. Hybrid simplifications are obtained by partial Kramers-Moyal expansion [1-3] which is equivalent to the application of the central limit theorem to a sub-model. By averaging and variable aggregation we drastically reduce simulation time and eliminate non-critical reactions. Hybrid and averaged simplifications can be used for more effective simulation algorithms and for obtaining general design principles relating noise to topology and time scales. The simplified models reproduce with good accuracy the stochastic properties of the gene networks, including waiting times in intermittence phenomena, fluctuation amplitudes and stationary distributions. The methods are illustrated on several gene network examples. Conclusion Hybrid simplifications can be used for onion-like (multi-layered) approaches to multi-scale biochemical systems, in which various descriptions are used at various scales. Sets of discrete and continuous variables are treated with different methods and are coupled together in a physically justified approach. PMID:19735554

Boolean dynamics of genetic regulatory networks inferred from microarray time series data

DOE PAGES

Martin, Shawn; Zhang, Zhaoduo; Martino, Anthony; ...

2007-01-31

Methods available for the inference of genetic regulatory networks strive to produce a single network, usually by optimizing some quantity to fit the experimental observations. In this paper we investigate the possibility that multiple networks can be inferred, all resulting in similar dynamics. This idea is motivated by theoretical work which suggests that biological networks are robust and adaptable to change, and that the overall behavior of a genetic regulatory network might be captured in terms of dynamical basins of attraction. We have developed and implemented a method for inferring genetic regulatory networks for time series microarray data. Our methodmore » first clusters and discretizes the gene expression data using k-means and support vector regression. We then enumerate Boolean activation–inhibition networks to match the discretized data. In conclusion, the dynamics of the Boolean networks are examined. We have tested our method on two immunology microarray datasets: an IL-2-stimulated T cell response dataset and a LPS-stimulated macrophage response dataset. In both cases, we discovered that many networks matched the data, and that most of these networks had similar dynamics.« less

An LMI approach to design H(infinity) controllers for discrete-time nonlinear systems based on unified models.

PubMed

Liu, Meiqin; Zhang, Senlin

2008-10-01

A unified neural network model termed standard neural network model (SNNM) is advanced. Based on the robust L(2) gain (i.e. robust H(infinity) performance) analysis of the SNNM with external disturbances, a state-feedback control law is designed for the SNNM to stabilize the closed-loop system and eliminate the effect of external disturbances. The control design constraints are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms (e.g. interior-point algorithms) to determine the control law. Most discrete-time recurrent neural network (RNNs) and discrete-time nonlinear systems modelled by neural networks or Takagi and Sugeno (T-S) fuzzy models can be transformed into the SNNMs to be robust H(infinity) performance analyzed or robust H(infinity) controller synthesized in a unified SNNM's framework. Finally, some examples are presented to illustrate the wide application of the SNNMs to the nonlinear systems, and the proposed approach is compared with related methods reported in the literature.

Toric Networks, Geometric R-Matrices and Generalized Discrete Toda Lattices

NASA Astrophysics Data System (ADS)

Inoue, Rei; Lam, Thomas; Pylyavskyy, Pavlo

2016-11-01

We use the combinatorics of toric networks and the double affine geometric R-matrix to define a three-parameter family of generalizations of the discrete Toda lattice. We construct the integrals of motion and a spectral map for this system. The family of commuting time evolutions arising from the action of the R-matrix is explicitly linearized on the Jacobian of the spectral curve. The solution to the initial value problem is constructed using Riemann theta functions.

The application of feature selection to the development of Gaussian process models for percutaneous absorption.

PubMed

Lam, Lun Tak; Sun, Yi; Davey, Neil; Adams, Rod; Prapopoulou, Maria; Brown, Marc B; Moss, Gary P

2010-06-01

The aim was to employ Gaussian processes to assess mathematically the nature of a skin permeability dataset and to employ these methods, particularly feature selection, to determine the key physicochemical descriptors which exert the most significant influence on percutaneous absorption, and to compare such models with established existing models. Gaussian processes, including automatic relevance detection (GPRARD) methods, were employed to develop models of percutaneous absorption that identified key physicochemical descriptors of percutaneous absorption. Using MatLab software, the statistical performance of these models was compared with single linear networks (SLN) and quantitative structure-permeability relationships (QSPRs). Feature selection methods were used to examine in more detail the physicochemical parameters used in this study. A range of statistical measures to determine model quality were used. The inherently nonlinear nature of the skin data set was confirmed. The Gaussian process regression (GPR) methods yielded predictive models that offered statistically significant improvements over SLN and QSPR models with regard to predictivity (where the rank order was: GPR > SLN > QSPR). Feature selection analysis determined that the best GPR models were those that contained log P, melting point and the number of hydrogen bond donor groups as significant descriptors. Further statistical analysis also found that great synergy existed between certain parameters. It suggested that a number of the descriptors employed were effectively interchangeable, thus questioning the use of models where discrete variables are output, usually in the form of an equation. The use of a nonlinear GPR method produced models with significantly improved predictivity, compared with SLN or QSPR models. Feature selection methods were able to provide important mechanistic information. However, it was also shown that significant synergy existed between certain parameters, and as such it was possible to interchange certain descriptors (i.e. molecular weight and melting point) without incurring a loss of model quality. Such synergy suggested that a model constructed from discrete terms in an equation may not be the most appropriate way of representing mechanistic understandings of skin absorption.

Event-based simulation of networks with pulse delayed coupling

NASA Astrophysics Data System (ADS)

Klinshov, Vladimir; Nekorkin, Vladimir

2017-10-01

Pulse-mediated interactions are common in networks of different nature. Here we develop a general framework for simulation of networks with pulse delayed coupling. We introduce the discrete map governing the dynamics of such networks and describe the computation algorithm for its numerical simulation.

Pinning impulsive control algorithms for complex network

NASA Astrophysics Data System (ADS)

Sun, Wen; Lü, Jinhu; Chen, Shihua; Yu, Xinghuo

2014-03-01

In this paper, we further investigate the synchronization of complex dynamical network via pinning control in which a selection of nodes are controlled at discrete times. Different from most existing work, the pinning control algorithms utilize only the impulsive signals at discrete time instants, which may greatly improve the communication channel efficiency and reduce control cost. Two classes of algorithms are designed, one for strongly connected complex network and another for non-strongly connected complex network. It is suggested that in the strongly connected network with suitable coupling strength, a single controller at any one of the network's nodes can always pin the network to its homogeneous solution. In the non-strongly connected case, the location and minimum number of nodes needed to pin the network are determined by the Frobenius normal form of the coupling matrix. In addition, the coupling matrix is not necessarily symmetric or irreducible. Illustrative examples are then given to validate the proposed pinning impulsive control algorithms.

Implementation of neuromorphic systems: from discrete components to analog VLSI chips (testing and communication issues).

PubMed

Dante, V; Del Giudice, P; Mattia, M

2001-01-01

We review a series of implementations of electronic devices aiming at imitating to some extent structure and function of simple neural systems, with particular emphasis on communication issues. We first provide a short overview of general features of such "neuromorphic" devices and the implications of setting up "tests" for them. We then review the developments directly related to our work at the Istituto Superiore di Sanità (ISS): a pilot electronic neural network implementing a simple classifier, autonomously developing internal representations of incoming stimuli; an output network, collecting information from the previous classifier and extracting the relevant part to be forwarded to the observer; an analog, VLSI (very large scale integration) neural chip implementing a recurrent network of spiking neurons and plastic synapses, and the test setup for it; a board designed to interface the standard PCI (peripheral component interconnect) bus of a PC with a special purpose, asynchronous bus for communication among neuromorphic chips; a short and preliminary account of an application-oriented device, taking advantage of the above communication infrastructure.

Data approximation using a blending type spline construction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dalmo, Rune; Bratlie, Jostein

2014-11-18

Generalized expo-rational B-splines (GERBS) is a blending type spline construction where local functions at each knot are blended together by C{sup k}-smooth basis functions. One way of approximating discrete regular data using GERBS is by partitioning the data set into subsets and fit a local function to each subset. Partitioning and fitting strategies can be devised such that important or interesting data points are interpolated in order to preserve certain features. We present a method for fitting discrete data using a tensor product GERBS construction. The method is based on detection of feature points using differential geometry. Derivatives, which aremore » necessary for feature point detection and used to construct local surface patches, are approximated from the discrete data using finite differences.« less

A toolbox for discrete modelling of cell signalling dynamics.

PubMed

Paterson, Yasmin Z; Shorthouse, David; Pleijzier, Markus W; Piterman, Nir; Bendtsen, Claus; Hall, Benjamin A; Fisher, Jasmin

2018-06-18

In an age where the volume of data regarding biological systems exceeds our ability to analyse it, many researchers are looking towards systems biology and computational modelling to help unravel the complexities of gene and protein regulatory networks. In particular, the use of discrete modelling allows generation of signalling networks in the absence of full quantitative descriptions of systems, which are necessary for ordinary differential equation (ODE) models. In order to make such techniques more accessible to mainstream researchers, tools such as the BioModelAnalyzer (BMA) have been developed to provide a user-friendly graphical interface for discrete modelling of biological systems. Here we use the BMA to build a library of discrete target functions of known canonical molecular interactions, translated from ordinary differential equations (ODEs). We then show that these BMA target functions can be used to reconstruct complex networks, which can correctly predict many known genetic perturbations. This new library supports the accessibility ethos behind the creation of BMA, providing a toolbox for the construction of complex cell signalling models without the need for extensive experience in computer programming or mathematical modelling, and allows for construction and simulation of complex biological systems with only small amounts of quantitative data.

Discrete Particle Swarm Optimization Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks.

PubMed

Yang, Jin; Liu, Fagui; Cao, Jianneng; Wang, Liangming

2016-07-14

Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle's position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption.

Parallel discrete event simulation: A shared memory approach

NASA Technical Reports Server (NTRS)

Reed, Daniel A.; Malony, Allen D.; Mccredie, Bradley D.

1987-01-01

With traditional event list techniques, evaluating a detailed discrete event simulation model can often require hours or even days of computation time. Parallel simulation mimics the interacting servers and queues of a real system by assigning each simulated entity to a processor. By eliminating the event list and maintaining only sufficient synchronization to insure causality, parallel simulation can potentially provide speedups that are linear in the number of processors. A set of shared memory experiments is presented using the Chandy-Misra distributed simulation algorithm to simulate networks of queues. Parameters include queueing network topology and routing probabilities, number of processors, and assignment of network nodes to processors. These experiments show that Chandy-Misra distributed simulation is a questionable alternative to sequential simulation of most queueing network models.

Integrated Circuit Chip Improves Network Efficiency

NASA Technical Reports Server (NTRS)

2008-01-01

Prior to 1999 and the development of SpaceWire, a standard for high-speed links for computer networks managed by the European Space Agency (ESA), there was no high-speed communications protocol for flight electronics. Onboard computers, processing units, and other electronics had to be designed for individual projects and then redesigned for subsequent projects, which increased development periods, costs, and risks. After adopting the SpaceWire protocol in 2000, NASA implemented the standard on the Swift mission, a gamma ray burst-alert telescope launched in November 2004. Scientists and developers on the James Webb Space Telescope further developed the network version of SpaceWire. In essence, SpaceWire enables more science missions at a lower cost, because it provides a standard interface between flight electronics components; new systems need not be custom built to accommodate individual missions, so electronics can be reused. New protocols are helping to standardize higher layers of computer communication. Goddard Space Flight Center improved on the ESA-developed SpaceWire by enabling standard protocols, which included defining quality of service and supporting plug-and-play capabilities. Goddard upgraded SpaceWire to make the routers more efficient and reliable, with features including redundant cables, simultaneous discrete broadcast pulses, prevention of network blockage, and improved verification. Redundant cables simplify management because the user does not need to worry about which connection is available, and simultaneous broadcast signals allow multiple users to broadcast low-latency side-band signal pulses across the network using the same resources for data communication. Additional features have been added to the SpaceWire switch to prevent network blockage so that more robust networks can be designed. Goddard s verification environment for the link-and-switch implementation continuously randomizes and tests different parts, constantly anticipating situations, which helps improve communications reliability. It has been tested in many different implementations for compatibility.

Self-Organization of Vocabularies under Different Interaction Orders.

PubMed

Vera, Javier

2017-01-01

Traditionally, the formation of vocabularies has been studied by agent-based models (primarily, the naming game) in which random pairs of agents negotiate word-meaning associations at each discrete time step. This article proposes a first approximation to a novel question: To what extent is the negotiation of word-meaning associations influenced by the order in which agents interact? Automata networks provide the adequate mathematical framework to explore this question. Computer simulations suggest that on two-dimensional lattices the typical features of the formation of word-meaning associations are recovered under random schemes that update small fractions of the population at the same time; by contrast, if larger subsets of the population are updated, a periodic behavior may appear.

Automated Age-related Macular Degeneration screening system using fundus images.

PubMed

Kunumpol, P; Umpaipant, W; Kanchanaranya, N; Charoenpong, T; Vongkittirux, S; Kupakanjana, T; Tantibundhit, C

2017-07-01

This work proposed an automated screening system for Age-related Macular Degeneration (AMD), and distinguishing between wet or dry types of AMD using fundus images to assist ophthalmologists in eye disease screening and management. The algorithm employs contrast-limited adaptive histogram equalization (CLAHE) in image enhancement. Subsequently, discrete wavelet transform (DWT) and locality sensitivity discrimination analysis (LSDA) were used to extract features for a neural network model to classify the results. The results showed that the proposed algorithm was able to distinguish between normal eyes, dry AMD, or wet AMD with 98.63% sensitivity, 99.15% specificity, and 98.94% accuracy, suggesting promising potential as a medical support system for faster eye disease screening at lower costs.

Adaptive fuzzy leader clustering of complex data sets in pattern recognition

NASA Technical Reports Server (NTRS)

Newton, Scott C.; Pemmaraju, Surya; Mitra, Sunanda

1992-01-01

A modular, unsupervised neural network architecture for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns on-line in a stable and efficient manner. The initial classification is performed in two stages: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from fuzzy C-means system equations for the centroids and the membership values. The AFLC algorithm is applied to the Anderson Iris data and laser-luminescent fingerprint image data. It is concluded that the AFLC algorithm successfully classifies features extracted from real data, discrete or continuous.

Impaired Conflict Resolution and Alerting in Children with ADHD: Evidence from the Attention Network Task (ANT)

ERIC Educational Resources Information Center

Johnson, Katherine A.; Robertson, Ian H.; Barry, Edwina; Mulligan, Aisling; Daibhis, Aoife; Daly, Michael; Watchorn, Amy; Gill, Michael; Bellgrove, Mark A.

2008-01-01

Background: An important theory of attention suggests that there are three separate networks that execute discrete cognitive functions. The "alerting" network acquires and maintains an alert state, the "orienting" network selects information from sensory input and the "conflict" network resolves conflict that arises between potential responses.…

Phenotypic switching in bacteria

NASA Astrophysics Data System (ADS)

Merrin, Jack

Living matter is a non-equilibrium system in which many components work in parallel to perpetuate themselves through a fluctuating environment. Physiological states or functionalities revealed by a particular environment are called phenotypes. Transitions between phenotypes may occur either spontaneously or via interaction with the environment. Even in the same environment, genetically identical bacteria can exhibit different phenotypes of a continuous or discrete nature. In this thesis, we pursued three lines of investigation into discrete phenotypic heterogeneity in bacterial populations: the quantitative characterization of the so-called bacterial persistence, a theoretical model of phenotypic switching based on those measurements, and the design of artificial genetic networks which implement this model. Persistence is the phenotype of a subpopulation of bacteria with a reduced sensitivity to antibiotics. We developed a microfluidic apparatus, which allowed us to monitor the growth rates of individual cells while applying repeated cycles of antibiotic treatments. We were able to identify distinct phenotypes (normal and persistent) and characterize the stochastic transitions between them. We also found that phenotypic heterogeneity was present prior to any environmental cue such as antibiotic exposure. Motivated by the experiments with persisters, we formulated a theoretical model describing the dynamic behavior of several discrete phenotypes in a periodically varying environment. This theoretical framework allowed us to quantitatively predict the fitness of dynamic populations and to compare survival strategies according to environmental time-symmetries. These calculations suggested that persistence is a strategy used by bacterial populations to adapt to fluctuating environments. Knowledge of the phenotypic transition rates for persistence may provide statistical information about the typical environments of bacteria. We also describe a design of artificial genetic networks that would implement a more general theoretical model of phenotypic switching. We will use a new cloning strategy in order to systematically assemble a large number of genetic features, such as site-specific recombination components from the R64 plasmid, which invert several coexisting DNA segments. The inversion of these segments would lead to discrete phenotypic transitions inside a living cell. These artificial phenotypic switches can be controlled precisely in experiments and may serve as a benchmark for their natural counterparts.

Global stabilization analysis of inertial memristive recurrent neural networks with discrete and distributed delays.

PubMed

Wang, Leimin; Zeng, Zhigang; Ge, Ming-Feng; Hu, Junhao

2018-05-02

This paper deals with the stabilization problem of memristive recurrent neural networks with inertial items, discrete delays, bounded and unbounded distributed delays. First, for inertial memristive recurrent neural networks (IMRNNs) with second-order derivatives of states, an appropriate variable substitution method is invoked to transfer IMRNNs into a first-order differential form. Then, based on nonsmooth analysis theory, several algebraic criteria are established for the global stabilizability of IMRNNs under proposed feedback control, where the cases with both bounded and unbounded distributed delays are successfully addressed. Finally, the theoretical results are illustrated via the numerical simulations. Copyright © 2018 Elsevier Ltd. All rights reserved.

Energy Minimization of Discrete Protein Titration State Models Using Graph Theory.

PubMed

Purvine, Emilie; Monson, Kyle; Jurrus, Elizabeth; Star, Keith; Baker, Nathan A

2016-08-25

There are several applications in computational biophysics that require the optimization of discrete interacting states, for example, amino acid titration states, ligand oxidation states, or discrete rotamer angles. Such optimization can be very time-consuming as it scales exponentially in the number of sites to be optimized. In this paper, we describe a new polynomial time algorithm for optimization of discrete states in macromolecular systems. This algorithm was adapted from image processing and uses techniques from discrete mathematics and graph theory to restate the optimization problem in terms of "maximum flow-minimum cut" graph analysis. The interaction energy graph, a graph in which vertices (amino acids) and edges (interactions) are weighted with their respective energies, is transformed into a flow network in which the value of the minimum cut in the network equals the minimum free energy of the protein and the cut itself encodes the state that achieves the minimum free energy. Because of its deterministic nature and polynomial time performance, this algorithm has the potential to allow for the ionization state of larger proteins to be discovered.

Energy Minimization of Discrete Protein Titration State Models Using Graph Theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Purvine, Emilie AH; Monson, Kyle E.; Jurrus, Elizabeth R.

There are several applications in computational biophysics which require the optimization of discrete interacting states; e.g., amino acid titration states, ligand oxidation states, or discrete rotamer angles. Such optimization can be very time-consuming as it scales exponentially in the number of sites to be optimized. In this paper, we describe a new polynomial-time algorithm for optimization of discrete states in macromolecular systems. This algorithm was adapted from image processing and uses techniques from discrete mathematics and graph theory to restate the optimization problem in terms of maximum flow-minimum cut graph analysis. The interaction energy graph, a graph in which verticesmore » (amino acids) and edges (interactions) are weighted with their respective energies, is transformed into a flow network in which the value of the minimum cut in the network equals the minimum free energy of the protein, and the cut itself encodes the state that achieves the minimum free energy. Because of its deterministic nature and polynomial-time performance, this algorithm has the potential to allow for the ionization state of larger proteins to be discovered.« less

Energy Minimization of Discrete Protein Titration State Models Using Graph Theory

PubMed Central

Purvine, Emilie; Monson, Kyle; Jurrus, Elizabeth; Star, Keith; Baker, Nathan A.

2016-01-01

There are several applications in computational biophysics which require the optimization of discrete interacting states; e.g., amino acid titration states, ligand oxidation states, or discrete rotamer angles. Such optimization can be very time-consuming as it scales exponentially in the number of sites to be optimized. In this paper, we describe a new polynomial-time algorithm for optimization of discrete states in macromolecular systems. This algorithm was adapted from image processing and uses techniques from discrete mathematics and graph theory to restate the optimization problem in terms of “maximum flow-minimum cut” graph analysis. The interaction energy graph, a graph in which vertices (amino acids) and edges (interactions) are weighted with their respective energies, is transformed into a flow network in which the value of the minimum cut in the network equals the minimum free energy of the protein, and the cut itself encodes the state that achieves the minimum free energy. Because of its deterministic nature and polynomial-time performance, this algorithm has the potential to allow for the ionization state of larger proteins to be discovered. PMID:27089174

SPEEDES - A multiple-synchronization environment for parallel discrete-event simulation

NASA Technical Reports Server (NTRS)

Steinman, Jeff S.

1992-01-01

Synchronous Parallel Environment for Emulation and Discrete-Event Simulation (SPEEDES) is a unified parallel simulation environment. It supports multiple-synchronization protocols without requiring users to recompile their code. When a SPEEDES simulation runs on one node, all the extra parallel overhead is removed automatically at run time. When the same executable runs in parallel, the user preselects the synchronization algorithm from a list of options. SPEEDES currently runs on UNIX networks and on the California Institute of Technology/Jet Propulsion Laboratory Mark III Hypercube. SPEEDES also supports interactive simulations. Featured in the SPEEDES environment is a new parallel synchronization approach called Breathing Time Buckets. This algorithm uses some of the conservative techniques found in Time Bucket synchronization, along with the optimism that characterizes the Time Warp approach. A mathematical model derived from first principles predicts the performance of Breathing Time Buckets. Along with the Breathing Time Buckets algorithm, this paper discusses the rules for processing events in SPEEDES, describes the implementation of various other synchronization protocols supported by SPEEDES, describes some new ones for the future, discusses interactive simulations, and then gives some performance results.

Encoding dependence in Bayesian causal networks

USDA-ARS?s Scientific Manuscript database

Bayesian networks (BNs) represent complex, uncertain spatio-temporal dynamics by propagation of conditional probabilities between identifiable states with a testable causal interaction model. Typically, they assume random variables are discrete in time and space with a static network structure that ...

Generalized Synchronization in AN Array of Nonlinear Dynamic Systems with Applications to Chaotic Cnn

NASA Astrophysics Data System (ADS)

Min, Lequan; Chen, Guanrong

This paper establishes some generalized synchronization (GS) theorems for a coupled discrete array of difference systems (CDADS) and a coupled continuous array of differential systems (CCADS). These constructive theorems provide general representations of GS in CDADS and CCADS. Based on these theorems, one can design GS-driven CDADS and CCADS via appropriate (invertible) transformations. As applications, the results are applied to autonomous and nonautonomous coupled Chen cellular neural network (CNN) CDADS and CCADS, discrete bidirectional Lorenz CNN CDADS, nonautonomous bidirectional Chua CNN CCADS, and nonautonomously bidirectional Chen CNN CDADS and CCADS, respectively. Extensive numerical simulations show their complex dynamic behaviors. These theorems provide new means for understanding the GS phenomena of complex discrete and continuously differentiable networks.

Computational complexity of Boolean functions

NASA Astrophysics Data System (ADS)

Korshunov, Aleksei D.

2012-02-01

Boolean functions are among the fundamental objects of discrete mathematics, especially in those of its subdisciplines which fall under mathematical logic and mathematical cybernetics. The language of Boolean functions is convenient for describing the operation of many discrete systems such as contact networks, Boolean circuits, branching programs, and some others. An important parameter of discrete systems of this kind is their complexity. This characteristic has been actively investigated starting from Shannon's works. There is a large body of scientific literature presenting many fundamental results. The purpose of this survey is to give an account of the main results over the last sixty years related to the complexity of computation (realization) of Boolean functions by contact networks, Boolean circuits, and Boolean circuits without branching. Bibliography: 165 titles.

On the deep structure of social affect: Attitudes, emotions, sentiments, and the case of "contempt".

PubMed

Gervais, Matthew M; Fessler, Daniel M T

2017-01-01

Contempt is typically studied as a uniquely human moral emotion. However, this approach has yielded inconclusive results. We argue this is because the folk affect concept "contempt" has been inaccurately mapped onto basic affect systems. "Contempt" has features that are inconsistent with a basic emotion, especially its protracted duration and frequently cold phenomenology. Yet other features are inconsistent with a basic attitude. Nonetheless, the features of "contempt" functionally cohere. To account for this, we revive and reconfigure the sentiment construct using the notion of evolved functional specialization. We develop the Attitude-Scenario-Emotion (ASE) model of sentiments, in which enduring attitudes represent others' social-relational value and moderate discrete emotions across scenarios. Sentiments are functional networks of attitudes and emotions. Distinct sentiments, including love, respect, like, hate, and fear, track distinct relational affordances, and each is emotionally pluripotent, thereby serving both bookkeeping and commitment functions within relationships. The sentiment contempt is an absence of respect; from cues to others' low efficacy, it represents them as worthless and small, muting compassion, guilt, and shame and potentiating anger, disgust, and mirth. This sentiment is ancient yet implicated in the ratcheting evolution of human ultrasocialty. The manifolds of the contempt network, differentially engaged across individuals and populations, explain the features of "contempt," its translatability, and its variable experience as "hot" or "cold," occurrent or enduring, and anger-like or disgust-like. This rapprochement between psychological anthropology and evolutionary psychology contributes both methodological and empirical insights, with broad implications for understanding the functional and cultural organization of social affect.

Probabilistic inference using linear Gaussian importance sampling for hybrid Bayesian networks

NASA Astrophysics Data System (ADS)

Sun, Wei; Chang, K. C.

2005-05-01

Probabilistic inference for Bayesian networks is in general NP-hard using either exact algorithms or approximate methods. However, for very complex networks, only the approximate methods such as stochastic sampling could be used to provide a solution given any time constraint. There are several simulation methods currently available. They include logic sampling (the first proposed stochastic method for Bayesian networks, the likelihood weighting algorithm) the most commonly used simulation method because of its simplicity and efficiency, the Markov blanket scoring method, and the importance sampling algorithm. In this paper, we first briefly review and compare these available simulation methods, then we propose an improved importance sampling algorithm called linear Gaussian importance sampling algorithm for general hybrid model (LGIS). LGIS is aimed for hybrid Bayesian networks consisting of both discrete and continuous random variables with arbitrary distributions. It uses linear function and Gaussian additive noise to approximate the true conditional probability distribution for continuous variable given both its parents and evidence in a Bayesian network. One of the most important features of the newly developed method is that it can adaptively learn the optimal important function from the previous samples. We test the inference performance of LGIS using a 16-node linear Gaussian model and a 6-node general hybrid model. The performance comparison with other well-known methods such as Junction tree (JT) and likelihood weighting (LW) shows that LGIS-GHM is very promising.

Complex Quantum Network Manifolds in Dimension d > 2 are Scale-Free

PubMed Central

Bianconi, Ginestra; Rahmede, Christoph

2015-01-01

In quantum gravity, several approaches have been proposed until now for the quantum description of discrete geometries. These theoretical frameworks include loop quantum gravity, causal dynamical triangulations, causal sets, quantum graphity, and energetic spin networks. Most of these approaches describe discrete spaces as homogeneous network manifolds. Here we define Complex Quantum Network Manifolds (CQNM) describing the evolution of quantum network states, and constructed from growing simplicial complexes of dimension . We show that in d = 2 CQNM are homogeneous networks while for d > 2 they are scale-free i.e. they are characterized by large inhomogeneities of degrees like most complex networks. From the self-organized evolution of CQNM quantum statistics emerge spontaneously. Here we define the generalized degrees associated with the -faces of the -dimensional CQNMs, and we show that the statistics of these generalized degrees can either follow Fermi-Dirac, Boltzmann or Bose-Einstein distributions depending on the dimension of the -faces. PMID:26356079

Complex Quantum Network Manifolds in Dimension d > 2 are Scale-Free.

PubMed

Bianconi, Ginestra; Rahmede, Christoph

2015-09-10

In quantum gravity, several approaches have been proposed until now for the quantum description of discrete geometries. These theoretical frameworks include loop quantum gravity, causal dynamical triangulations, causal sets, quantum graphity, and energetic spin networks. Most of these approaches describe discrete spaces as homogeneous network manifolds. Here we define Complex Quantum Network Manifolds (CQNM) describing the evolution of quantum network states, and constructed from growing simplicial complexes of dimension d. We show that in d = 2 CQNM are homogeneous networks while for d > 2 they are scale-free i.e. they are characterized by large inhomogeneities of degrees like most complex networks. From the self-organized evolution of CQNM quantum statistics emerge spontaneously. Here we define the generalized degrees associated with the δ-faces of the d-dimensional CQNMs, and we show that the statistics of these generalized degrees can either follow Fermi-Dirac, Boltzmann or Bose-Einstein distributions depending on the dimension of the δ-faces.

Parameterisation of multi-scale continuum perfusion models from discrete vascular networks.

PubMed

Hyde, Eoin R; Michler, Christian; Lee, Jack; Cookson, Andrew N; Chabiniok, Radek; Nordsletten, David A; Smith, Nicolas P

2013-05-01

Experimental data and advanced imaging techniques are increasingly enabling the extraction of detailed vascular anatomy from biological tissues. Incorporation of anatomical data within perfusion models is non-trivial, due to heterogeneous vessel density and disparate radii scales. Furthermore, previous idealised networks have assumed a spatially repeating motif or periodic canonical cell, thereby allowing for a flow solution via homogenisation. However, such periodicity is not observed throughout anatomical networks. In this study, we apply various spatial averaging methods to discrete vascular geometries in order to parameterise a continuum model of perfusion. Specifically, a multi-compartment Darcy model was used to provide vascular scale separation for the fluid flow. Permeability tensor fields were derived from both synthetic and anatomically realistic networks using (1) porosity-scaled isotropic, (2) Huyghe and Van Campen, and (3) projected-PCA methods. The Darcy pressure fields were compared via a root-mean-square error metric to an averaged Poiseuille pressure solution over the same domain. The method of Huyghe and Van Campen performed better than the other two methods in all simulations, even for relatively coarse networks. Furthermore, inter-compartment volumetric flux fields, determined using the spatially averaged discrete flux per unit pressure difference, were shown to be accurate across a range of pressure boundary conditions. This work justifies the application of continuum flow models to characterise perfusion resulting from flow in an underlying vascular network.

Nonstationary Dynamics Data Analysis with Wavelet-SVD Filtering

NASA Technical Reports Server (NTRS)

Brenner, Marty; Groutage, Dale; Bessette, Denis (Technical Monitor)

2001-01-01

Nonstationary time-frequency analysis is used for identification and classification of aeroelastic and aeroservoelastic dynamics. Time-frequency multiscale wavelet processing generates discrete energy density distributions. The distributions are processed using the singular value decomposition (SVD). Discrete density functions derived from the SVD generate moments that detect the principal features in the data. The SVD standard basis vectors are applied and then compared with a transformed-SVD, or TSVD, which reduces the number of features into more compact energy density concentrations. Finally, from the feature extraction, wavelet-based modal parameter estimation is applied.

Integrable discrete PT symmetric model.

PubMed

Ablowitz, Mark J; Musslimani, Ziad H

2014-09-01

An exactly solvable discrete PT invariant nonlinear Schrödinger-like model is introduced. It is an integrable Hamiltonian system that exhibits a nontrivial nonlinear PT symmetry. A discrete one-soliton solution is constructed using a left-right Riemann-Hilbert formulation. It is shown that this pure soliton exhibits unique features such as power oscillations and singularity formation. The proposed model can be viewed as a discretization of a recently obtained integrable nonlocal nonlinear Schrödinger equation.

Research on Signature Verification Method Based on Discrete Fréchet Distance

NASA Astrophysics Data System (ADS)

Fang, J. L.; Wu, W.

2018-05-01

This paper proposes a multi-feature signature template based on discrete Fréchet distance, which breaks through the limitation of traditional signature authentication using a single signature feature. It solves the online handwritten signature authentication signature global feature template extraction calculation workload, signature feature selection unreasonable problem. In this experiment, the false recognition rate (FAR) and false rejection rate (FRR) of the statistical signature are calculated and the average equal error rate (AEER) is calculated. The feasibility of the combined template scheme is verified by comparing the average equal error rate of the combination template and the original template.

Terrain representation impact on periurban catchment morphological properties

NASA Astrophysics Data System (ADS)

Rodriguez, F.; Bocher, E.; Chancibault, K.

2013-04-01

SummaryModelling the hydrological behaviour of suburban catchments requires an estimation of environmental features, including land use and hydrographic networks. Suburban areas display a highly heterogeneous composition and encompass many anthropogenic elements that affect water flow paths, such as ditches, sewers, culverts and embankments. The geographical data available, either raster or vector data, may be of various origins and resolutions. Urban databases often offer very detailed data for sewer networks and 3D streets, yet the data covering rural zones may be coarser. This study is intended to highlight the sensitivity of geographical data as well as the data discretisation method used on the essential features of a periurban catchment, i.e. the catchment border and the drainage network. Three methods are implemented for this purpose. The first is the DEM (for digital elevation model) treatment method, which has traditionally been applied in the field of catchment hydrology. The second is based on urban database analysis and focuses on vector data, i.e. polygons and segments. The third method is a TIN (or triangular irregular network), which provides a consistent description of flow directions from an accurate representation of slope. It is assumed herein that the width function is representative of the catchment's hydrological response. The periurban Chézine catchment, located within the Nantes metropolitan area in western France, serves as the case study. The determination of both the main morphological features and the hydrological response of a suburban catchment varies significantly according to the discretization method employed, especially on upstream rural areas. Vector- and TIN-based methods allow representing the higher drainage density of urban areas, and consequently reveal the impact of these areas on the width function, since the DEM method fails. TINs seem to be more appropriate to take streets into account, because it allows a finer representation of topographical discontinuities. These results may help future developments of distributed hydrological models on periurban areas.

A Generalization Strategy for Discrete Area Feature by Using Stroke Grouping and Polarization Transportation Selection

NASA Astrophysics Data System (ADS)

Wang, Xiao; Burghardt, Dirk

2018-05-01

This paper presents a new strategy for the generalization of discrete area features by using stroke grouping method and polarization transportation selection. The mentioned stroke is constructed on derive of the refined proximity graph of area features, and the refinement is under the control of four constraints to meet different grouping requirements. The area features which belong to the same stroke are detected into the same group. The stroke-based strategy decomposes the generalization process into two sub-processes by judging whether the area features related to strokes or not. For the area features which belong to the same one stroke, they normally present a linear like pat-tern, and in order to preserve this kind of pattern, typification is chosen as the operator to implement the generalization work. For the remaining area features which are not related by strokes, they are still distributed randomly and discretely, and the selection is chosen to conduct the generalization operation. For the purpose of retaining their original distribution characteristic, a Polarization Transportation (PT) method is introduced to implement the selection operation. Buildings and lakes are selected as the representatives of artificial area feature and natural area feature respectively to take the experiments. The generalized results indicate that by adopting this proposed strategy, the original distribution characteristics of building and lake data can be preserved, and the visual perception is pre-served as before.

Low-power hardware implementation of movement decoding for brain computer interface with reduced-resolution discrete cosine transform.

PubMed

Minho Won; Albalawi, Hassan; Xin Li; Thomas, Donald E

2014-01-01

This paper describes a low-power hardware implementation for movement decoding of brain computer interface. Our proposed hardware design is facilitated by two novel ideas: (i) an efficient feature extraction method based on reduced-resolution discrete cosine transform (DCT), and (ii) a new hardware architecture of dual look-up table to perform discrete cosine transform without explicit multiplication. The proposed hardware implementation has been validated for movement decoding of electrocorticography (ECoG) signal by using a Xilinx FPGA Zynq-7000 board. It achieves more than 56× energy reduction over a reference design using band-pass filters for feature extraction.

Impulsive stabilization and impulsive synchronization of discrete-time delayed neural networks.

PubMed

Chen, Wu-Hua; Lu, Xiaomei; Zheng, Wei Xing

2015-04-01

This paper investigates the problems of impulsive stabilization and impulsive synchronization of discrete-time delayed neural networks (DDNNs). Two types of DDNNs with stabilizing impulses are studied. By introducing the time-varying Lyapunov functional to capture the dynamical characteristics of discrete-time impulsive delayed neural networks (DIDNNs) and by using a convex combination technique, new exponential stability criteria are derived in terms of linear matrix inequalities. The stability criteria for DIDNNs are independent of the size of time delay but rely on the lengths of impulsive intervals. With the newly obtained stability results, sufficient conditions on the existence of linear-state feedback impulsive controllers are derived. Moreover, a novel impulsive synchronization scheme for two identical DDNNs is proposed. The novel impulsive synchronization scheme allows synchronizing two identical DDNNs with unknown delays. Simulation results are given to validate the effectiveness of the proposed criteria of impulsive stabilization and impulsive synchronization of DDNNs. Finally, an application of the obtained impulsive synchronization result for two identical chaotic DDNNs to a secure communication scheme is presented.

Infrared and visual image fusion method based on discrete cosine transform and local spatial frequency in discrete stationary wavelet transform domain

NASA Astrophysics Data System (ADS)

Jin, Xin; Jiang, Qian; Yao, Shaowen; Zhou, Dongming; Nie, Rencan; Lee, Shin-Jye; He, Kangjian

2018-01-01

In order to promote the performance of infrared and visual image fusion and provide better visual effects, this paper proposes a hybrid fusion method for infrared and visual image by the combination of discrete stationary wavelet transform (DSWT), discrete cosine transform (DCT) and local spatial frequency (LSF). The proposed method has three key processing steps. Firstly, DSWT is employed to decompose the important features of the source image into a series of sub-images with different levels and spatial frequencies. Secondly, DCT is used to separate the significant details of the sub-images according to the energy of different frequencies. Thirdly, LSF is applied to enhance the regional features of DCT coefficients, and it can be helpful and useful for image feature extraction. Some frequently-used image fusion methods and evaluation metrics are employed to evaluate the validity of the proposed method. The experiments indicate that the proposed method can achieve good fusion effect, and it is more efficient than other conventional image fusion methods.

Application of texture analysis method for mammogram density classification

NASA Astrophysics Data System (ADS)

Nithya, R.; Santhi, B.

2017-07-01

Mammographic density is considered a major risk factor for developing breast cancer. This paper proposes an automated approach to classify breast tissue types in digital mammogram. The main objective of the proposed Computer-Aided Diagnosis (CAD) system is to investigate various feature extraction methods and classifiers to improve the diagnostic accuracy in mammogram density classification. Texture analysis methods are used to extract the features from the mammogram. Texture features are extracted by using histogram, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Difference Matrix (GLDM), Local Binary Pattern (LBP), Entropy, Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), Gabor transform and trace transform. These extracted features are selected using Analysis of Variance (ANOVA). The features selected by ANOVA are fed into the classifiers to characterize the mammogram into two-class (fatty/dense) and three-class (fatty/glandular/dense) breast density classification. This work has been carried out by using the mini-Mammographic Image Analysis Society (MIAS) database. Five classifiers are employed namely, Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). Experimental results show that ANN provides better performance than LDA, NB, KNN and SVM classifiers. The proposed methodology has achieved 97.5% accuracy for three-class and 99.37% for two-class density classification.

Predictions of first passage times in sparse discrete fracture networks using graph-based reductions

NASA Astrophysics Data System (ADS)

Hyman, J.; Hagberg, A.; Srinivasan, G.; Mohd-Yusof, J.; Viswanathan, H. S.

2017-12-01

We present a graph-based methodology to reduce the computational cost of obtaining first passage times through sparse fracture networks. We derive graph representations of generic three-dimensional discrete fracture networks (DFNs) using the DFN topology and flow boundary conditions. Subgraphs corresponding to the union of the k shortest paths between the inflow and outflow boundaries are identified and transport on their equivalent subnetworks is compared to transport through the full network. The number of paths included in the subgraphs is based on the scaling behavior of the number of edges in the graph with the number of shortest paths. First passage times through the subnetworks are in good agreement with those obtained in the full network, both for individual realizations and in distribution. Accurate estimates of first passage times are obtained with an order of magnitude reduction of CPU time and mesh size using the proposed method.

Predictions of first passage times in sparse discrete fracture networks using graph-based reductions

NASA Astrophysics Data System (ADS)

Hyman, Jeffrey D.; Hagberg, Aric; Srinivasan, Gowri; Mohd-Yusof, Jamaludin; Viswanathan, Hari

2017-07-01

We present a graph-based methodology to reduce the computational cost of obtaining first passage times through sparse fracture networks. We derive graph representations of generic three-dimensional discrete fracture networks (DFNs) using the DFN topology and flow boundary conditions. Subgraphs corresponding to the union of the k shortest paths between the inflow and outflow boundaries are identified and transport on their equivalent subnetworks is compared to transport through the full network. The number of paths included in the subgraphs is based on the scaling behavior of the number of edges in the graph with the number of shortest paths. First passage times through the subnetworks are in good agreement with those obtained in the full network, both for individual realizations and in distribution. Accurate estimates of first passage times are obtained with an order of magnitude reduction of CPU time and mesh size using the proposed method.

An opinion-driven behavioral dynamics model for addictive behaviors

NASA Astrophysics Data System (ADS)

Moore, Thomas W.; Finley, Patrick D.; Apelberg, Benjamin J.; Ambrose, Bridget K.; Brodsky, Nancy S.; Brown, Theresa J.; Husten, Corinne; Glass, Robert J.

2015-04-01

We present a model of behavioral dynamics that combines a social network-based opinion dynamics model with behavioral mapping. The behavioral component is discrete and history-dependent to represent situations in which an individual's behavior is initially driven by opinion and later constrained by physiological or psychological conditions that serve to maintain the behavior. Individuals are modeled as nodes in a social network connected by directed edges. Parameter sweeps illustrate model behavior and the effects of individual parameters and parameter interactions on model results. Mapping a continuous opinion variable into a discrete behavioral space induces clustering on directed networks. Clusters provide targets of opportunity for influencing the network state; however, the smaller the network the greater the stochasticity and potential variability in outcomes. This has implications both for behaviors that are influenced by close relationships verses those influenced by societal norms and for the effectiveness of strategies for influencing those behaviors.

Conservative, unconditionally stable discretization methods for Hamiltonian equations, applied to wave motion in lattice equations modeling protein molecules

NASA Astrophysics Data System (ADS)

LeMesurier, Brenton

2012-01-01

A new approach is described for generating exactly energy-momentum conserving time discretizations for a wide class of Hamiltonian systems of DEs with quadratic momenta, including mechanical systems with central forces; it is well-suited in particular to the large systems that arise in both spatial discretizations of nonlinear wave equations and lattice equations such as the Davydov System modeling energetic pulse propagation in protein molecules. The method is unconditionally stable, making it well-suited to equations of broadly “Discrete NLS form”, including many arising in nonlinear optics. Key features of the resulting discretizations are exact conservation of both the Hamiltonian and quadratic conserved quantities related to continuous linear symmetries, preservation of time reversal symmetry, unconditional stability, and respecting the linearity of certain terms. The last feature allows a simple, efficient iterative solution of the resulting nonlinear algebraic systems that retain unconditional stability, avoiding the need for full Newton-type solvers. One distinction from earlier work on conservative discretizations is a new and more straightforward nearly canonical procedure for constructing the discretizations, based on a “discrete gradient calculus with product rule” that mimics the essential properties of partial derivatives. This numerical method is then used to study the Davydov system, revealing that previously conjectured continuum limit approximations by NLS do not hold, but that sech-like pulses related to NLS solitons can nevertheless sometimes arise.

Simulation Model for Scenario Optimization of the Ready-Mix Concrete Delivery Problem

NASA Astrophysics Data System (ADS)

Galić, Mario; Kraus, Ivan

2016-12-01

This paper introduces a discrete simulation model for solving routing and network material flow problems in construction projects. Before the description of the model a detailed literature review is provided. The model is verified using a case study of solving the ready-mix concrete network flow and routing problem in metropolitan area in Croatia. Within this study real-time input parameters were taken into account. Simulation model is structured in Enterprise Dynamics simulation software and Microsoft Excel linked with Google Maps. The model is dynamic, easily managed and adjustable, but also provides good estimation for minimization of costs and realization time in solving discrete routing and material network flow problems.

Neural networks for tracking of unknown SISO discrete-time nonlinear dynamic systems.

PubMed

Aftab, Muhammad Saleheen; Shafiq, Muhammad

2015-11-01

This article presents a Lyapunov function based neural network tracking (LNT) strategy for single-input, single-output (SISO) discrete-time nonlinear dynamic systems. The proposed LNT architecture is composed of two feedforward neural networks operating as controller and estimator. A Lyapunov function based back propagation learning algorithm is used for online adjustment of the controller and estimator parameters. The controller and estimator error convergence and closed-loop system stability analysis is performed by Lyapunov stability theory. Moreover, two simulation examples and one real-time experiment are investigated as case studies. The achieved results successfully validate the controller performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Plane stress problems using hysteretic rigid body spring network models

NASA Astrophysics Data System (ADS)

Christos, Sofianos D.; Vlasis, Koumousis K.

2017-10-01

In this work, a discrete numerical scheme is presented capable of modeling the hysteretic behavior of 2D structures. Rigid Body Spring Network (RBSN) models that were first proposed by Kawai (Nucl Eng Des 48(1):29-207, 1978) are extended to account for hysteretic elastoplastic behavior. Discretization is based on Voronoi tessellation, as proposed specifically for RBSN models to ensure uniformity. As a result, the structure is discretized into convex polygons that form the discrete rigid bodies of the model. These are connected with three zero length, i.e., single-node springs in the middle of their common facets. The springs follow the smooth hysteretic Bouc-Wen model which efficiently incorporates classical plasticity with no direct reference to a yield surface. Numerical results for both static and dynamic loadings are presented, which validate the proposed simplified spring-mass formulation. In addition, they verify the model's applicability on determining primarily the displacement field and plastic zones compared to the standard elastoplastic finite element method.

Short-Term Memory in Orthogonal Neural Networks

NASA Astrophysics Data System (ADS)

White, Olivia L.; Lee, Daniel D.; Sompolinsky, Haim

2004-04-01

We study the ability of linear recurrent networks obeying discrete time dynamics to store long temporal sequences that are retrievable from the instantaneous state of the network. We calculate this temporal memory capacity for both distributed shift register and random orthogonal connectivity matrices. We show that the memory capacity of these networks scales with system size.

Using RDF to Model the Structure and Process of Systems

NASA Astrophysics Data System (ADS)

Rodriguez, Marko A.; Watkins, Jennifer H.; Bollen, Johan; Gershenson, Carlos

Many systems can be described in terms of networks of discrete elements and their various relationships to one another. A semantic network, or multi-relational network, is a directed labeled graph consisting of a heterogeneous set of entities connected by a heterogeneous set of relationships. Semantic networks serve as a promising general-purpose modeling substrate for complex systems. Various standardized formats and tools are now available to support practical, large-scale semantic network models. First, the Resource Description Framework (RDF) offers a standardized semantic network data model that can be further formalized by ontology modeling languages such as RDF Schema (RDFS) and the Web Ontology Language (OWL). Second, the recent introduction of highly performant triple-stores (i.e. semantic network databases) allows semantic network models on the order of 109 edges to be efficiently stored and manipulated. RDF and its related technologies are currently used extensively in the domains of computer science, digital library science, and the biological sciences. This article will provide an introduction to RDF/RDFS/OWL and an examination of its suitability to model discrete element complex systems.

Discrete Particle Swarm Optimization Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks

PubMed Central

Yang, Jin; Liu, Fagui; Cao, Jianneng; Wang, Liangming

2016-01-01

Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle’s position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption. PMID:27428971

Hydraulic Fracturing and Production Optimization in Eagle Ford Shale Using Coupled Geomechanics and Fluid Flow Model

NASA Astrophysics Data System (ADS)

Suppachoknirun, Theerapat; Tutuncu, Azra N.

2017-12-01

With increasing production from shale gas and tight oil reservoirs, horizontal drilling and multistage hydraulic fracturing processes have become a routine procedure in unconventional field development efforts. Natural fractures play a critical role in hydraulic fracture growth, subsequently affecting stimulated reservoir volume and the production efficiency. Moreover, the existing fractures can also contribute to the pressure-dependent fluid leak-off during the operations. Hence, a reliable identification of the discrete fracture network covering the zone of interest prior to the hydraulic fracturing design needs to be incorporated into the hydraulic fracturing and reservoir simulations for realistic representation of the in situ reservoir conditions. In this research study, an integrated 3-D fracture and fluid flow model have been developed using a new approach to simulate the fluid flow and deliver reliable production forecasting in naturally fractured and hydraulically stimulated tight reservoirs. The model was created with three key modules. A complex 3-D discrete fracture network model introduces realistic natural fracture geometry with the associated fractured reservoir characteristics. A hydraulic fracturing model is created utilizing the discrete fracture network for simulation of the hydraulic fracture and flow in the complex discrete fracture network. Finally, a reservoir model with the production grid system is used allowing the user to efficiently perform the fluid flow simulation in tight formations with complex fracture networks. The complex discrete natural fracture model, the integrated discrete fracture model for the hydraulic fracturing, the fluid flow model, and the input dataset have been validated against microseismic fracture mapping and commingled production data obtained from a well pad with three horizontal production wells located in the Eagle Ford oil window in south Texas. Two other fracturing geometries were also evaluated to optimize the cumulative production and for the three wells individually. Significant reduction in the production rate in early production times is anticipated in tight reservoirs regardless of the fracturing techniques implemented. The simulations conducted using the alternating fracturing technique led to more oil production than when zipper fracturing was used for a 20-year production period. Yet, due to the decline experienced, the differences in cumulative production get smaller, and the alternating fracturing is not practically implementable while field application of zipper fracturing technique is more practical and widely used.

Progressively expanded neural network for automatic material identification in hyperspectral imagery

NASA Astrophysics Data System (ADS)

Paheding, Sidike

The science of hyperspectral remote sensing focuses on the exploitation of the spectral signatures of various materials to enhance capabilities including object detection, recognition, and material characterization. Hyperspectral imagery (HSI) has been extensively used for object detection and identification applications since it provides plenty of spectral information to uniquely identify materials by their reflectance spectra. HSI-based object detection algorithms can be generally classified into stochastic and deterministic approaches. Deterministic approaches are comparatively simple to apply since it is usually based on direct spectral similarity such as spectral angles or spectral correlation. In contrast, stochastic algorithms require statistical modeling and estimation for target class and non-target class. Over the decades, many single class object detection methods have been proposed in the literature, however, deterministic multiclass object detection in HSI has not been explored. In this work, we propose a deterministic multiclass object detection scheme, named class-associative spectral fringe-adjusted joint transform correlation. Human brain is capable of simultaneously processing high volumes of multi-modal data received every second of the day. In contrast, a machine sees input data simply as random binary numbers. Although machines are computationally efficient, they are inferior when comes to data abstraction and interpretation. Thus, mimicking the learning strength of human brain has been current trend in artificial intelligence. In this work, we present a biological inspired neural network, named progressively expanded neural network (PEN Net), based on nonlinear transformation of input neurons to a feature space for better pattern differentiation. In PEN Net, discrete fixed excitations are disassembled and scattered in the feature space as a nonlinear line. Each disassembled element on the line corresponds to a pattern with similar features. Unlike the conventional neural network where hidden neurons need to be iteratively adjusted to achieve better accuracy, our proposed PEN Net does not require hidden neurons tuning which achieves better computational efficiency, and it has also shown superior performance in HSI classification tasks compared to the state-of-the-arts. Spectral-spatial features based HSI classification framework has shown stronger strength compared to spectral-only based methods. In our lastly proposed technique, PEN Net is incorporated with multiscale spatial features (i.e., multiscale complete local binary pattern) to perform a spectral-spatial classification of HSI. Several experiments demonstrate excellent performance of our proposed technique compared to the more recent developed approaches.

Extended Plefka expansion for stochastic dynamics

NASA Astrophysics Data System (ADS)

Bravi, B.; Sollich, P.; Opper, M.

2016-05-01

We propose an extension of the Plefka expansion, which is well known for the dynamics of discrete spins, to stochastic differential equations with continuous degrees of freedom and exhibiting generic nonlinearities. The scenario is sufficiently general to allow application to e.g. biochemical networks involved in metabolism and regulation. The main feature of our approach is to constrain in the Plefka expansion not just first moments akin to magnetizations, but also second moments, specifically two-time correlations and responses for each degree of freedom. The end result is an effective equation of motion for each single degree of freedom, where couplings to other variables appear as a self-coupling to the past (i.e. memory term) and a coloured noise. This constitutes a new mean field approximation that should become exact in the thermodynamic limit of a large network, for suitably long-ranged couplings. For the analytically tractable case of linear dynamics we establish this exactness explicitly by appeal to spectral methods of random matrix theory, for Gaussian couplings with arbitrary degree of symmetry.

Periodicity and global exponential stability of generalized Cohen-Grossberg neural networks with discontinuous activations and mixed delays.

PubMed

Wang, Dongshu; Huang, Lihong

2014-03-01

In this paper, we investigate the periodic dynamical behaviors for a class of general Cohen-Grossberg neural networks with discontinuous right-hand sides, time-varying and distributed delays. By means of retarded differential inclusions theory and the fixed point theorem of multi-valued maps, the existence of periodic solutions for the neural networks is obtained. After that, we derive some sufficient conditions for the global exponential stability and convergence of the neural networks, in terms of nonsmooth analysis theory with generalized Lyapunov approach. Without assuming the boundedness (or the growth condition) and monotonicity of the discontinuous neuron activation functions, our results will also be valid. Moreover, our results extend previous works not only on discrete time-varying and distributed delayed neural networks with continuous or even Lipschitz continuous activations, but also on discrete time-varying and distributed delayed neural networks with discontinuous activations. We give some numerical examples to show the applicability and effectiveness of our main results. Copyright © 2013 Elsevier Ltd. All rights reserved.

A discrete mathematical model applied to genetic regulation and metabolic networks.

PubMed

Asenjo, A J; Ramirez, P; Rapaport, I; Aracena, J; Goles, E; Andrews, B A

2007-03-01

This paper describes the use of a discrete mathematical model to represent the basic mechanisms of regulation of the bacteria E. coli in batch fermentation. The specific phenomena studied were the changes in metabolism and genetic regulation when the bacteria use three different carbon substrates (glucose, glycerol, and acetate). The model correctly predicts the behavior of E. coli vis-à-vis substrate mixtures. In a mixture of glucose, glycerol, and acetate, it prefers glucose, then glycerol, and finally acetate. The model included 67 nodes; 28 were genes, 20 enzymes, and 19 regulators/biochemical compounds. The model represents both the genetic regulation and metabolic networks in an inrtegrated form, which is how they function biologically. This is one of the first attempts to include both of these networks in one model. Previously, discrete mathematical models were used only to describe genetic regulation networks. The study of the network dynamics generated 8 (2(3)) fixed points, one for each nutrient configuration (substrate mixture) in the medium. The fixed points of the discrete model reflect the phenotypes described. Gene expression and the patterns of the metabolic fluxes generated are described accurately. The activation of the gene regulation network depends basically on the presence of glucose and glycerol. The model predicts the behavior when mixed carbon sources are utilized as well as when there is no carbon source present. Fictitious jokers (Joker1, Joker2, and Repressor SdhC) had to be created to control 12 genes whose regulation mechanism is unknown, since glycerol and glucose do not act directly on the genes. The approach presented in this paper is particularly useful to investigate potential unknown gene regulation mechanisms; such a novel approach can also be used to describe other gene regulation situations such as the comparison between non-recombinant and recombinant yeast strain, producing recombinant proteins, presently under investigation in our group.

Formal modeling and analysis of ER-α associated Biological Regulatory Network in breast cancer.

PubMed

Khalid, Samra; Hanif, Rumeza; Tareen, Samar H K; Siddiqa, Amnah; Bibi, Zurah; Ahmad, Jamil

2016-01-01

Breast cancer (BC) is one of the leading cause of death among females worldwide. The increasing incidence of BC is due to various genetic and environmental changes which lead to the disruption of cellular signaling network(s). It is a complex disease in which several interlinking signaling cascades play a crucial role in establishing a complex regulatory network. The logical modeling approach of René Thomas has been applied to analyze the behavior of estrogen receptor-alpha (ER- α ) associated Biological Regulatory Network (BRN) for a small part of complex events that leads to BC metastasis. A discrete model was constructed using the kinetic logic formalism and its set of logical parameters were obtained using the model checking technique implemented in the SMBioNet software which is consistent with biological observations. The discrete model was further enriched with continuous dynamics by converting it into an equivalent Petri Net (PN) to analyze the logical parameters of the involved entities. In-silico based discrete and continuous modeling of ER- α associated signaling network involved in BC provides information about behaviors and gene-gene interaction in detail. The dynamics of discrete model revealed, imperative behaviors represented as cyclic paths and trajectories leading to pathogenic states such as metastasis. Results suggest that the increased expressions of receptors ER- α , IGF-1R and EGFR slow down the activity of tumor suppressor genes (TSGs) such as BRCA1, p53 and Mdm2 which can lead to metastasis. Therefore, IGF-1R and EGFR are considered as important inhibitory targets to control the metastasis in BC. The in-silico approaches allow us to increase our understanding of the functional properties of living organisms. It opens new avenues of investigations of multiple inhibitory targets (ER- α , IGF-1R and EGFR) for wet lab experiments as well as provided valuable insights in the treatment of cancers such as BC.

Designing perturbative metamaterials from discrete models.

PubMed

Matlack, Kathryn H; Serra-Garcia, Marc; Palermo, Antonio; Huber, Sebastian D; Daraio, Chiara

2018-04-01

Identifying material geometries that lead to metamaterials with desired functionalities presents a challenge for the field. Discrete, or reduced-order, models provide a concise description of complex phenomena, such as negative refraction, or topological surface states; therefore, the combination of geometric building blocks to replicate discrete models presenting the desired features represents a promising approach. However, there is no reliable way to solve such an inverse problem. Here, we introduce 'perturbative metamaterials', a class of metamaterials consisting of weakly interacting unit cells. The weak interaction allows us to associate each element of the discrete model with individual geometric features of the metamaterial, thereby enabling a systematic design process. We demonstrate our approach by designing two-dimensional elastic metamaterials that realize Veselago lenses, zero-dispersion bands and topological surface phonons. While our selected examples are within the mechanical domain, the same design principle can be applied to acoustic, thermal and photonic metamaterials composed of weakly interacting unit cells.

Applications of Artificial Neural Networks in Structural Engineering with Emphasis on Continuum Models

NASA Technical Reports Server (NTRS)

Kapania, Rakesh K.; Liu, Youhua

1998-01-01

The use of continuum models for the analysis of discrete built-up complex aerospace structures is an attractive idea especially at the conceptual and preliminary design stages. But the diversity of available continuum models and hard-to-use qualities of these models have prevented them from finding wide applications. In this regard, Artificial Neural Networks (ANN or NN) may have a great potential as these networks are universal approximators that can realize any continuous mapping, and can provide general mechanisms for building models from data whose input-output relationship can be highly nonlinear. The ultimate aim of the present work is to be able to build high fidelity continuum models for complex aerospace structures using the ANN. As a first step, the concepts and features of ANN are familiarized through the MATLAB NN Toolbox by simulating some representative mapping examples, including some problems in structural engineering. Then some further aspects and lessons learned about the NN training are discussed, including the performances of Feed-Forward and Radial Basis Function NN when dealing with noise-polluted data and the technique of cross-validation. Finally, as an example of using NN in continuum models, a lattice structure with repeating cells is represented by a continuum beam whose properties are provided by neural networks.

Orienting in virtual environments: How are surface features and environmental geometry weighted in an orientation task?

PubMed

Kelly, Debbie M; Bischof, Walter F

2008-10-01

We investigated how human adults orient in enclosed virtual environments, when discrete landmark information is not available and participants have to rely on geometric and featural information on the environmental surfaces. In contrast to earlier studies, where, for women, the featural information from discrete landmarks overshadowed the encoding of the geometric information, Experiment 1 showed that when featural information is conjoined with the environmental surfaces, men and women encoded both types of information. Experiment 2 showed that, although both types of information are encoded, performance in locating a goal position is better if it is close to a geometrically or featurally distinct location. Furthermore, although features are relied upon more strongly than geometry, initial experience with an environment influences the relative weighting of featural and geometric cues. Taken together, these results show that human adults use a flexible strategy for encoding spatial information.

Harmonic wavelet packet transform for on-line system health diagnosis

NASA Astrophysics Data System (ADS)

Yan, Ruqiang; Gao, Robert X.

2004-07-01

This paper presents a new approach to on-line health diagnosis of mechanical systems, based on the wavelet packet transform. Specifically, signals acquired from vibration sensors are decomposed into sub-bands by means of the discrete harmonic wavelet packet transform (DHWPT). Based on the Fisher linear discriminant criterion, features in the selected sub-bands are then used as inputs to three classifiers (Nearest Neighbor rule-based and two Neural Network-based), for system health condition assessment. Experimental results have confirmed that, comparing to the conventional approach where statistical parameters from raw signals are used, the presented approach enabled higher signal-to-noise ratio for more effective and intelligent use of the sensory information, thus leading to more accurate system health diagnosis.

Toxicological Tipping Points: Learning Boolean Networks from High-Content Imaging Data. (BOSC meeting)

EPA Science Inventory

The objective of this work is to elucidate biological networks underlying cellular tipping points using time-course data. We discretized the high-content imaging (HCI) data and inferred Boolean networks (BNs) that could accurately predict dynamic cellular trajectories. We found t...

EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN

PubMed Central

AlSharabi, Khalil; Ibrahim, Sutrisno; Alsuwailem, Abdullah

2017-01-01

Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD) of autism ‎based on electroencephalography (EEG) signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT), entropy (En), and artificial neural network (ANN). DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands. The feature vector is constructed by computing Shannon entropy values from each EEG subband. ANN classifies the corresponding EEG signal into normal or autistic based on the extracted features. The experimental results show the effectiveness of the proposed method for assisting autism diagnosis. A receiver operating characteristic (ROC) curve metric is used to quantify the performance of the proposed method. The proposed method obtained promising results tested using real dataset provided by King Abdulaziz Hospital, Jeddah, Saudi Arabia. PMID:28484720

An Algorithm for the Mixed Transportation Network Design Problem

PubMed Central

Liu, Xinyu; Chen, Qun

2016-01-01

This paper proposes an optimization algorithm, the dimension-down iterative algorithm (DDIA), for solving a mixed transportation network design problem (MNDP), which is generally expressed as a mathematical programming with equilibrium constraint (MPEC). The upper level of the MNDP aims to optimize the network performance via both the expansion of the existing links and the addition of new candidate links, whereas the lower level is a traditional Wardrop user equilibrium (UE) problem. The idea of the proposed solution algorithm (DDIA) is to reduce the dimensions of the problem. A group of variables (discrete/continuous) is fixed to optimize another group of variables (continuous/discrete) alternately; then, the problem is transformed into solving a series of CNDPs (continuous network design problems) and DNDPs (discrete network design problems) repeatedly until the problem converges to the optimal solution. The advantage of the proposed algorithm is that its solution process is very simple and easy to apply. Numerical examples show that for the MNDP without budget constraint, the optimal solution can be found within a few iterations with DDIA. For the MNDP with budget constraint, however, the result depends on the selection of initial values, which leads to different optimal solutions (i.e., different local optimal solutions). Some thoughts are given on how to derive meaningful initial values, such as by considering the budgets of new and reconstruction projects separately. PMID:27626803

Continuous-variable quantum network coding for coherent states

NASA Astrophysics Data System (ADS)

Shang, Tao; Li, Ke; Liu, Jian-wei

2017-04-01

As far as the spectral characteristic of quantum information is concerned, the existing quantum network coding schemes can be looked on as the discrete-variable quantum network coding schemes. Considering the practical advantage of continuous variables, in this paper, we explore two feasible continuous-variable quantum network coding (CVQNC) schemes. Basic operations and CVQNC schemes are both provided. The first scheme is based on Gaussian cloning and ADD/SUB operators and can transmit two coherent states across with a fidelity of 1/2, while the second scheme utilizes continuous-variable quantum teleportation and can transmit two coherent states perfectly. By encoding classical information on quantum states, quantum network coding schemes can be utilized to transmit classical information. Scheme analysis shows that compared with the discrete-variable paradigms, the proposed CVQNC schemes provide better network throughput from the viewpoint of classical information transmission. By modulating the amplitude and phase quadratures of coherent states with classical characters, the first scheme and the second scheme can transmit 4{log _2}N and 2{log _2}N bits of information by a single network use, respectively.

MORE: mixed optimization for reverse engineering--an application to modeling biological networks response via sparse systems of nonlinear differential equations.

PubMed

Sambo, Francesco; de Oca, Marco A Montes; Di Camillo, Barbara; Toffolo, Gianna; Stützle, Thomas

2012-01-01

Reverse engineering is the problem of inferring the structure of a network of interactions between biological variables from a set of observations. In this paper, we propose an optimization algorithm, called MORE, for the reverse engineering of biological networks from time series data. The model inferred by MORE is a sparse system of nonlinear differential equations, complex enough to realistically describe the dynamics of a biological system. MORE tackles separately the discrete component of the problem, the determination of the biological network topology, and the continuous component of the problem, the strength of the interactions. This approach allows us both to enforce system sparsity, by globally constraining the number of edges, and to integrate a priori information about the structure of the underlying interaction network. Experimental results on simulated and real-world networks show that the mixed discrete/continuous optimization approach of MORE significantly outperforms standard continuous optimization and that MORE is competitive with the state of the art in terms of accuracy of the inferred networks.

Electro-mechanical dynamics of spiral waves in a discrete 2D model of human atrial tissue.

PubMed

Brocklehurst, Paul; Ni, Haibo; Zhang, Henggui; Ye, Jianqiao

2017-01-01

We investigate the effect of mechano-electrical feedback and atrial fibrillation induced electrical remodelling (AFER) of cellular ion channel properties on the dynamics of spiral waves in a discrete 2D model of human atrial tissue. The tissue electro-mechanics are modelled using the discrete element method (DEM). Millions of bonded DEM particles form a network of coupled atrial cells representing 2D cardiac tissue, allowing simulations of the dynamic behaviour of electrical excitation waves and mechanical contraction in the tissue. In the tissue model, each cell is modelled by nine particles, accounting for the features of individual cellular geometry; and discrete inter-cellular spatial arrangement of cells is also considered. The electro-mechanical model of a human atrial single-cell was constructed by strongly coupling the electrophysiological model of Colman et al. to the mechanical myofilament model of Rice et al., with parameters modified based on experimental data. A stretch-activated channel was incorporated into the model to simulate the mechano-electrical feedback. In order to investigate the effect of mechano-electrical feedback on the dynamics of spiral waves, simulations of spiral waves were conducted in both the electromechanical model and the electrical-only model in normal and AFER conditions, to allow direct comparison of the results between the models. Dynamics of spiral waves were characterized by tracing their tip trajectories, stability, excitation frequencies and meandering range of tip trajectories. It was shown that the developed DEM method provides a stable and efficient model of human atrial tissue with considerations of the intrinsically discrete and anisotropic properties of the atrial tissue, which are challenges to handle in traditional continuum mechanics models. This study provides mechanistic insights into the complex behaviours of spiral waves and the genesis of atrial fibrillation by showing an important role of the mechano-electrical feedback in facilitating and promoting atrial fibrillation.

Electro-mechanical dynamics of spiral waves in a discrete 2D model of human atrial tissue

PubMed Central

Zhang, Henggui

2017-01-01

We investigate the effect of mechano-electrical feedback and atrial fibrillation induced electrical remodelling (AFER) of cellular ion channel properties on the dynamics of spiral waves in a discrete 2D model of human atrial tissue. The tissue electro-mechanics are modelled using the discrete element method (DEM). Millions of bonded DEM particles form a network of coupled atrial cells representing 2D cardiac tissue, allowing simulations of the dynamic behaviour of electrical excitation waves and mechanical contraction in the tissue. In the tissue model, each cell is modelled by nine particles, accounting for the features of individual cellular geometry; and discrete inter-cellular spatial arrangement of cells is also considered. The electro-mechanical model of a human atrial single-cell was constructed by strongly coupling the electrophysiological model of Colman et al. to the mechanical myofilament model of Rice et al., with parameters modified based on experimental data. A stretch-activated channel was incorporated into the model to simulate the mechano-electrical feedback. In order to investigate the effect of mechano-electrical feedback on the dynamics of spiral waves, simulations of spiral waves were conducted in both the electromechanical model and the electrical-only model in normal and AFER conditions, to allow direct comparison of the results between the models. Dynamics of spiral waves were characterized by tracing their tip trajectories, stability, excitation frequencies and meandering range of tip trajectories. It was shown that the developed DEM method provides a stable and efficient model of human atrial tissue with considerations of the intrinsically discrete and anisotropic properties of the atrial tissue, which are challenges to handle in traditional continuum mechanics models. This study provides mechanistic insights into the complex behaviours of spiral waves and the genesis of atrial fibrillation by showing an important role of the mechano-electrical feedback in facilitating and promoting atrial fibrillation. PMID:28510575

Efficacy Evaluation of Different Wavelet Feature Extraction Methods on Brain MRI Tumor Detection

NASA Astrophysics Data System (ADS)

Nabizadeh, Nooshin; John, Nigel; Kubat, Miroslav

2014-03-01

Automated Magnetic Resonance Imaging brain tumor detection and segmentation is a challenging task. Among different available methods, feature-based methods are very dominant. While many feature extraction techniques have been employed, it is still not quite clear which of feature extraction methods should be preferred. To help improve the situation, we present the results of a study in which we evaluate the efficiency of using different wavelet transform features extraction methods in brain MRI abnormality detection. Applying T1-weighted brain image, Discrete Wavelet Transform (DWT), Discrete Wavelet Packet Transform (DWPT), Dual Tree Complex Wavelet Transform (DTCWT), and Complex Morlet Wavelet Transform (CMWT) methods are applied to construct the feature pool. Three various classifiers as Support Vector Machine, K Nearest Neighborhood, and Sparse Representation-Based Classifier are applied and compared for classifying the selected features. The results show that DTCWT and CMWT features classified with SVM, result in the highest classification accuracy, proving of capability of wavelet transform features to be informative in this application.

Discrete time modeling and stability analysis of TCP Vegas

NASA Astrophysics Data System (ADS)

You, Byungyong; Koo, Kyungmo; Lee, Jin S.

2007-12-01

This paper presents an analysis method for TCP Vegas network model with single link and single source. Some papers showed global stability of several network models, but those models are not a dual problem where dynamics both exist in sources and links such as TCP Vegas. Other papers studied TCP Vegas as a dual problem, but it did not fully derive an asymptotic stability region. Therefore we analyze TCP Vegas with Jury's criterion which is necessary and sufficient condition. So we use state space model in discrete time and by using Jury's criterion, we could find an asymptotic stability region of TCP Vegas network model. This result is verified by ns-2 simulation. And by comparing with other results, we could know our method performed well.

Comparison of stationary and oscillatory dynamics described by differential equations and Boolean maps in transcriptional regulatory circuits

NASA Astrophysics Data System (ADS)

Ye, Weiming; Li, Pengfei; Huang, Xuhui; Xia, Qinzhi; Mi, Yuanyuan; Chen, Runsheng; Hu, Gang

2010-10-01

Exploring the principle and relationship of gene transcriptional regulations (TR) has been becoming a generally researched issue. So far, two major mathematical methods, ordinary differential equation (ODE) method and Boolean map (BM) method have been widely used for these purposes. It is commonly believed that simplified BMs are reasonable approximations of more realistic ODEs, and both methods may reveal qualitatively the same essential features though the dynamical details of both systems may show some differences. In this Letter we exhaustively enumerated all the 3-gene networks and many autonomous randomly constructed TR networks with more genes by using both the ODE and BM methods. In comparison we found that both methods provide practically identical results in most of cases of steady solutions. However, to our great surprise, most of network structures showing periodic cycles with the BM method possess only stationary states in ODE descriptions. These observations strongly suggest that many periodic oscillations and other complicated oscillatory states revealed by the BM rule may be related to the computational errors of variable and time discretizations and rarely have correspondence in realistic biology transcriptional regulatory circuits.

Cardiac cell: a biological laser?

PubMed

Chorvat, D; Chorvatova, A

2008-04-01

We present a new concept of cardiac cells based on an analogy with lasers, practical implementations of quantum resonators. In this concept, each cardiac cell comprises a network of independent nodes, characterised by a set of discrete energy levels and certain transition probabilities between them. Interaction between the nodes is given by threshold-limited energy transfer, leading to quantum-like behaviour of the whole network. We propose that in cardiomyocytes, during each excitation-contraction coupling cycle, stochastic calcium release and the unitary properties of ionic channels constitute an analogue to laser active medium prone to "population inversion" and "spontaneous emission" phenomena. This medium, when powered by an incoming threshold-reaching voltage discharge in the form of an action potential, responds to the calcium influx through L-type calcium channels by stimulated emission of Ca2+ ions in a coherent, synchronised and amplified release process known as calcium-induced calcium release. In parallel, phosphorylation-stimulated molecular amplification in protein cascades adds tuneable features to the cells. In this framework, the heart can be viewed as a coherent network of synchronously firing cardiomyocytes behaving as pulsed laser-like amplifiers, coupled to pulse-generating pacemaker master-oscillators. The concept brings a new viewpoint on cardiac diseases as possible alterations of "cell lasing" properties.

Robust passivity analysis for discrete-time recurrent neural networks with mixed delays

NASA Astrophysics Data System (ADS)

Huang, Chuan-Kuei; Shu, Yu-Jeng; Chang, Koan-Yuh; Shou, Ho-Nien; Lu, Chien-Yu

2015-02-01

This article considers the robust passivity analysis for a class of discrete-time recurrent neural networks (DRNNs) with mixed time-delays and uncertain parameters. The mixed time-delays that consist of both the discrete time-varying and distributed time-delays in a given range are presented, and the uncertain parameters are norm-bounded. The activation functions are assumed to be globally Lipschitz continuous. Based on new bounding technique and appropriate type of Lyapunov functional, a sufficient condition is investigated to guarantee the existence of the desired robust passivity condition for the DRNNs, which can be derived in terms of a family of linear matrix inequality (LMI). Some free-weighting matrices are introduced to reduce the conservatism of the criterion by using the bounding technique. A numerical example is given to illustrate the effectiveness and applicability.

ADFNE: Open source software for discrete fracture network engineering, two and three dimensional applications

NASA Astrophysics Data System (ADS)

Fadakar Alghalandis, Younes

2017-05-01

Rapidly growing topic, the discrete fracture network engineering (DFNE), has already attracted many talents from diverse disciplines in academia and industry around the world to challenge difficult problems related to mining, geothermal, civil, oil and gas, water and many other projects. Although, there are few commercial software capable of providing some useful functionalities fundamental for DFNE, their costs, closed code (black box) distributions and hence limited programmability and tractability encouraged us to respond to this rising demand with a new solution. This paper introduces an open source comprehensive software package for stochastic modeling of fracture networks in two- and three-dimension in discrete formulation. Functionalities included are geometric modeling (e.g., complex polygonal fracture faces, and utilizing directional statistics), simulations, characterizations (e.g., intersection, clustering and connectivity analyses) and applications (e.g., fluid flow). The package is completely written in Matlab scripting language. Significant efforts have been made to bring maximum flexibility to the functions in order to solve problems in both two- and three-dimensions in an easy and united way that is suitable for beginners, advanced and experienced users.

An Equivalent Fracture Modeling Method

NASA Astrophysics Data System (ADS)

Li, Shaohua; Zhang, Shujuan; Yu, Gaoming; Xu, Aiyun

2017-12-01

3D fracture network model is built based on discrete fracture surfaces, which are simulated based on fracture length, dip, aperture, height and so on. The interesting area of Wumishan Formation of Renqiu buried hill reservoir is about 57 square kilometer and the thickness of target strata is more than 2000 meters. In addition with great fracture density, the fracture simulation and upscaling of discrete fracture network model of Wumishan Formation are very intense computing. In order to solve this problem, a method of equivalent fracture modeling is proposed. First of all, taking the fracture interpretation data obtained from imaging logging and conventional logging as the basic data, establish the reservoir level model, and then under the constraint of reservoir level model, take fault distance analysis model as the second variable, establish fracture density model by Sequential Gaussian Simulation method. Increasing the width, height and length of fracture, at the same time decreasing its density in order to keep the similar porosity and permeability after upscaling discrete fracture network model. In this way, the fracture model of whole interesting area can be built within an accepted time.

Taylor O(h³) Discretization of ZNN Models for Dynamic Equality-Constrained Quadratic Programming With Application to Manipulators.

PubMed

Liao, Bolin; Zhang, Yunong; Jin, Long

2016-02-01

In this paper, a new Taylor-type numerical differentiation formula is first presented to discretize the continuous-time Zhang neural network (ZNN), and obtain higher computational accuracy. Based on the Taylor-type formula, two Taylor-type discrete-time ZNN models (termed Taylor-type discrete-time ZNNK and Taylor-type discrete-time ZNNU models) are then proposed and discussed to perform online dynamic equality-constrained quadratic programming. For comparison, Euler-type discrete-time ZNN models (called Euler-type discrete-time ZNNK and Euler-type discrete-time ZNNU models) and Newton iteration, with interesting links being found, are also presented. It is proved herein that the steady-state residual errors of the proposed Taylor-type discrete-time ZNN models, Euler-type discrete-time ZNN models, and Newton iteration have the patterns of O(h(3)), O(h(2)), and O(h), respectively, with h denoting the sampling gap. Numerical experiments, including the application examples, are carried out, of which the results further substantiate the theoretical findings and the efficacy of Taylor-type discrete-time ZNN models. Finally, the comparisons with Taylor-type discrete-time derivative model and other Lagrange-type discrete-time ZNN models for dynamic equality-constrained quadratic programming substantiate the superiority of the proposed Taylor-type discrete-time ZNN models once again.

Efficient computation of parameter sensitivities of discrete stochastic chemical reaction networks.

PubMed

Rathinam, Muruhan; Sheppard, Patrick W; Khammash, Mustafa

2010-01-21

Parametric sensitivity of biochemical networks is an indispensable tool for studying system robustness properties, estimating network parameters, and identifying targets for drug therapy. For discrete stochastic representations of biochemical networks where Monte Carlo methods are commonly used, sensitivity analysis can be particularly challenging, as accurate finite difference computations of sensitivity require a large number of simulations for both nominal and perturbed values of the parameters. In this paper we introduce the common random number (CRN) method in conjunction with Gillespie's stochastic simulation algorithm, which exploits positive correlations obtained by using CRNs for nominal and perturbed parameters. We also propose a new method called the common reaction path (CRP) method, which uses CRNs together with the random time change representation of discrete state Markov processes due to Kurtz to estimate the sensitivity via a finite difference approximation applied to coupled reaction paths that emerge naturally in this representation. While both methods reduce the variance of the estimator significantly compared to independent random number finite difference implementations, numerical evidence suggests that the CRP method achieves a greater variance reduction. We also provide some theoretical basis for the superior performance of CRP. The improved accuracy of these methods allows for much more efficient sensitivity estimation. In two example systems reported in this work, speedup factors greater than 300 and 10,000 are demonstrated.

On an LAS-integrated soft PLC system based on WorldFIP fieldbus.

PubMed

Liang, Geng; Li, Zhijun; Li, Wen; Bai, Yan

2012-01-01

Communication efficiency is lowered and real-time performance is not good enough in discrete control based on traditional WorldFIP field intelligent nodes in case that the scale of control in field is large. A soft PLC system based on WorldFIP fieldbus was designed and implemented. Link Activity Scheduler (LAS) was integrated into the system and field intelligent I/O modules acted as networked basic nodes. Discrete control logic was implemented with the LAS-integrated soft PLC system. The proposed system was composed of configuration and supervisory sub-systems and running sub-systems. The configuration and supervisory sub-system was implemented with a personal computer or an industrial personal computer; running subsystems were designed and implemented based on embedded hardware and software systems. Communication and schedule in the running subsystem was implemented with an embedded sub-module; discrete control and system self-diagnosis were implemented with another embedded sub-module. Structure of the proposed system was presented. Methodology for the design of the sub-systems was expounded. Experiments were carried out to evaluate the performance of the proposed system both in discrete and process control by investigating the effect of network data transmission delay induced by the soft PLC in WorldFIP network and CPU workload on resulting control performances. The experimental observations indicated that the proposed system is practically applicable. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Multisource Data Classification Using A Hybrid Semi-supervised Learning Scheme

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vatsavai, Raju; Bhaduri, Budhendra L; Shekhar, Shashi

2009-01-01

In many practical situations thematic classes can not be discriminated by spectral measurements alone. Often one needs additional features such as population density, road density, wetlands, elevation, soil types, etc. which are discrete attributes. On the other hand remote sensing image features are continuous attributes. Finding a suitable statistical model and estimation of parameters is a challenging task in multisource (e.g., discrete and continuous attributes) data classification. In this paper we present a semi-supervised learning method by assuming that the samples were generated by a mixture model, where each component could be either a continuous or discrete distribution. Overall classificationmore » accuracy of the proposed method is improved by 12% in our initial experiments.« less

Incorporating environmental justice measures into equilibrium-based transportation network design models

DOT National Transportation Integrated Search

2007-08-01

This research outlines three major challenges of incorporating Environmental Justice (EJ) into metropolitan transportation planning and proposes a new variation of the user equilibrium discrete network design problem (UEDNDP) for achieving EJ amongst...

Output synchronization of discrete-time dynamical networks based on geometrically incremental dissipativity.

PubMed

Li, Chensong; Zhao, Jun

2017-01-01

In this work, we investigate the output synchronization problem for discrete-time dynamical networks with identical nodes. Firstly, if each node of a network is geometrically incrementally dissipative, the entire network can be viewed as a geometrically dissipative nonlinear system by choosing a particular input-output pair. Then, based on the geometrical dissipativity property, we consider two cases: output synchronization under arbitrary topology and switching topology, respectively. For the first case, we establish several criteria of output synchronization under arbitrary switching between a set of connection topologies by employing a common Lyapunov function. For the other case, we give the design method of a switching signal to achieve output synchronization even if all subnetworks are not synchronous. Finally, an example is provided to illustrate the effectiveness of the main results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Event-triggered H∞ state estimation for semi-Markov jumping discrete-time neural networks with quantization.

PubMed

Rakkiyappan, R; Maheswari, K; Velmurugan, G; Park, Ju H

2018-05-17

This paper investigates H ∞ state estimation problem for a class of semi-Markovian jumping discrete-time neural networks model with event-triggered scheme and quantization. First, a new event-triggered communication scheme is introduced to determine whether or not the current sampled sensor data should be broad-casted and transmitted to the quantizer, which can save the limited communication resource. Second, a novel communication framework is employed by the logarithmic quantizer that quantifies and reduces the data transmission rate in the network, which apparently improves the communication efficiency of networks. Third, a stabilization criterion is derived based on the sufficient condition which guarantees a prescribed H ∞ performance level in the estimation error system in terms of the linear matrix inequalities. Finally, numerical simulations are given to illustrate the correctness of the proposed scheme. Copyright © 2018 Elsevier Ltd. All rights reserved.

Signal processing method and system for noise removal and signal extraction

DOEpatents

Fu, Chi Yung; Petrich, Loren

2009-04-14

A signal processing method and system combining smooth level wavelet pre-processing together with artificial neural networks all in the wavelet domain for signal denoising and extraction. Upon receiving a signal corrupted with noise, an n-level decomposition of the signal is performed using a discrete wavelet transform to produce a smooth component and a rough component for each decomposition level. The n.sup.th level smooth component is then inputted into a corresponding neural network pre-trained to filter out noise in that component by pattern recognition in the wavelet domain. Additional rough components, beginning at the highest level, may also be retained and inputted into corresponding neural networks pre-trained to filter out noise in those components also by pattern recognition in the wavelet domain. In any case, an inverse discrete wavelet transform is performed on the combined output from all the neural networks to recover a clean signal back in the time domain.

Parallel discrete-event simulation of FCFS stochastic queueing networks

NASA Technical Reports Server (NTRS)

Nicol, David M.

1988-01-01

Physical systems are inherently parallel. Intuition suggests that simulations of these systems may be amenable to parallel execution. The parallel execution of a discrete-event simulation requires careful synchronization of processes in order to ensure the execution's correctness; this synchronization can degrade performance. Largely negative results were recently reported in a study which used a well-known synchronization method on queueing network simulations. Discussed here is a synchronization method (appointments), which has proven itself to be effective on simulations of FCFS queueing networks. The key concept behind appointments is the provision of lookahead. Lookahead is a prediction on a processor's future behavior, based on an analysis of the processor's simulation state. It is shown how lookahead can be computed for FCFS queueing network simulations, give performance data that demonstrates the method's effectiveness under moderate to heavy loads, and discuss performance tradeoffs between the quality of lookahead, and the cost of computing lookahead.

An opinion-driven behavioral dynamics model for addictive behaviors

DOE PAGES

Moore, Thomas W.; Finley, Patrick D.; Apelberg, Benjamin J.; ...

2015-04-08

We present a model of behavioral dynamics that combines a social network-based opinion dynamics model with behavioral mapping. The behavioral component is discrete and history-dependent to represent situations in which an individual’s behavior is initially driven by opinion and later constrained by physiological or psychological conditions that serve to maintain the behavior. Additionally, individuals are modeled as nodes in a social network connected by directed edges. Parameter sweeps illustrate model behavior and the effects of individual parameters and parameter interactions on model results. Mapping a continuous opinion variable into a discrete behavioral space induces clustering on directed networks. Clusters providemore » targets of opportunity for influencing the network state; however, the smaller the network the greater the stochasticity and potential variability in outcomes. Furthermore, this has implications both for behaviors that are influenced by close relationships verses those influenced by societal norms and for the effectiveness of strategies for influencing those behaviors.« less

Predictions of first passage times in sparse discrete fracture networks using graph-based reductions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hyman, Jeffrey De'Haven; Hagberg, Aric Arild; Mohd-Yusof, Jamaludin

Here, we present a graph-based methodology to reduce the computational cost of obtaining first passage times through sparse fracture networks. We also derive graph representations of generic three-dimensional discrete fracture networks (DFNs) using the DFN topology and flow boundary conditions. Subgraphs corresponding to the union of the k shortest paths between the inflow and outflow boundaries are identified and transport on their equivalent subnetworks is compared to transport through the full network. The number of paths included in the subgraphs is based on the scaling behavior of the number of edges in the graph with the number of shortest paths.more » First passage times through the subnetworks are in good agreement with those obtained in the full network, both for individual realizations and in distribution. We obtain accurate estimates of first passage times with an order of magnitude reduction of CPU time and mesh size using the proposed method.« less

Predictions of first passage times in sparse discrete fracture networks using graph-based reductions

DOE PAGES

Hyman, Jeffrey De'Haven; Hagberg, Aric Arild; Mohd-Yusof, Jamaludin; ...

2017-07-10

Here, we present a graph-based methodology to reduce the computational cost of obtaining first passage times through sparse fracture networks. We also derive graph representations of generic three-dimensional discrete fracture networks (DFNs) using the DFN topology and flow boundary conditions. Subgraphs corresponding to the union of the k shortest paths between the inflow and outflow boundaries are identified and transport on their equivalent subnetworks is compared to transport through the full network. The number of paths included in the subgraphs is based on the scaling behavior of the number of edges in the graph with the number of shortest paths.more » First passage times through the subnetworks are in good agreement with those obtained in the full network, both for individual realizations and in distribution. We obtain accurate estimates of first passage times with an order of magnitude reduction of CPU time and mesh size using the proposed method.« less

Dynamic modeling and parameter estimation of a radial and loop type distribution system network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jun Qui; Heng Chen; Girgis, A.A.

1993-05-01

This paper presents a new identification approach to three-phase power system modeling and model reduction taking power system network as multi-input, multi-output (MIMO) processes. The model estimate can be obtained in discrete-time input-output form, discrete- or continuous-time state-space variable form, or frequency-domain impedance transfer function matrix form. An algorithm for determining the model structure of this MIMO process is described. The effect of measurement noise on the approach is also discussed. This approach has been applied on a sample system and simulation results are also presented in this paper.

Defeaturing CAD models using a geometry-based size field and facet-based reduction operators.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Quadros, William Roshan; Owen, Steven James

2010-04-01

We propose a method to automatically defeature a CAD model by detecting irrelevant features using a geometry-based size field and a method to remove the irrelevant features via facet-based operations on a discrete representation. A discrete B-Rep model is first created by obtaining a faceted representation of the CAD entities. The candidate facet entities are then marked for reduction by using a geometry-based size field. This is accomplished by estimating local mesh sizes based on geometric criteria. If the field value at a facet entity goes below a user specified threshold value then it is identified as an irrelevant featuremore » and is marked for reduction. The reduction of marked facet entities is primarily performed using an edge collapse operator. Care is taken to retain a valid geometry and topology of the discrete model throughout the procedure. The original model is not altered as the defeaturing is performed on a separate discrete model. Associativity between the entities of the discrete model and that of original CAD model is maintained in order to decode the attributes and boundary conditions applied on the original CAD entities onto the mesh via the entities of the discrete model. Example models are presented to illustrate the effectiveness of the proposed approach.« less

Automated detection of Schlemm's canal in spectral-domain optical coherence tomography

NASA Astrophysics Data System (ADS)

Tom, Manu; Ramakrishnan, Vignesh; van Oterendorp, Christian; Deserno, Thomas M.

2015-03-01

Recent advances in optical coherence tomography (OCT) technology allow in vivo imaging of the complex network of intra-scleral aqueous veins in the anterior segment of the eye. Pathological changes in this network, draining the aqueous humor from the eye, are considered to play a role in intraocular pressure elevation, which can lead to glaucoma, one of the major causes of blindness in the world. Through acquisition of OCT volume scans of the anterior eye segment, we aim at reconstructing the three dimensional network of aqueous veins in healthy and glaucomatous subjects. A novel algorithm for segmentation of the three-dimensional (3D) vessel system in human Schlemms canal is presented analyzing frames of spectral domain OCT (SD-OCT) of the eyes surface in either horizontal or vertical orientation. Distortions such as vertical stripes are caused by the superficial blood vessels in the conjunctiva and the episclera. They are removed in the discrete Fourier domain (DFT) masking particular frequencies. Feature-based rigid registration of these noise-filtered images is then performed using the scale invariant feature transform (SIFT). Segmentation of the vessels deep in the sclera originating at or in the vicinity of or having indirect connection to the Schlemm's canal is then performed with 3D region growing technique. The segmented vessels are visualized in 3D providing diagnostically relevant information to the physicians. A proof-of-concept study was performed on a healthy volunteer before and after a pharmaceutical narrowing of Schlemm's canal. A relative decreases 17% was measured based on manual ground truth and the image processing method.

PetriScape - A plugin for discrete Petri net simulations in Cytoscape.

PubMed

Almeida, Diogo; Azevedo, Vasco; Silva, Artur; Baumbach, Jan

2016-06-04

Systems biology plays a central role for biological network analysis in the post-genomic era. Cytoscape is the standard bioinformatics tool offering the community an extensible platform for computational analysis of the emerging cellular network together with experimental omics data sets. However, only few apps/plugins/tools are available for simulating network dynamics in Cytoscape 3. Many approaches of varying complexity exist but none of them have been integrated into Cytoscape as app/plugin yet. Here, we introduce PetriScape, the first Petri net simulator for Cytoscape. Although discrete Petri nets are quite simplistic models, they are capable of modeling global network properties and simulating their behaviour. In addition, they are easily understood and well visualizable. PetriScape comes with the following main functionalities: (1) import of biological networks in SBML format, (2) conversion into a Petri net, (3) visualization as Petri net, and (4) simulation and visualization of the token flow in Cytoscape. PetriScape is the first Cytoscape plugin for Petri nets. It allows a straightforward Petri net model creation, simulation and visualization with Cytoscape, providing clues about the activity of key components in biological networks.

PetriScape - A plugin for discrete Petri net simulations in Cytoscape.

PubMed

Almeida, Diogo; Azevedo, Vasco; Silva, Artur; Baumbach, Jan

2016-03-01

Systems biology plays a central role for biological network analysis in the post-genomic era. Cytoscape is the standard bioinformatics tool offering the community an extensible platform for computational analysis of the emerging cellular network together with experimental omics data sets. However, only few apps/plugins/tools are available for simulating network dynamics in Cytoscape 3. Many approaches of varying complexity exist but none of them have been integrated into Cytoscape as app/plugin yet. Here, we introduce PetriScape, the first Petri net simulator for Cytoscape. Although discrete Petri nets are quite simplistic models, they are capable of modeling global network properties and simulating their behaviour. In addition, they are easily understood and well visualizable. PetriScape comes with the following main functionalities: (1) import of biological networks in SBML format, (2) conversion into a Petri net, (3) visualization as Petri net, and (4) simulation and visualization of the token flow in Cytoscape. PetriScape is the first Cytoscape plugin for Petri nets. It allows a straightforward Petri net model creation, simulation and visualization with Cytoscape, providing clues about the activity of key components in biological networks.

Progress on Discrete Fracture Network models with implications on the predictions of permeability and flow channeling structure

NASA Astrophysics Data System (ADS)

Darcel, C.; Davy, P.; Le Goc, R.; Maillot, J.; Selroos, J. O.

2017-12-01

We present progress on Discrete Fracture Network (DFN) flow modeling, including realistic advanced DFN spatial structures and local fracture transmissivity properties, through an application to the Forsmark site in Sweden. DFN models are a framework to combine fracture datasets from different sources and scales and to interpolate them in combining statistical distributions and stereological relations. The resulting DFN upscaling function - size density distribution - is a model component key to extrapolating fracture size densities between data gaps, from borehole core up to site scale. Another important feature of DFN models lays in the spatial correlations between fractures, with still unevaluated consequences on flow predictions. Indeed, although common Poisson (i.e. spatially random) models are widely used, they do not reflect these geological evidences for more complex structures. To model them, we define a DFN growth process from kinematic rules for nucleation, growth and stopping conditions. It mimics in a simplified way the geological fracturing processes and produces DFN characteristics -both upscaling function and spatial correlations- fully consistent with field observations. DFN structures are first compared for constant transmissivities. Flow simulations for the kinematic and equivalent Poisson DFN models show striking differences: with the kinematic DFN, connectivity and permeability are significantly smaller, down to a difference of one order of magnitude, and flow is much more channelized. Further flow analyses are performed with more realistic transmissivity distribution conditions (sealed parts, relations to fracture sizes, orientations and in-situ stress field). The relative importance of the overall DFN structure in the final flow predictions is discussed.

Delay-dependent dynamical analysis of complex-valued memristive neural networks: Continuous-time and discrete-time cases.

PubMed

Wang, Jinling; Jiang, Haijun; Ma, Tianlong; Hu, Cheng

2018-05-01

This paper considers the delay-dependent stability of memristive complex-valued neural networks (MCVNNs). A novel linear mapping function is presented to transform the complex-valued system into the real-valued system. Under such mapping function, both continuous-time and discrete-time MCVNNs are analyzed in this paper. Firstly, when activation functions are continuous but not Lipschitz continuous, an extended matrix inequality is proved to ensure the stability of continuous-time MCVNNs. Furthermore, if activation functions are discontinuous, a discontinuous adaptive controller is designed to acquire its stability by applying Lyapunov-Krasovskii functionals. Secondly, compared with techniques in continuous-time MCVNNs, the Halanay-type inequality and comparison principle are firstly used to exploit the dynamical behaviors of discrete-time MCVNNs. Finally, the effectiveness of theoretical results is illustrated through numerical examples. Copyright © 2018 Elsevier Ltd. All rights reserved.

Surrogate Modeling of High-Fidelity Fracture Simulations for Real-Time Residual Strength Predictions

NASA Technical Reports Server (NTRS)

Spear, Ashley D.; Priest, Amanda R.; Veilleux, Michael G.; Ingraffea, Anthony R.; Hochhalter, Jacob D.

2011-01-01

A surrogate model methodology is described for predicting in real time the residual strength of flight structures with discrete-source damage. Starting with design of experiment, an artificial neural network is developed that takes as input discrete-source damage parameters and outputs a prediction of the structural residual strength. Target residual strength values used to train the artificial neural network are derived from 3D finite element-based fracture simulations. A residual strength test of a metallic, integrally-stiffened panel is simulated to show that crack growth and residual strength are determined more accurately in discrete-source damage cases by using an elastic-plastic fracture framework rather than a linear-elastic fracture mechanics-based method. Improving accuracy of the residual strength training data would, in turn, improve accuracy of the surrogate model. When combined, the surrogate model methodology and high-fidelity fracture simulation framework provide useful tools for adaptive flight technology.

Adaptive NN control for discrete-time pure-feedback systems with unknown control direction under amplitude and rate actuator constraints.

PubMed

Chen, Weisheng

2009-07-01

This paper focuses on the problem of adaptive neural network tracking control for a class of discrete-time pure-feedback systems with unknown control direction under amplitude and rate actuator constraints. Two novel state-feedback and output-feedback dynamic control laws are established where the function tanh(.) is employed to solve the saturation constraint problem. Implicit function theorem and mean value theorem are exploited to deal with non-affine variables that are used as actual control. Radial basis function neural networks are used to approximate the desired input function. Discrete Nussbaum gain is used to estimate the unknown sign of control gain. The uniform boundedness of all closed-loop signals is guaranteed. The tracking error is proved to converge to a small residual set around the origin. A simulation example is provided to illustrate the effectiveness of control schemes proposed in this paper.

Models for discrete-time self-similar vector processes with application to network traffic

NASA Astrophysics Data System (ADS)

Lee, Seungsin; Rao, Raghuveer M.; Narasimha, Rajesh

2003-07-01

The paper defines self-similarity for vector processes by employing the discrete-time continuous-dilation operation which has successfully been used previously by the authors to define 1-D discrete-time stochastic self-similar processes. To define self-similarity of vector processes, it is required to consider the cross-correlation functions between different 1-D processes as well as the autocorrelation function of each constituent 1-D process in it. System models to synthesize self-similar vector processes are constructed based on the definition. With these systems, it is possible to generate self-similar vector processes from white noise inputs. An important aspect of the proposed models is that they can be used to synthesize various types of self-similar vector processes by choosing proper parameters. Additionally, the paper presents evidence of vector self-similarity in two-channel wireless LAN data and applies the aforementioned systems to simulate the corresponding network traffic traces.

Surrogate Modeling of High-Fidelity Fracture Simulations for Real-Time Residual Strength Predictions

NASA Technical Reports Server (NTRS)

Spear, Ashley D.; Priest, Amanda R.; Veilleux, Michael G.; Ingraffea, Anthony R.; Hochhalter, Jacob D.

2011-01-01

A surrogate model methodology is described for predicting, during flight, the residual strength of aircraft structures that sustain discrete-source damage. Starting with design of experiment, an artificial neural network is developed that takes as input discrete-source damage parameters and outputs a prediction of the structural residual strength. Target residual strength values used to train the artificial neural network are derived from 3D finite element-based fracture simulations. Two ductile fracture simulations are presented to show that crack growth and residual strength are determined more accurately in discrete-source damage cases by using an elastic-plastic fracture framework rather than a linear-elastic fracture mechanics-based method. Improving accuracy of the residual strength training data does, in turn, improve accuracy of the surrogate model. When combined, the surrogate model methodology and high fidelity fracture simulation framework provide useful tools for adaptive flight technology.

Finite time synchronization of memristor-based Cohen-Grossberg neural networks with mixed delays.

PubMed

Chen, Chuan; Li, Lixiang; Peng, Haipeng; Yang, Yixian

2017-01-01

Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don't include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs) with both discrete delay and distributed delay (mixed delays). By means of a simple feedback controller and novel finite time synchronization analysis methods, several new criteria are derived to ensure the finite time synchronization of MCGNNs with mixed delays. The obtained criteria are very concise and easy to verify. Numerical simulations are presented to demonstrate the effectiveness of our theoretical results.

Indirect iterative learning control for a discrete visual servo without a camera-robot model.

PubMed

Jiang, Ping; Bamforth, Leon C A; Feng, Zuren; Baruch, John E F; Chen, YangQuan

2007-08-01

This paper presents a discrete learning controller for vision-guided robot trajectory imitation with no prior knowledge of the camera-robot model. A teacher demonstrates a desired movement in front of a camera, and then, the robot is tasked to replay it by repetitive tracking. The imitation procedure is considered as a discrete tracking control problem in the image plane, with an unknown and time-varying image Jacobian matrix. Instead of updating the control signal directly, as is usually done in iterative learning control (ILC), a series of neural networks are used to approximate the unknown Jacobian matrix around every sample point in the demonstrated trajectory, and the time-varying weights of local neural networks are identified through repetitive tracking, i.e., indirect ILC. This makes repetitive segmented training possible, and a segmented training strategy is presented to retain the training trajectories solely within the effective region for neural network approximation. However, a singularity problem may occur if an unmodified neural-network-based Jacobian estimation is used to calculate the robot end-effector velocity. A new weight modification algorithm is proposed which ensures invertibility of the estimation, thus circumventing the problem. Stability is further discussed, and the relationship between the approximation capability of the neural network and the tracking accuracy is obtained. Simulations and experiments are carried out to illustrate the validity of the proposed controller for trajectory imitation of robot manipulators with unknown time-varying Jacobian matrices.

Cone penetrometer testing and discrete-depth ground water sampling techniques: A cost-effective method of site characterization in a multiple-aquifer setting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zemo, D.A.; Pierce, Y.G.; Gallinatti, J.D.

Cone penetrometer testing (CPT), combined with discrete-depth ground water sampling methods, can significantly reduce the time and expense required to characterize large sites that have multiple aquifers. Results from the screening site characterization can then be used to design and install a cost-effective monitoring well network. At a site in northern California, it was necessary to characterize the stratigraphy and the distribution of volatile organic compounds (VOCs). To expedite characterization, a five-week field screening program was implemented that consisted of a shallow ground water survey, CPT soundings and pore-pressure measurements, and discrete-depth ground water sampling. Based on continuous lithologic informationmore » provided by the CPT soundings, four predominantly coarse-grained, water yielding stratigraphic packages were identified. Seventy-nine discrete-depth ground water samples were collected using either shallow ground water survey techniques, the BAT Enviroprobe, or the QED HydroPunch I, depending on subsurface conditions. Using results from these efforts, a 20-well monitoring network was designed and installed to monitor critical points within each stratigraphic package. Good correlation was found for hydraulic head and chemical results between discrete-depth screening data and monitoring well data. Understanding the vertical VOC distribution and concentrations produced substantial time and cost savings by minimizing the number of permanent monitoring wells and reducing the number of costly conductor casings that had to be installed. Additionally, significant long-term cost savings will result from reduced sampling costs, because fewer wells comprise the monitoring network. The authors estimate these savings to be 50% for site characterization costs, 65% for site characterization time, and 60% for long-term monitoring costs.« less

ADAM: analysis of discrete models of biological systems using computer algebra.

PubMed

Hinkelmann, Franziska; Brandon, Madison; Guang, Bonny; McNeill, Rustin; Blekherman, Grigoriy; Veliz-Cuba, Alan; Laubenbacher, Reinhard

2011-07-20

Many biological systems are modeled qualitatively with discrete models, such as probabilistic Boolean networks, logical models, Petri nets, and agent-based models, to gain a better understanding of them. The computational complexity to analyze the complete dynamics of these models grows exponentially in the number of variables, which impedes working with complex models. There exist software tools to analyze discrete models, but they either lack the algorithmic functionality to analyze complex models deterministically or they are inaccessible to many users as they require understanding the underlying algorithm and implementation, do not have a graphical user interface, or are hard to install. Efficient analysis methods that are accessible to modelers and easy to use are needed. We propose a method for efficiently identifying attractors and introduce the web-based tool Analysis of Dynamic Algebraic Models (ADAM), which provides this and other analysis methods for discrete models. ADAM converts several discrete model types automatically into polynomial dynamical systems and analyzes their dynamics using tools from computer algebra. Specifically, we propose a method to identify attractors of a discrete model that is equivalent to solving a system of polynomial equations, a long-studied problem in computer algebra. Based on extensive experimentation with both discrete models arising in systems biology and randomly generated networks, we found that the algebraic algorithms presented in this manuscript are fast for systems with the structure maintained by most biological systems, namely sparseness and robustness. For a large set of published complex discrete models, ADAM identified the attractors in less than one second. Discrete modeling techniques are a useful tool for analyzing complex biological systems and there is a need in the biological community for accessible efficient analysis tools. ADAM provides analysis methods based on mathematical algorithms as a web-based tool for several different input formats, and it makes analysis of complex models accessible to a larger community, as it is platform independent as a web-service and does not require understanding of the underlying mathematics.

Consensus Algorithms for Networks of Systems with Second- and Higher-Order Dynamics

NASA Astrophysics Data System (ADS)

Fruhnert, Michael

This thesis considers homogeneous networks of linear systems. We consider linear feedback controllers and require that the directed graph associated with the network contains a spanning tree and systems are stabilizable. We show that, in continuous-time, consensus with a guaranteed rate of convergence can always be achieved using linear state feedback. For networks of continuous-time second-order systems, we provide a new and simple derivation of the conditions for a second-order polynomials with complex coefficients to be Hurwitz. We apply this result to obtain necessary and sufficient conditions to achieve consensus with networks whose graph Laplacian matrix may have complex eigenvalues. Based on the conditions found, methods to compute feedback gains are proposed. We show that gains can be chosen such that consensus is achieved robustly over a variety of communication structures and system dynamics. We also consider the use of static output feedback. For networks of discrete-time second-order systems, we provide a new and simple derivation of the conditions for a second-order polynomials with complex coefficients to be Schur. We apply this result to obtain necessary and sufficient conditions to achieve consensus with networks whose graph Laplacian matrix may have complex eigenvalues. We show that consensus can always be achieved for marginally stable systems and discretized systems. Simple conditions for consensus achieving controllers are obtained when the Laplacian eigenvalues are all real. For networks of continuous-time time-variant higher-order systems, we show that uniform consensus can always be achieved if systems are quadratically stabilizable. In this case, we provide a simple condition to obtain a linear feedback control. For networks of discrete-time higher-order systems, we show that constant gains can be chosen such that consensus is achieved for a variety of network topologies. First, we develop simple results for networks of time-invariant systems and networks of time-variant systems that are given in controllable canonical form. Second, we formulate the problem in terms of Linear Matrix Inequalities (LMIs). The condition found simplifies the design process and avoids the parallel solution of multiple LMIs. The result yields a modified Algebraic Riccati Equation (ARE) for which we present an equivalent LMI condition.

Algorithms for optimization of branching gravity-driven water networks

NASA Astrophysics Data System (ADS)

Dardani, Ian; Jones, Gerard F.

2018-05-01

The design of a water network involves the selection of pipe diameters that satisfy pressure and flow requirements while considering cost. A variety of design approaches can be used to optimize for hydraulic performance or reduce costs. To help designers select an appropriate approach in the context of gravity-driven water networks (GDWNs), this work assesses three cost-minimization algorithms on six moderate-scale GDWN test cases. Two algorithms, a backtracking algorithm and a genetic algorithm, use a set of discrete pipe diameters, while a new calculus-based algorithm produces a continuous-diameter solution which is mapped onto a discrete-diameter set. The backtracking algorithm finds the global optimum for all but the largest of cases tested, for which its long runtime makes it an infeasible option. The calculus-based algorithm's discrete-diameter solution produced slightly higher-cost results but was more scalable to larger network cases. Furthermore, the new calculus-based algorithm's continuous-diameter and mapped solutions provided lower and upper bounds, respectively, on the discrete-diameter global optimum cost, where the mapped solutions were typically within one diameter size of the global optimum. The genetic algorithm produced solutions even closer to the global optimum with consistently short run times, although slightly higher solution costs were seen for the larger network cases tested. The results of this study highlight the advantages and weaknesses of each GDWN design method including closeness to the global optimum, the ability to prune the solution space of infeasible and suboptimal candidates without missing the global optimum, and algorithm run time. We also extend an existing closed-form model of Jones (2011) to include minor losses and a more comprehensive two-part cost model, which realistically applies to pipe sizes that span a broad range typical of GDWNs of interest in this work, and for smooth and commercial steel roughness values.

Critical Node Location in De Bruijn Networks

DTIC Science & Technology

2016-10-01

transmitter tower, node 0000. Let us say we broadcast a signal at four discrete and incrementally higher power levels, 1 - 4. After each transmission , we...When deploying a wireless network, some highly desirable properties are (a) many short paths between any two nodes, and (b) relatively few edges. One...minimal. To deal with this problem, we propose the use of a special network structure - de Bruijn networks. When deploying a wireless network, some

Analysis of spike-wave discharges in rats using discrete wavelet transform.

PubMed

Ubeyli, Elif Derya; Ilbay, Gül; Sahin, Deniz; Ateş, Nurbay

2009-03-01

A feature is a distinctive or characteristic measurement, transform, structural component extracted from a segment of a pattern. Features are used to represent patterns with the goal of minimizing the loss of important information. The discrete wavelet transform (DWT) as a feature extraction method was used in representing the spike-wave discharges (SWDs) records of Wistar Albino Glaxo/Rijswijk (WAG/Rij) rats. The SWD records of WAG/Rij rats were decomposed into time-frequency representations using the DWT and the statistical features were calculated to depict their distribution. The obtained wavelet coefficients were used to identify characteristics of the signal that were not apparent from the original time domain signal. The present study demonstrates that the wavelet coefficients are useful in determining the dynamics in the time-frequency domain of SWD records.

DigitSeis: A New Digitization Software and its Application to the Harvard-Adam Dziewoński Observatory Collection

NASA Astrophysics Data System (ADS)

Bogiatzis, P.; Altoé, I. L.; Karamitrou, A.; Ishii, M.; Ishii, H.

2015-12-01

DigitSeis is a new open-source, interactive digitization software written in MATLAB that converts digital, raster images of analog seismograms to readily usable, discretized time series using image processing algorithms. DigitSeis automatically identifies and corrects for various geometrical distortions of seismogram images that are acquired through the original recording, storage, and scanning procedures. With human supervision, the software further identifies and classifies important features such as time marks and notes, corrects time-mark offsets from the main trace, and digitizes the combined trace with an analysis to obtain as accurate timing as possible. Although a large effort has been made to minimize the human input, DigitSeis provides interactive tools for challenging situations such as trace crossings and stains in the paper. The effectiveness of the software is demonstrated with the digitization of seismograms that are over half a century old from the Harvard-Adam Dziewoński observatory that is still in operation as a part of the Global Seismographic Network (station code HRV and network code IU). The spectral analysis of the digitized time series shows no spurious features that may be related to the occurrence of minute and hour marks. They also display signals associated with significant earthquakes, and a comparison of the spectrograms with modern recordings reveals similarities in the background noise.

Martian channels and valleys: Their characteristics, distribution, and age

USGS Publications Warehouse

Carr, M.H.; Clow, G.D.

1981-01-01

All Martian channels and valleys visible at a resolution of 125 to 300 meters between 65??N and 65??S were mapped at a scale of 1:5,000,000 and the maps then digitized. Correlations of valley presence with other surface features show that almost all valleys are in the old cratered terrain. preferentially in areas of low albedo, low violet/red ratios, and high elevation. The networks are open, the individual drainage basins are small relative to Earth, and large distances separate the basins, features which all suggest an immature drainage system. The simplest explanation of the correlations and the restriction of valley networks to old terrain is that the channels themselves are old, and that the climatic conditions necessary for their formation did not prevail for long after the decline in the cratering rate around 3.9 billion years ago. Two types of outflow channel are distinguished: unconfined, in which broad swaths of terrain are scoured, and confined, in which flow is restricted to discrete channels. The outflow channels have a wide range of ages and may form under present climatic conditions. Fretted channels are largely restrited to two latitude belts centered on 40??N and 45??S, where relatively rapid erosion along escarpments results from mass wasting. They probably form by enlargement of preexisting channels by escarpment retreat. ?? 1981.

Using Sorting Networks for Skill Building and Reasoning

ERIC Educational Resources Information Center

Andre, Robert; Wiest, Lynda R.

2007-01-01

Sorting networks, used in graph theory, have instructional value as a skill- building tool as well as an interesting exploration in discrete mathematics. Students can practice mathematics facts and develop reasoning and logic skills with this topic. (Contains 4 figures.)

DISCRETE VOLUME-ELEMENT METHOD FOR NETWORK WATER- QUALITY MODELS

EPA Science Inventory

An explicit dynamic water-quality modeling algorithm is developed for tracking dissolved substances in water-distribution networks. The algorithm is based on a mass-balance relation within pipes that considers both advective transport and reaction kinetics. Complete mixing of m...

Sorption of CO 2 in a hydrogen-bonded diamondoid network of sulfonylcalix[4]arene

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sinnwell, Michael A.; Atwood, Jerry L.; Thallapally, Praveen K.

An organic material, p-tert-butyltetrasulfonylcalix[4]arene, self-assembles via hydrogen bonding to form a diamondoid supramolecular network. Possessing discrete, zero-dimensional (0D) microcavities, the thiacalixarene derivative adsorbs CO2 at high pressures

Random discrete linear canonical transform.

PubMed

Wei, Deyun; Wang, Ruikui; Li, Yuan-Min

2016-12-01

Linear canonical transforms (LCTs) are a family of integral transforms with wide applications in optical, acoustical, electromagnetic, and other wave propagation problems. In this paper, we propose the random discrete linear canonical transform (RDLCT) by randomizing the kernel transform matrix of the discrete linear canonical transform (DLCT). The RDLCT inherits excellent mathematical properties from the DLCT along with some fantastic features of its own. It has a greater degree of randomness because of the randomization in terms of both eigenvectors and eigenvalues. Numerical simulations demonstrate that the RDLCT has an important feature that the magnitude and phase of its output are both random. As an important application of the RDLCT, it can be used for image encryption. The simulation results demonstrate that the proposed encryption method is a security-enhanced image encryption scheme.

Does expert knowledge improve automatic probabilistic classification of gait joint motion patterns in children with cerebral palsy?

PubMed Central

Papageorgiou, Eirini; Nieuwenhuys, Angela; Desloovere, Kaat

2017-01-01

Background This study aimed to improve the automatic probabilistic classification of joint motion gait patterns in children with cerebral palsy by using the expert knowledge available via a recently developed Delphi-consensus study. To this end, this study applied both Naïve Bayes and Logistic Regression classification with varying degrees of usage of the expert knowledge (expert-defined and discretized features). A database of 356 patients and 1719 gait trials was used to validate the classification performance of eleven joint motions. Hypotheses Two main hypotheses stated that: (1) Joint motion patterns in children with CP, obtained through a Delphi-consensus study, can be automatically classified following a probabilistic approach, with an accuracy similar to clinical expert classification, and (2) The inclusion of clinical expert knowledge in the selection of relevant gait features and the discretization of continuous features increases the performance of automatic probabilistic joint motion classification. Findings This study provided objective evidence supporting the first hypothesis. Automatic probabilistic gait classification using the expert knowledge available from the Delphi-consensus study resulted in accuracy (91%) similar to that obtained with two expert raters (90%), and higher accuracy than that obtained with non-expert raters (78%). Regarding the second hypothesis, this study demonstrated that the use of more advanced machine learning techniques such as automatic feature selection and discretization instead of expert-defined and discretized features can result in slightly higher joint motion classification performance. However, the increase in performance is limited and does not outweigh the additional computational cost and the higher risk of loss of clinical interpretability, which threatens the clinical acceptance and applicability. PMID:28570616

Photos of Measles and People with Measles

MedlinePlus

... children. Viewing discretion is advised. Measles Clinical Features Video CDC’s Dr. Raymond Strikas, MD, describes clinical features of measles infection. Video, 3:15 minutes Video transcript [1 page] Measles ...

Discrete rational and breather solution in the spatial discrete complex modified Korteweg-de Vries equation and continuous counterparts.

PubMed

Zhao, Hai-Qiong; Yu, Guo-Fu

2017-04-01

In this paper, a spatial discrete complex modified Korteweg-de Vries equation is investigated. The Lax pair, conservation laws, Darboux transformations, and breather and rational wave solutions to the semi-discrete system are presented. The distinguished feature of the model is that the discrete rational solution can possess new W-shape rational periodic-solitary waves that were not reported before. In addition, the first-order rogue waves reach peak amplitudes which are at least three times of the background amplitude, whereas their continuous counterparts are exactly three times the constant background. Finally, the integrability of the discrete system, including Lax pair, conservation laws, Darboux transformations, and explicit solutions, yields the counterparts of the continuous system in the continuum limit.

A Comparison of General Diagnostic Models (GDM) and Bayesian Networks Using a Middle School Mathematics Test

ERIC Educational Resources Information Center

Wu, Haiyan

2013-01-01

General diagnostic models (GDMs) and Bayesian networks are mathematical frameworks that cover a wide variety of psychometric models. Both extend latent class models, and while GDMs also extend item response theory (IRT) models, Bayesian networks can be parameterized using discretized IRT. The purpose of this study is to examine similarities and…

Loss of regional accent after damage to the speech production network.

PubMed

Berthier, Marcelo L; Dávila, Guadalupe; Moreno-Torres, Ignacio; Beltrán-Corbellini, Álvaro; Santana-Moreno, Daniel; Roé-Vellvé, Núria; Thurnhofer-Hemsi, Karl; Torres-Prioris, María José; Massone, María Ignacia; Ruiz-Cruces, Rafael

2015-01-01

Lesion-symptom mapping studies reveal that selective damage to one or more components of the speech production network can be associated with foreign accent syndrome, changes in regional accent (e.g., from Parisian accent to Alsatian accent), stronger regional accent, or re-emergence of a previously learned and dormant regional accent. Here, we report loss of regional accent after rapidly regressive Broca's aphasia in three Argentinean patients who had suffered unilateral or bilateral focal lesions in components of the speech production network. All patients were monolingual speakers with three different native Spanish accents (Cordobés or central, Guaranítico or northeast, and Bonaerense). Samples of speech production from the patient with native Córdoba accent were compared with previous recordings of his voice, whereas data from the patient with native Guaranítico accent were compared with speech samples from one healthy control matched for age, gender, and native accent. Speech samples from the patient with native Buenos Aires's accent were compared with data obtained from four healthy control subjects with the same accent. Analysis of speech production revealed discrete slowing in speech rate, inappropriate long pauses, and monotonous intonation. Phonemic production remained similar to those of healthy Spanish speakers, but phonetic variants peculiar to each accent (e.g., intervocalic aspiration of /s/ in Córdoba accent) were absent. While basic normal prosodic features of Spanish prosody were preserved, features intrinsic to melody of certain geographical areas (e.g., rising end F0 excursion in declarative sentences intoned with Córdoba accent) were absent. All patients were also unable to produce sentences with different emotional prosody. Brain imaging disclosed focal left hemisphere lesions involving the middle part of the motor cortex, the post-central cortex, the posterior inferior and/or middle frontal cortices, insula, anterior putamen and supplementary motor area. Our findings suggest that lesions affecting the middle part of the left motor cortex and other components of the speech production network disrupt neural processes involved in the production of regional accent features.

Loss of regional accent after damage to the speech production network

PubMed Central

Berthier, Marcelo L.; Dávila, Guadalupe; Moreno-Torres, Ignacio; Beltrán-Corbellini, Álvaro; Santana-Moreno, Daniel; Roé-Vellvé, Núria; Thurnhofer-Hemsi, Karl; Torres-Prioris, María José; Massone, María Ignacia; Ruiz-Cruces, Rafael

2015-01-01

Lesion-symptom mapping studies reveal that selective damage to one or more components of the speech production network can be associated with foreign accent syndrome, changes in regional accent (e.g., from Parisian accent to Alsatian accent), stronger regional accent, or re-emergence of a previously learned and dormant regional accent. Here, we report loss of regional accent after rapidly regressive Broca’s aphasia in three Argentinean patients who had suffered unilateral or bilateral focal lesions in components of the speech production network. All patients were monolingual speakers with three different native Spanish accents (Cordobés or central, Guaranítico or northeast, and Bonaerense). Samples of speech production from the patient with native Córdoba accent were compared with previous recordings of his voice, whereas data from the patient with native Guaranítico accent were compared with speech samples from one healthy control matched for age, gender, and native accent. Speech samples from the patient with native Buenos Aires’s accent were compared with data obtained from four healthy control subjects with the same accent. Analysis of speech production revealed discrete slowing in speech rate, inappropriate long pauses, and monotonous intonation. Phonemic production remained similar to those of healthy Spanish speakers, but phonetic variants peculiar to each accent (e.g., intervocalic aspiration of /s/ in Córdoba accent) were absent. While basic normal prosodic features of Spanish prosody were preserved, features intrinsic to melody of certain geographical areas (e.g., rising end F0 excursion in declarative sentences intoned with Córdoba accent) were absent. All patients were also unable to produce sentences with different emotional prosody. Brain imaging disclosed focal left hemisphere lesions involving the middle part of the motor cortex, the post-central cortex, the posterior inferior and/or middle frontal cortices, insula, anterior putamen and supplementary motor area. Our findings suggest that lesions affecting the middle part of the left motor cortex and other components of the speech production network disrupt neural processes involved in the production of regional accent features. PMID:26594161

Professional- Amateur Astronomer Partnerships in Scientific Research: The Re-emergence of Jupiter's 5-Micron Hot Spots

NASA Astrophysics Data System (ADS)

Yanamandra-Fisher, P. A.

2012-12-01

The night sky, with all its delights and mysteries, enthrall professional and amateur astronomers alike. The discrete data sets acquired by professional astronomers via their approved observing programs at various national facilities are supplemented by the nearly daily observations of the same celestial object by amateur astronomers around the world. The emerging partnerships between professional and dedicated amateur astronomers rely on creating a niche for long timeline of multispectral remote sensing. "Citizen Astronomy" can be thought of as the paradigm shift transforming the nature of observational astronomy. In the past decade, it is the collective observations and their analyses by the ever-increasing global network of amateur astronomers that has discovered interesting phenomena and provided the reference backdrop for observations by ground-based professional astronomers and spacecraft missions. We shall present results from our collaborations to observe the recent global upheaval on Jupiter for the past five years and illustrate the strong synergy between the two groups. Global upheavals on Jupiter involve changes in the albedo of entire axisymmetric regions, lasting several years, with the last two occurring in 1989 and 2006. Against this backdrop of planetary-scale changes, discrete features such as the Great Red Spot (GRS), and other vortices exhibit changes on shorter spatial- and time-scales. One set of features we are currently tracking is the variability of the discrete equatorial 5-μm hot spots, semi-evenly spaced in longitude and confined to a narrow latitude band centered at 6.5°N (southern edge of the North Equatorial Belt, NEB), abundant in Voyager images (1980-1981). Tantalizingly similar patterns were observed in the visible (bright plumes and blue-gray regions), where reflectivity in the red is anti-correlated with 5-μm thermal radiance. During the recent NEB fade (2011 - early 2012), however, these otherwise ubiquitous features were absent, an atmospheric state not seen in decades. The ongoing NEB revival indicates nascent 5-μm hot spots as early as April 2012, with corresponding visible dark spots. The South Equatorial Belt (SEB) and NEB revivals began similarly with an instability that developed into a major outbreak, and many similarities in the observed propagation of clear regions. With the active inclusion and use of emerging social media (Facebook, Twitter, etc.), the near daily communication and updates (via email, Skype, Facebook) between the professional and amateur astronomers is becoming a powerful tool for ground-based remote sensing. The archival of amateur data via global repositories such as Planetary Virtual Observatory and Laboratory (PVOL), The Association of Lunar and Planetary Observers (ALPO) and British Astronomical Association (BAA); and development of data reduction software, independent of professional astronomer community, provides an additional resource and dimension to scientific research. We shall present preliminary results that are the outcomes of the "Pro-Am" collaboration in the case of the re-emergence of Jupiter's 5-micron hot spots and highlight several members of our global amateur astronomer network.

Stability of discrete time recurrent neural networks and nonlinear optimization problems.

PubMed

Singh, Jayant; Barabanov, Nikita

2016-02-01

We consider the method of Reduction of Dissipativity Domain to prove global Lyapunov stability of Discrete Time Recurrent Neural Networks. The standard and advanced criteria for Absolute Stability of these essentially nonlinear systems produce rather weak results. The method mentioned above is proved to be more powerful. It involves a multi-step procedure with maximization of special nonconvex functions over polytopes on every step. We derive conditions which guarantee an existence of at most one point of local maximum for such functions over every hyperplane. This nontrivial result is valid for wide range of neuron transfer functions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Gaussian-modulated coherent-state measurement-device-independent quantum key distribution

NASA Astrophysics Data System (ADS)

Ma, Xiang-Chun; Sun, Shi-Hai; Jiang, Mu-Sheng; Gui, Ming; Liang, Lin-Mei

2014-04-01

Measurement-device-independent quantum key distribution (MDI-QKD), leaving the detection procedure to the third partner and thus being immune to all detector side-channel attacks, is very promising for the construction of high-security quantum information networks. We propose a scheme to implement MDI-QKD, but with continuous variables instead of discrete ones, i.e., with the source of Gaussian-modulated coherent states, based on the principle of continuous-variable entanglement swapping. This protocol not only can be implemented with current telecom components but also has high key rates compared to its discrete counterpart; thus it will be highly compatible with quantum networks.

Multilayer neural networks with extensively many hidden units.

PubMed

Rosen-Zvi, M; Engel, A; Kanter, I

2001-08-13

The information processing abilities of a multilayer neural network with a number of hidden units scaling as the input dimension are studied using statistical mechanics methods. The mapping from the input layer to the hidden units is performed by general symmetric Boolean functions, whereas the hidden layer is connected to the output by either discrete or continuous couplings. Introducing an overlap in the space of Boolean functions as order parameter, the storage capacity is found to scale with the logarithm of the number of implementable Boolean functions. The generalization behavior is smooth for continuous couplings and shows a discontinuous transition to perfect generalization for discrete ones.

SPIR: The potential spreaders involved SIR model for information diffusion in social networks

NASA Astrophysics Data System (ADS)

Rui, Xiaobin; Meng, Fanrong; Wang, Zhixiao; Yuan, Guan; Du, Changjiang

2018-09-01

The Susceptible-Infective-Removed (SIR) model is one of the most widely used models for the information diffusion research in social networks. Many researchers have devoted themselves to improving the classic SIR model in different aspects. However, on the one hand, the equations of these improved models are regarded as continuous functions, while the corresponding simulation experiments use discrete time, leading to the mismatch between numerical solutions got from mathematical method and experimental results obtained by simulating the spreading behaviour of each node. On the other hand, if the equations of these improved models are solved discretely, susceptible nodes will be calculated repeatedly, resulting in a big deviation from the actual value. In order to solve the above problem, this paper proposes a Susceptible-Potential-Infective-Removed (SPIR) model that analyses the diffusion process based on the discrete time according to simulation. Besides, this model also introduces a potential spreader set which solve the problem of repeated calculation effectively. To test the SPIR model, various experiments have been carried out from different angles on both artificial networks and real world networks. The Pearson correlation coefficient between numerical solutions of our SPIR equations and corresponding simulation results is mostly bigger than 0.95, which reveals that the proposed SPIR model is able to depict the information diffusion process with high accuracy.

Monitoring industrial facilities using principles of integration of fiber classifier and local sensor networks

NASA Astrophysics Data System (ADS)

Korotaev, Valery V.; Denisov, Victor M.; Rodrigues, Joel J. P. C.; Serikova, Mariya G.; Timofeev, Andrey V.

2015-05-01

The paper deals with the creation of integrated monitoring systems. They combine fiber-optic classifiers and local sensor networks. These systems allow for the monitoring of complex industrial objects. Together with adjacent natural objects, they form the so-called geotechnical systems. An integrated monitoring system may include one or more spatially continuous fiber-optic classifiers based on optic fiber and one or more arrays of discrete measurement sensors, which are usually combined in sensor networks. Fiber-optic classifiers are already widely used for the control of hazardous extended objects (oil and gas pipelines, railways, high-rise buildings, etc.). To monitor local objects, discrete measurement sensors are generally used (temperature, pressure, inclinometers, strain gauges, accelerometers, sensors measuring the composition of impurities in the air, and many others). However, monitoring complex geotechnical systems require a simultaneous use of continuous spatially distributed sensors based on fiber-optic cable and connected local discrete sensors networks. In fact, we are talking about integration of the two monitoring methods. This combination provides an additional way to create intelligent monitoring systems. Modes of operation of intelligent systems can automatically adapt to changing environmental conditions. For this purpose, context data received from one sensor (e.g., optical channel) may be used to change modes of work of other sensors within the same monitoring system. This work also presents experimental results of the prototype of the integrated monitoring system.

Reactanceless synthesized impedance bandpass amplifier

NASA Technical Reports Server (NTRS)

Kleinberg, L. L. (Inventor)

1985-01-01

An active R bandpass filter network is formed by four operational amplifier stages interconnected by discrete resistances. One pair of stages synthesize an equivalent input impedance of an inductance (L sub eq) in parallel with a discrete resistance (R sub o) while the second pair of stages synthesizes an equivalent input impedance of a capacitance (C sub eq) serially coupled to another discrete resistance (R sub i) coupled in parallel with the first two stages. The equivalent input impedances aggregately define a tuned resonant bandpass filter in the roll-off regions of the operational amplifiers.

Spiking neural network simulation: memory-optimal synaptic event scheduling.

PubMed

Stewart, Robert D; Gurney, Kevin N

2011-06-01

Spiking neural network simulations incorporating variable transmission delays require synaptic events to be scheduled prior to delivery. Conventional methods have memory requirements that scale with the total number of synapses in a network. We introduce novel scheduling algorithms for both discrete and continuous event delivery, where the memory requirement scales instead with the number of neurons. Superior algorithmic performance is demonstrated using large-scale, benchmarking network simulations.

An integrated workflow for stress and flow modelling using outcrop-derived discrete fracture networks

NASA Astrophysics Data System (ADS)

Bisdom, K.; Nick, H. M.; Bertotti, G.

2017-06-01

Fluid flow in naturally fractured reservoirs is often controlled by subseismic-scale fracture networks. Although the fracture network can be partly sampled in the direct vicinity of wells, the inter-well scale network is poorly constrained in fractured reservoir models. Outcrop analogues can provide data for populating domains of the reservoir model where no direct measurements are available. However, extracting relevant statistics from large outcrops representative of inter-well scale fracture networks remains challenging. Recent advances in outcrop imaging provide high-resolution datasets that can cover areas of several hundred by several hundred meters, i.e. the domain between adjacent wells, but even then, data from the high-resolution models is often upscaled to reservoir flow grids, resulting in loss of accuracy. We present a workflow that uses photorealistic georeferenced outcrop models to construct geomechanical and fluid flow models containing thousands of discrete fractures covering sufficiently large areas, that does not require upscaling to model permeability. This workflow seamlessly integrates geomechanical Finite Element models with flow models that take into account stress-sensitive fracture permeability and matrix flow to determine the full permeability tensor. The applicability of this workflow is illustrated using an outcropping carbonate pavement in the Potiguar basin in Brazil, from which 1082 fractures are digitised. The permeability tensor for a range of matrix permeabilities shows that conventional upscaling to effective grid properties leads to potential underestimation of the true permeability and the orientation of principal permeabilities. The presented workflow yields the full permeability tensor model of discrete fracture networks with stress-induced apertures, instead of relying on effective properties as most conventional flow models do.

Classification of crystal structure using a convolutional neural network

PubMed Central

Park, Woon Bae; Chung, Jiyong; Sohn, Keemin; Pyo, Myoungho

2017-01-01

A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds. PMID:28875035

Classification of crystal structure using a convolutional neural network.

PubMed

Park, Woon Bae; Chung, Jiyong; Jung, Jaeyoung; Sohn, Keemin; Singh, Satendra Pal; Pyo, Myoungho; Shin, Namsoo; Sohn, Kee-Sun

2017-07-01

A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds.

Aboriginal Australians' experience of social capital and its relevance to health and wellbeing in urban settings.

PubMed

Browne-Yung, Kathryn; Ziersch, Anna; Baum, Fran; Gallaher, Gilbert

2013-11-01

Social capital has been linked to physical and mental health. While definitions of social capital vary, all include networks of social relationships and refer to the subsequent benefits and disadvantages accrued to members. Research on social capital for Aboriginal Australians has mainly focused on discrete rural and remote Aboriginal contexts with less known about the features and health and other benefits of social capital in urban settings. This paper presents findings from in-depth interviews with 153 Aboriginal people living in urban areas on their experiences of social capital. Of particular interest was how engagement in bonding and bridging networks influenced health and wellbeing. Employing Bourdieu's relational theory of capital where resources are unequally distributed and reproduced in society we found that patterns of social capital are strongly associated with economic, social and cultural position which in turn reflects the historical experiences of dispossession and disadvantage experienced by Aboriginal Australians. Social capital was also found to both reinforce and influence Aboriginal cultural identity, and had both positive and negative impacts on health and wellbeing. Copyright © 2013 Elsevier Ltd. All rights reserved.

Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons

PubMed Central

Buesing, Lars; Bill, Johannes; Nessler, Bernhard; Maass, Wolfgang

2011-01-01

The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons. PMID:22096452

Numerical Experiments on Advective Transport in Large Three-Dimensional Discrete Fracture Networks

NASA Astrophysics Data System (ADS)

Makedonska, N.; Painter, S. L.; Karra, S.; Gable, C. W.

2013-12-01

Modeling of flow and solute transport in discrete fracture networks is an important approach for understanding the migration of contaminants in impermeable hard rocks such as granite, where fractures provide dominant flow and transport pathways. The discrete fracture network (DFN) model attempts to mimic discrete pathways for fluid flow through a fractured low-permeable rock mass, and may be combined with particle tracking simulations to address solute transport. However, experience has shown that it is challenging to obtain accurate transport results in three-dimensional DFNs because of the high computational burden and difficulty in constructing a high-quality unstructured computational mesh on simulated fractures. An integrated DFN meshing [1], flow, and particle tracking [2] simulation capability that enables accurate flow and particle tracking simulation on large DFNs has recently been developed. The new capability has been used in numerical experiments on advective transport in large DFNs with tens of thousands of fractures and millions of computational cells. The modeling procedure starts from the fracture network generation using a stochastic model derived from site data. A high-quality computational mesh is then generated [1]. Flow is then solved using the highly parallel PFLOTRAN [3] code. PFLOTRAN uses the finite volume approach, which is locally mass conserving and thus eliminates mass balance problems during particle tracking. The flow solver provides the scalar fluxes on each control volume face. From the obtained fluxes the Darcy velocity is reconstructed for each node in the network [4]. Velocities can then be continuously interpolated to any point in the domain of interest, thus enabling random walk particle tracking. In order to describe the flow field on fractures intersections, the control volume cells on intersections are split into four planar polygons, where each polygon corresponds to a piece of a fracture near the intersection line. Thus, computational nodes lying on fracture intersections have four associated velocities, one on each side of the intersection in each fracture plane [2]. This information is used to route particles arriving at the fracture intersection to the appropriate downstream fracture segment. Verified for small DFNs, the new simulation capability allows accurate particle tracking on more realistic representations of fractured rock sites. In the current work we focus on travel time statistics and spatial dispersion and show numerical results in DFNs of different sizes, fracture densities, and transmissivity distributions. [1] Hyman J.D., Gable C.W., Painter S.L., Automated meshing of stochastically generated discrete fracture networks, Abstract H33G-1403, 2011 AGU, San Francisco, CA, 5-9 Dec. [2] N. Makedonska, S. L. Painter, T.-L. Hsieh, Q.M. Bui, and C. W. Gable., Development and verification of a new particle tracking capability for modeling radionuclide transport in discrete fracture networks, Abstract, 2013 IHLRWM, Albuquerque, NM, Apr. 28 - May 3. [3] Lichtner, P.C., Hammond, G.E., Bisht, G., Karra, S., Mills, R.T., and Kumar, J. (2013) PFLOTRAN User's Manual: A Massively Parallel Reactive Flow Code. [4] Painter S.L., Gable C.W., Kelkar S., Pathline tracing on fully unstructured control-volume grids, Computational Geosciences, 16 (4), 2012, 1125-1134.

Discrete exterior calculus discretization of incompressible Navier-Stokes equations over surface simplicial meshes

NASA Astrophysics Data System (ADS)

Mohamed, Mamdouh S.; Hirani, Anil N.; Samtaney, Ravi

2016-05-01

A conservative discretization of incompressible Navier-Stokes equations is developed based on discrete exterior calculus (DEC). A distinguishing feature of our method is the use of an algebraic discretization of the interior product operator and a combinatorial discretization of the wedge product. The governing equations are first rewritten using the exterior calculus notation, replacing vector calculus differential operators by the exterior derivative, Hodge star and wedge product operators. The discretization is then carried out by substituting with the corresponding discrete operators based on the DEC framework. Numerical experiments for flows over surfaces reveal a second order accuracy for the developed scheme when using structured-triangular meshes, and first order accuracy for otherwise unstructured meshes. By construction, the method is conservative in that both mass and vorticity are conserved up to machine precision. The relative error in kinetic energy for inviscid flow test cases converges in a second order fashion with both the mesh size and the time step.

Discretized kinetic theory on scale-free networks

NASA Astrophysics Data System (ADS)

Bertotti, Maria Letizia; Modanese, Giovanni

2016-10-01

The network of interpersonal connections is one of the possible heterogeneous factors which affect the income distribution emerging from micro-to-macro economic models. In this paper we equip our model discussed in [1, 2] with a network structure. The model is based on a system of n differential equations of the kinetic discretized-Boltzmann kind. The network structure is incorporated in a probabilistic way, through the introduction of a link density P(α) and of correlation coefficients P(β|α), which give the conditioned probability that an individual with α links is connected to one with β links. We study the properties of the equations and give analytical results concerning the existence, normalization and positivity of the solutions. For a fixed network with P(α) = c/α q , we investigate numerically the dependence of the detailed and marginal equilibrium distributions on the initial conditions and on the exponent q. Our results are compatible with those obtained from the Bouchaud-Mezard model and from agent-based simulations, and provide additional information about the dependence of the individual income on the level of connectivity.

Discrete event command and control for networked teams with multiple missions

NASA Astrophysics Data System (ADS)

Lewis, Frank L.; Hudas, Greg R.; Pang, Chee Khiang; Middleton, Matthew B.; McMurrough, Christopher

2009-05-01

During mission execution in military applications, the TRADOC Pamphlet 525-66 Battle Command and Battle Space Awareness capabilities prescribe expectations that networked teams will perform in a reliable manner under changing mission requirements, varying resource availability and reliability, and resource faults. In this paper, a Command and Control (C2) structure is presented that allows for computer-aided execution of the networked team decision-making process, control of force resources, shared resource dispatching, and adaptability to change based on battlefield conditions. A mathematically justified networked computing environment is provided called the Discrete Event Control (DEC) Framework. DEC has the ability to provide the logical connectivity among all team participants including mission planners, field commanders, war-fighters, and robotic platforms. The proposed data management tools are developed and demonstrated on a simulation study and an implementation on a distributed wireless sensor network. The results show that the tasks of multiple missions are correctly sequenced in real-time, and that shared resources are suitably assigned to competing tasks under dynamically changing conditions without conflicts and bottlenecks.

Discrete-time systems with random switches: From systems stability to networks synchronization.

PubMed

Guo, Yao; Lin, Wei; Ho, Daniel W C

2016-03-01

In this article, we develop some approaches, which enable us to more accurately and analytically identify the essential patterns that guarantee the almost sure stability of discrete-time systems with random switches. We allow for the case that the elements in the switching connection matrix even obey some unbounded and continuous-valued distributions. In addition to the almost sure stability, we further investigate the almost sure synchronization in complex dynamical networks consisting of randomly connected nodes. Numerical examples illustrate that a chaotic dynamics in the synchronization manifold is preserved when statistical parameters enter some almost sure synchronization region established by the developed approach. Moreover, some delicate configurations are considered on probability space for ensuring synchronization in networks whose nodes are described by nonlinear maps. Both theoretical and numerical results on synchronization are presented by setting only a few random connections in each switch duration. More interestingly, we analytically find it possible to achieve almost sure synchronization in the randomly switching complex networks even with very large population sizes, which cannot be easily realized in non-switching but deterministically connected networks.

Discrete-time systems with random switches: From systems stability to networks synchronization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guo, Yao; Lin, Wei, E-mail: wlin@fudan.edu.cn; Shanghai Key Laboratory of Contemporary Applied Mathematics, LMNS, and Shanghai Center for Mathematical Sciences, Shanghai 200433

2016-03-15

In this article, we develop some approaches, which enable us to more accurately and analytically identify the essential patterns that guarantee the almost sure stability of discrete-time systems with random switches. We allow for the case that the elements in the switching connection matrix even obey some unbounded and continuous-valued distributions. In addition to the almost sure stability, we further investigate the almost sure synchronization in complex dynamical networks consisting of randomly connected nodes. Numerical examples illustrate that a chaotic dynamics in the synchronization manifold is preserved when statistical parameters enter some almost sure synchronization region established by the developedmore » approach. Moreover, some delicate configurations are considered on probability space for ensuring synchronization in networks whose nodes are described by nonlinear maps. Both theoretical and numerical results on synchronization are presented by setting only a few random connections in each switch duration. More interestingly, we analytically find it possible to achieve almost sure synchronization in the randomly switching complex networks even with very large population sizes, which cannot be easily realized in non-switching but deterministically connected networks.« less

A hybrid neural learning algorithm using evolutionary learning and derivative free local search method.

PubMed

Ghosh, Ranadhir; Yearwood, John; Ghosh, Moumita; Bagirov, Adil

2006-06-01

In this paper we investigate a hybrid model based on the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. Also we discuss different variants for hybrid models using the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. The Discrete Gradient method has the advantage of being able to jump over many local minima and find very deep local minima. However, earlier research has shown that a good starting point for the discrete gradient method can improve the quality of the solution point. Evolutionary algorithms are best suited for global optimisation problems. Nevertheless they are cursed with longer training times and often unsuitable for real world application. For optimisation problems such as weight optimisation for ANNs in real world applications the dimensions are large and time complexity is critical. Hence the idea of a hybrid model can be a suitable option. In this paper we propose different fusion strategies for hybrid models combining the evolutionary strategy with the discrete gradient method to obtain an optimal solution much quicker. Three different fusion strategies are discussed: a linear hybrid model, an iterative hybrid model and a restricted local search hybrid model. Comparative results on a range of standard datasets are provided for different fusion hybrid models.

Reliable classification of high explosive and chemical/biological artillery using acoustic sensors

NASA Astrophysics Data System (ADS)

Desai, Sachi V.; Hohil, Myron E.; Bass, Henry E.; Chambers, Jim

2005-05-01

Feature extraction methods based on the discrete wavelet transform and multiresolution analysis are used to develop a robust classification algorithm that reliably discriminates between conventional and simulated chemical/biological artillery rounds via acoustic signals produced during detonation utilizing a generic acoustic sensor. Based on the transient properties of the signature blast distinct characteristics arise within the different acoustic signatures because high explosive warheads emphasize concussive and shrapnel effects, while chemical/biological warheads are designed to disperse their contents over large areas, therefore employing a slower burning, less intense explosive to mix and spread their contents. The ensuing blast waves are readily characterized by variations in the corresponding peak pressure and rise time of the blast, differences in the ratio of positive pressure amplitude to the negative amplitude, and variations in the overall duration of the resulting waveform. Unique attributes can also be identified that depend upon the properties of the gun tube, projectile speed at the muzzle, and the explosive burn rates of the warhead. The algorithm enables robust classification of various airburst signatures using acoustics. It is capable of being integrated within an existing chemical/biological sensor, a stand-alone generic sensor, or a part of a disparate sensor suite. When emplaced in high-threat areas, this added capability would further provide field personal with advanced battlefield knowledge without the aide of so-called "sniffer" sensors that rely upon air particle information based on direct contact with possible contaminated air. In this work, the discrete wavelet transform is used to extract the predominant components of these characteristics from air burst signatures at ranges exceeding 2km while maintaining temporal sequence of the data to keep relevance to the transient differences of the airburst signatures. Highly reliable discrimination is achieved with a feedforward neural network classifier trained on a feature space derived from the distribution of wavelet coefficients and higher frequency details found within different levels of the multiresolution decomposition the neural network then is capable of classifying new airburst signatures as Chemical/Biological or High Explosive.

Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images

PubMed Central

Lahmiri, Salim; Boukadoum, Mounir

2013-01-01

A new methodology for automatic feature extraction from biomedical images and subsequent classification is presented. The approach exploits the spatial orientation of high-frequency textural features of the processed image as determined by a two-step process. First, the two-dimensional discrete wavelet transform (DWT) is applied to obtain the HH high-frequency subband image. Then, a Gabor filter bank is applied to the latter at different frequencies and spatial orientations to obtain new Gabor-filtered image whose entropy and uniformity are computed. Finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier. The approach was validated on mammograms, retina, and brain magnetic resonance (MR) images. The obtained classification accuracies show better performance in comparison to common approaches that use only the DWT or Gabor filter banks for feature extraction. PMID:27006906

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

NASA Astrophysics Data System (ADS)

Chiadamrong, N.; Piyathanavong, V.

2017-12-01

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

Discrete Mathematical Approaches to Graph-Based Traffic Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Joslyn, Cliff A.; Cowley, Wendy E.; Hogan, Emilie A.

2014-04-01

Modern cyber defense and anlaytics requires general, formal models of cyber systems. Multi-scale network models are prime candidates for such formalisms, using discrete mathematical methods based in hierarchically-structured directed multigraphs which also include rich sets of labels. An exemplar of an application of such an approach is traffic analysis, that is, observing and analyzing connections between clients, servers, hosts, and actors within IP networks, over time, to identify characteristic or suspicious patterns. Towards that end, NetFlow (or more generically, IPFLOW) data are available from routers and servers which summarize coherent groups of IP packets flowing through the network. In thismore » paper, we consider traffic analysis of Netflow using both basic graph statistics and two new mathematical measures involving labeled degree distributions and time interval overlap measures. We do all of this over the VAST test data set of 96M synthetic Netflow graph edges, against which we can identify characteristic patterns of simulated ground-truth network attacks.« less

Convergence analysis of stochastic hybrid bidirectional associative memory neural networks with delays

NASA Astrophysics Data System (ADS)

Wan, Li; Zhou, Qinghua

2007-10-01

The stability property of stochastic hybrid bidirectional associate memory (BAM) neural networks with discrete delays is considered. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, the delay-independent sufficient conditions to guarantee the exponential stability of the equilibrium solution for such networks are given by using the nonnegative semimartingale convergence theorem.

A grid layout algorithm for automatic drawing of biochemical networks.

PubMed

Li, Weijiang; Kurata, Hiroyuki

2005-05-01

Visualization is indispensable in the research of complex biochemical networks. Available graph layout algorithms are not adequate for satisfactorily drawing such networks. New methods are required to visualize automatically the topological architectures and facilitate the understanding of the functions of the networks. We propose a novel layout algorithm to draw complex biochemical networks. A network is modeled as a system of interacting nodes on squared grids. A discrete cost function between each node pair is designed based on the topological relation and the geometric positions of the two nodes. The layouts are produced by minimizing the total cost. We design a fast algorithm to minimize the discrete cost function, by which candidate layouts can be produced efficiently. A simulated annealing procedure is used to choose better candidates. Our algorithm demonstrates its ability to exhibit cluster structures clearly in relatively compact layout areas without any prior knowledge. We developed Windows software to implement the algorithm for CADLIVE. All materials can be freely downloaded from http://kurata21.bio.kyutech.ac.jp/grid/grid_layout.htm; http://www.cadlive.jp/ http://kurata21.bio.kyutech.ac.jp/grid/grid_layout.htm; http://www.cadlive.jp/

Economic Feasibility of Wireless Sensor Network-Based Service Provision in a Duopoly Setting with a Monopolist Operator.

PubMed

Sanchis-Cano, Angel; Romero, Julián; Sacoto-Cabrera, Erwin J; Guijarro, Luis

2017-11-25

We analyze the feasibility of providing Wireless Sensor Network-data-based services in an Internet of Things scenario from an economical point of view. The scenario has two competing service providers with their own private sensor networks, a network operator and final users. The scenario is analyzed as two games using game theory. In the first game, sensors decide to subscribe or not to the network operator to upload the collected sensing-data, based on a utility function related to the mean service time and the price charged by the operator. In the second game, users decide to subscribe or not to the sensor-data-based service of the service providers based on a Logit discrete choice model related to the quality of the data collected and the subscription price. The sinks and users subscription stages are analyzed using population games and discrete choice models, while network operator and service providers pricing stages are analyzed using optimization and Nash equilibrium concepts respectively. The model is shown feasible from an economic point of view for all the actors if there are enough interested final users and opens the possibility of developing more efficient models with different types of services.

Radial artery pulse waveform analysis based on curve fitting using discrete Fourier series.

PubMed

Jiang, Zhixing; Zhang, David; Lu, Guangming

2018-04-19

Radial artery pulse diagnosis has been playing an important role in traditional Chinese medicine (TCM). For its non-invasion and convenience, the pulse diagnosis has great significance in diseases analysis of modern medicine. The practitioners sense the pulse waveforms in patients' wrist to make diagnoses based on their non-objective personal experience. With the researches of pulse acquisition platforms and computerized analysis methods, the objective study on pulse diagnosis can help the TCM to keep up with the development of modern medicine. In this paper, we propose a new method to extract feature from pulse waveform based on discrete Fourier series (DFS). It regards the waveform as one kind of signal that consists of a series of sub-components represented by sine and cosine (SC) signals with different frequencies and amplitudes. After the pulse signals are collected and preprocessed, we fit the average waveform for each sample using discrete Fourier series by least squares. The feature vector is comprised by the coefficients of discrete Fourier series function. Compared with the fitting method using Gaussian mixture function, the fitting errors of proposed method are smaller, which indicate that our method can represent the original signal better. The classification performance of proposed feature is superior to the other features extracted from waveform, liking auto-regression model and Gaussian mixture model. The coefficients of optimized DFS function, who is used to fit the arterial pressure waveforms, can obtain better performance in modeling the waveforms and holds more potential information for distinguishing different psychological states. Copyright © 2018 Elsevier B.V. All rights reserved.

Observability of Automata Networks: Fixed and Switching Cases.

PubMed

Li, Rui; Hong, Yiguang; Wang, Xingyuan

2018-04-01

Automata networks are a class of fully discrete dynamical systems, which have received considerable interest in various different areas. This brief addresses the observability of automata networks and switched automata networks in a unified framework, and proposes simple necessary and sufficient conditions for observability. The results are achieved by employing methods from symbolic computation, and are suited for implementation using computer algebra systems. Several examples are presented to demonstrate the application of the results.

Computationally efficient approach for solving time dependent diffusion equation with discrete temporal convolution applied to granular particles of battery electrodes

NASA Astrophysics Data System (ADS)

Senegačnik, Jure; Tavčar, Gregor; Katrašnik, Tomaž

2015-03-01

The paper presents a computationally efficient method for solving the time dependent diffusion equation in a granule of the Li-ion battery's granular solid electrode. The method, called Discrete Temporal Convolution method (DTC), is based on a discrete temporal convolution of the analytical solution of the step function boundary value problem. This approach enables modelling concentration distribution in the granular particles for arbitrary time dependent exchange fluxes that do not need to be known a priori. It is demonstrated in the paper that the proposed method features faster computational times than finite volume/difference methods and Padé approximation at the same accuracy of the results. It is also demonstrated that all three addressed methods feature higher accuracy compared to the quasi-steady polynomial approaches when applied to simulate the current densities variations typical for mobile/automotive applications. The proposed approach can thus be considered as one of the key innovative methods enabling real-time capability of the multi particle electrochemical battery models featuring spatial and temporal resolved particle concentration profiles.

CNNEDGEPOT: CNN based edge detection of 2D near surface potential field data

NASA Astrophysics Data System (ADS)

Aydogan, D.

2012-09-01

All anomalies are important in the interpretation of gravity and magnetic data because they indicate some important structural features. One of the advantages of using gravity or magnetic data for searching contacts is to be detected buried structures whose signs could not be seen on the surface. In this paper, a general view of the cellular neural network (CNN) method with a large scale nonlinear circuit is presented focusing on its image processing applications. The proposed CNN model is used consecutively in order to extract body and body edges. The algorithm is a stochastic image processing method based on close neighborhood relationship of the cells and optimization of A, B and I matrices entitled as cloning template operators. Setting up a CNN (continues time cellular neural network (CTCNN) or discrete time cellular neural network (DTCNN)) for a particular task needs a proper selection of cloning templates which determine the dynamics of the method. The proposed algorithm is used for image enhancement and edge detection. The proposed method is applied on synthetic and field data generated for edge detection of near-surface geological bodies that mask each other in various depths and dimensions. The program named as CNNEDGEPOT is a set of functions written in MATLAB software. The GUI helps the user to easily change all the required CNN model parameters. A visual evaluation of the outputs due to DTCNN and CTCNN are carried out and the results are compared with each other. These examples demonstrate that in detecting the geological features the CNN model can be used for visual interpretation of near surface gravity or magnetic anomaly maps.

Metal-organic and supramolecular networks driven by 5-chloronicotinic acid: Hydrothermal self-assembly synthesis, structural diversity, luminescent and magnetic properties

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gao, Zhu-Qing, E-mail: zqgao2008@163.com; Li, Hong-Jin; Gu, Jin-Zhong, E-mail: gujzh@lzu.edu.cn

2016-09-15

Four new crystalline solids, namely [Co{sub 2}(µ{sub 2}-5-Clnic){sub 2}(µ{sub 3}-5-Clnic){sub 2}(µ{sub 2}-H{sub 2}O)]{sub n} (1), [Co(5-Clnic){sub 2}(H{sub 2}O){sub 4}]·2(5-ClnicH) (2), [Pb(µ{sub 2}-5-Clnic){sub 2}(phen)]{sub n} (3), and [Cd(5-Clnic){sub 2}(phen){sub 2}]·3H{sub 2}O (4) were generated by hydrothermal self-assembly methods from the corresponding metal(II) chlorides, 5-chloronicotinic acid (5-ClnicH) as a principal building block, and 1,10-phenanthroline (phen) as an ancillary ligand (optional). All the products 1–4 were characterized by IR spectroscopy, elemental analysis, thermogravimetric (TGA), powder X-ray diffraction (PXRD) and single-crystal X-ray diffraction. Their structures range from an intricate 3D metal-organic network 1 with the 3,6T7 topology to a ladder-like 1D coordination polymer 3more » with the 2C1 topology, whereas compounds 2 and 4 are the discrete 0D monomers. The structures of 2 and 4 are further extended (0D→2D or 0D→3D) by hydrogen bonds, generating supramolecular networks with the 3,8L18 and ins topologies, respectively. Synthetic aspects, structural features, thermal stability, magnetic (for 1) and luminescent (for 3 and 4) properties were also investigated and discussed. - Graphical abstract: A new series of crystalline solids was self-assembled and fully characterized; their structural, topological, luminescent and magnetic features were investigated. Display Omitted.« less

Modeling a Million-Node Slim Fly Network Using Parallel Discrete-Event Simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wolfe, Noah; Carothers, Christopher; Mubarak, Misbah

As supercomputers close in on exascale performance, the increased number of processors and processing power translates to an increased demand on the underlying network interconnect. The Slim Fly network topology, a new lowdiameter and low-latency interconnection network, is gaining interest as one possible solution for next-generation supercomputing interconnect systems. In this paper, we present a high-fidelity Slim Fly it-level model leveraging the Rensselaer Optimistic Simulation System (ROSS) and Co-Design of Exascale Storage (CODES) frameworks. We validate our Slim Fly model with the Kathareios et al. Slim Fly model results provided at moderately sized network scales. We further scale the modelmore » size up to n unprecedented 1 million compute nodes; and through visualization of network simulation metrics such as link bandwidth, packet latency, and port occupancy, we get an insight into the network behavior at the million-node scale. We also show linear strong scaling of the Slim Fly model on an Intel cluster achieving a peak event rate of 36 million events per second using 128 MPI tasks to process 7 billion events. Detailed analysis of the underlying discrete-event simulation performance shows that a million-node Slim Fly model simulation can execute in 198 seconds on the Intel cluster.« less

Fusion of ECG and ABP signals based on wavelet transform for cardiac arrhythmias classification.

PubMed

Arvanaghi, Roghayyeh; Daneshvar, Sabalan; Seyedarabi, Hadi; Goshvarpour, Atefeh

2017-11-01

Each of Electrocardiogram (ECG) and Atrial Blood Pressure (ABP) signals contain information of cardiac status. This information can be used for diagnosis and monitoring of diseases. The majority of previously proposed methods rely only on ECG signal to classify heart rhythms. In this paper, ECG and ABP were used to classify five different types of heart rhythms. To this end, two mentioned signals (ECG and ABP) have been fused. These physiological signals have been used from MINIC physioNet database. ECG and ABP signals have been fused together on the basis of the proposed Discrete Wavelet Transformation fusion technique. Then, some frequency features were extracted from the fused signal. To classify the different types of cardiac arrhythmias, these features were given to a multi-layer perceptron neural network. In this study, the best results for the proposed fusion algorithm were obtained. In this case, the accuracy rates of 96.6%, 96.9%, 95.6% and 93.9% were achieved for two, three, four and five classes, respectively. However, the maximum classification rate of 89% was obtained for two classes on the basis of ECG features. It has been found that the higher accuracy rates were acquired by using the proposed fusion technique. The results confirmed the importance of fusing features from different physiological signals to gain more accurate assessments. Copyright © 2017 Elsevier B.V. All rights reserved.

Youth with Psychopathy Features Are Not a Discrete Class: A Taxometric Analysis

ERIC Educational Resources Information Center

Murrie, Daniel C.; Marcus, David K.; Douglas, Kevin S.; Lee, Zina; Salekin, Randall T.; Vincent, Gina

2007-01-01

Background: Recently, researchers have sought to measure psychopathy-like features among youth in hopes of identifying children who may be progressing toward a particularly destructive form of adult pathology. However, it remains unclear whether psychopathy-like personality features among youth are best conceptualized as dimensional (distributed…

A discrete model of Drosophila eggshell patterning reveals cell-autonomous and juxtacrine effects.

PubMed

Fauré, Adrien; Vreede, Barbara M I; Sucena, Elio; Chaouiya, Claudine

2014-03-01

The Drosophila eggshell constitutes a remarkable system for the study of epithelial patterning, both experimentally and through computational modeling. Dorsal eggshell appendages arise from specific regions in the anterior follicular epithelium that covers the oocyte: two groups of cells expressing broad (roof cells) bordered by rhomboid expressing cells (floor cells). Despite the large number of genes known to participate in defining these domains and the important modeling efforts put into this developmental system, key patterning events still lack a proper mechanistic understanding and/or genetic basis, and the literature appears to conflict on some crucial points. We tackle these issues with an original, discrete framework that considers single-cell models that are integrated to construct epithelial models. We first build a phenomenological model that reproduces wild type follicular epithelial patterns, confirming EGF and BMP signaling input as sufficient to establish the major features of this patterning system within the anterior domain. Importantly, this simple model predicts an instructive juxtacrine signal linking the roof and floor domains. To explore this prediction, we define a mechanistic model that integrates the combined effects of cellular genetic networks, cell communication and network adjustment through developmental events. Moreover, we focus on the anterior competence region, and postulate that early BMP signaling participates with early EGF signaling in its specification. This model accurately simulates wild type pattern formation and is able to reproduce, with unprecedented level of precision and completeness, various published gain-of-function and loss-of-function experiments, including perturbations of the BMP pathway previously seen as conflicting results. The result is a coherent model built upon rules that may be generalized to other epithelia and developmental systems.

Multiple ECG Fiducial Points-Based Random Binary Sequence Generation for Securing Wireless Body Area Networks.

PubMed

Zheng, Guanglou; Fang, Gengfa; Shankaran, Rajan; Orgun, Mehmet A; Zhou, Jie; Qiao, Li; Saleem, Kashif

2017-05-01

Generating random binary sequences (BSes) is a fundamental requirement in cryptography. A BS is a sequence of N bits, and each bit has a value of 0 or 1. For securing sensors within wireless body area networks (WBANs), electrocardiogram (ECG)-based BS generation methods have been widely investigated in which interpulse intervals (IPIs) from each heartbeat cycle are processed to produce BSes. Using these IPI-based methods to generate a 128-bit BS in real time normally takes around half a minute. In order to improve the time efficiency of such methods, this paper presents an ECG multiple fiducial-points based binary sequence generation (MFBSG) algorithm. The technique of discrete wavelet transforms is employed to detect arrival time of these fiducial points, such as P, Q, R, S, and T peaks. Time intervals between them, including RR, RQ, RS, RP, and RT intervals, are then calculated based on this arrival time, and are used as ECG features to generate random BSes with low latency. According to our analysis on real ECG data, these ECG feature values exhibit the property of randomness and, thus, can be utilized to generate random BSes. Compared with the schemes that solely rely on IPIs to generate BSes, this MFBSG algorithm uses five feature values from one heart beat cycle, and can be up to five times faster than the solely IPI-based methods. So, it achieves a design goal of low latency. According to our analysis, the complexity of the algorithm is comparable to that of fast Fourier transforms. These randomly generated ECG BSes can be used as security keys for encryption or authentication in a WBAN system.

Synchronization Of Parallel Discrete Event Simulations

NASA Technical Reports Server (NTRS)

Steinman, Jeffrey S.

1992-01-01

Adaptive, parallel, discrete-event-simulation-synchronization algorithm, Breathing Time Buckets, developed in Synchronous Parallel Environment for Emulation and Discrete Event Simulation (SPEEDES) operating system. Algorithm allows parallel simulations to process events optimistically in fluctuating time cycles that naturally adapt while simulation in progress. Combines best of optimistic and conservative synchronization strategies while avoiding major disadvantages. Algorithm processes events optimistically in time cycles adapting while simulation in progress. Well suited for modeling communication networks, for large-scale war games, for simulated flights of aircraft, for simulations of computer equipment, for mathematical modeling, for interactive engineering simulations, and for depictions of flows of information.

Analytical Models of Cross-Layer Protocol Optimization in Real-Time Wireless Sensor Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Hortos, William S.

The real-time interactions among the nodes of a wireless sensor network (WSN) to cooperatively process data from multiple sensors are modeled. Quality-of-service (QoS) metrics are associated with the quality of fused information: throughput, delay, packet error rate, etc. Multivariate point process (MVPP) models of discrete random events in WSNs establish stochastic characteristics of optimal cross-layer protocols. Discrete-event, cross-layer interactions in mobile ad hoc network (MANET) protocols have been modeled using a set of concatenated design parameters and associated resource levels by the MVPPs. Characterization of the "best" cross-layer designs for a MANET is formulated by applying the general theory of martingale representations to controlled MVPPs. Performance is described in terms of concatenated protocol parameters and controlled through conditional rates of the MVPPs. Modeling limitations to determination of closed-form solutions versus explicit iterative solutions for ad hoc WSN controls are examined.

NasoNet, modeling the spread of nasopharyngeal cancer with networks of probabilistic events in discrete time.

PubMed

Galán, S F; Aguado, F; Díez, F J; Mira, J

2002-07-01

The spread of cancer is a non-deterministic dynamic process. As a consequence, the design of an assistant system for the diagnosis and prognosis of the extent of a cancer should be based on a representation method that deals with both uncertainty and time. The ultimate goal is to know the stage of development of a cancer in a patient before selecting the appropriate treatment. A network of probabilistic events in discrete time (NPEDT) is a type of Bayesian network for temporal reasoning that models the causal mechanisms associated with the time evolution of a process. This paper describes NasoNet, a system that applies NPEDTs to the diagnosis and prognosis of nasopharyngeal cancer. We have made use of temporal noisy gates to model the dynamic causal interactions that take place in the domain. The methodology we describe is general enough to be applied to any other type of cancer.

Networked event-triggered control: an introduction and research trends

NASA Astrophysics Data System (ADS)

Mahmoud, Magdi S.; Sabih, Muhammad

2014-11-01

A physical system can be studied as either continuous time or discrete-time system depending upon the control objectives. Discrete-time control systems can be further classified into two categories based on the sampling: (1) time-triggered control systems and (2) event-triggered control systems. Time-triggered systems sample states and calculate controls at every sampling instant in a periodic fashion, even in cases when states and calculated control do not change much. This indicates unnecessary and useless data transmission and computation efforts of a time-triggered system, thus inefficiency. For networked systems, the transmission of measurement and control signals, thus, cause unnecessary network traffic. Event-triggered systems, on the other hand, have potential to reduce the communication burden in addition to reducing the computation of control signals. This paper provides an up-to-date survey on the event-triggered methods for control systems and highlights the potential research directions.

Finite size effects in epidemic spreading: the problem of overpopulated systems

NASA Astrophysics Data System (ADS)

Ganczarek, Wojciech

2013-12-01

In this paper we analyze the impact of network size on the dynamics of epidemic spreading. In particular, we investigate the pace of infection in overpopulated systems. In order to do that, we design a model for epidemic spreading on a finite complex network with a restriction to at most one contamination per time step, which can serve as a model for sexually transmitted diseases spreading in some student communes. Because of the highly discrete character of the process, the analysis cannot use the continuous approximation widely exploited for most models. Using a discrete approach, we investigate the epidemic threshold and the quasi-stationary distribution. The main results are two theorems about the mixing time for the process: it scales like the logarithm of the network size and it is proportional to the inverse of the distance from the epidemic threshold.

Detection of small bowel tumors in capsule endoscopy frames using texture analysis based on the discrete wavelet transform.

PubMed

Barbosa, Daniel J C; Ramos, Jaime; Lima, Carlos S

2008-01-01

Capsule endoscopy is an important tool to diagnose tumor lesions in the small bowel. The capsule endoscopic images possess vital information expressed by color and texture. This paper presents an approach based in the textural analysis of the different color channels, using the wavelet transform to select the bands with the most significant texture information. A new image is then synthesized from the selected wavelet bands, trough the inverse wavelet transform. The features of each image are based on second-order textural information, and they are used in a classification scheme using a multilayer perceptron neural network. The proposed methodology has been applied in real data taken from capsule endoscopic exams and reached 98.7% sensibility and 96.6% specificity. These results support the feasibility of the proposed algorithm.

Mechanical Characterization of Partially Crystallized Sphere Packings

NASA Astrophysics Data System (ADS)

Hanifpour, M.; Francois, N.; Vaez Allaei, S. M.; Senden, T.; Saadatfar, M.

2014-10-01

We study grain-scale mechanical and geometrical features of partially crystallized packings of frictional spheres, produced experimentally by a vibrational protocol. By combining x-ray computed tomography, 3D image analysis, and discrete element method simulations, we have access to the 3D structure of internal forces. We investigate how the network of mechanical contacts and intergranular forces change when the packing structure evolves from amorphous to near perfect crystalline arrangements. We compare the behavior of the geometrical neighbors (quasicontracts) of a grain to the evolution of the mechanical contacts. The mechanical coordination number Zm is a key parameter characterizing the crystallization onset. The high fluctuation level of Zm and of the force distribution in highly crystallized packings reveals that a geometrically ordered structure still possesses a highly random mechanical backbone similar to that of amorphous packings.

ADAM: Analysis of Discrete Models of Biological Systems Using Computer Algebra

PubMed Central

2011-01-01

Background Many biological systems are modeled qualitatively with discrete models, such as probabilistic Boolean networks, logical models, Petri nets, and agent-based models, to gain a better understanding of them. The computational complexity to analyze the complete dynamics of these models grows exponentially in the number of variables, which impedes working with complex models. There exist software tools to analyze discrete models, but they either lack the algorithmic functionality to analyze complex models deterministically or they are inaccessible to many users as they require understanding the underlying algorithm and implementation, do not have a graphical user interface, or are hard to install. Efficient analysis methods that are accessible to modelers and easy to use are needed. Results We propose a method for efficiently identifying attractors and introduce the web-based tool Analysis of Dynamic Algebraic Models (ADAM), which provides this and other analysis methods for discrete models. ADAM converts several discrete model types automatically into polynomial dynamical systems and analyzes their dynamics using tools from computer algebra. Specifically, we propose a method to identify attractors of a discrete model that is equivalent to solving a system of polynomial equations, a long-studied problem in computer algebra. Based on extensive experimentation with both discrete models arising in systems biology and randomly generated networks, we found that the algebraic algorithms presented in this manuscript are fast for systems with the structure maintained by most biological systems, namely sparseness and robustness. For a large set of published complex discrete models, ADAM identified the attractors in less than one second. Conclusions Discrete modeling techniques are a useful tool for analyzing complex biological systems and there is a need in the biological community for accessible efficient analysis tools. ADAM provides analysis methods based on mathematical algorithms as a web-based tool for several different input formats, and it makes analysis of complex models accessible to a larger community, as it is platform independent as a web-service and does not require understanding of the underlying mathematics. PMID:21774817

Interim Service ISDN Satellite (ISIS) network model for advanced satellite designs and experiments

NASA Technical Reports Server (NTRS)

Pepin, Gerard R.; Hager, E. Paul

1991-01-01

The Interim Service Integrated Services Digital Network (ISDN) Satellite (ISIS) Network Model for Advanced Satellite Designs and Experiments describes a model suitable for discrete event simulations. A top-down model design uses the Advanced Communications Technology Satellite (ACTS) as its basis. The ISDN modeling abstractions are added to permit the determination and performance for the NASA Satellite Communications Research (SCAR) Program.

The Social Networks of Small Arms Proliferation: Mapping an Aviation Enabled Supply Chain

DTIC Science & Technology

2007-12-01

each of the discrete arms 248 Wouter de Nooy, Andrej Mrvar , and Vladimir Batagelj , Exploratory Social...303 Wouter de Nooy, Andrej Mrvar , and Vladimir Batagelj , Exploratory Social Network Analysis with Pajek, 101. 304 Ibid., 21. 93 entity. The data...305 Wouter de Nooy, Andrej Mrvar , and Vladimir Batagelj , Exploratory Social Network Analysis with Pajek, 101. 306 Linton C. Freeman, "Graphical

Enabling parallel simulation of large-scale HPC network systems

DOE PAGES

Mubarak, Misbah; Carothers, Christopher D.; Ross, Robert B.; ...

2016-04-07

Here, with the increasing complexity of today’s high-performance computing (HPC) architectures, simulation has become an indispensable tool for exploring the design space of HPC systems—in particular, networks. In order to make effective design decisions, simulations of these systems must possess the following properties: (1) have high accuracy and fidelity, (2) produce results in a timely manner, and (3) be able to analyze a broad range of network workloads. Most state-of-the-art HPC network simulation frameworks, however, are constrained in one or more of these areas. In this work, we present a simulation framework for modeling two important classes of networks usedmore » in today’s IBM and Cray supercomputers: torus and dragonfly networks. We use the Co-Design of Multi-layer Exascale Storage Architecture (CODES) simulation framework to simulate these network topologies at a flit-level detail using the Rensselaer Optimistic Simulation System (ROSS) for parallel discrete-event simulation. Our simulation framework meets all the requirements of a practical network simulation and can assist network designers in design space exploration. First, it uses validated and detailed flit-level network models to provide an accurate and high-fidelity network simulation. Second, instead of relying on serial time-stepped or traditional conservative discrete-event simulations that limit simulation scalability and efficiency, we use the optimistic event-scheduling capability of ROSS to achieve efficient and scalable HPC network simulations on today’s high-performance cluster systems. Third, our models give network designers a choice in simulating a broad range of network workloads, including HPC application workloads using detailed network traces, an ability that is rarely offered in parallel with high-fidelity network simulations« less

Enabling parallel simulation of large-scale HPC network systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mubarak, Misbah; Carothers, Christopher D.; Ross, Robert B.

Here, with the increasing complexity of today’s high-performance computing (HPC) architectures, simulation has become an indispensable tool for exploring the design space of HPC systems—in particular, networks. In order to make effective design decisions, simulations of these systems must possess the following properties: (1) have high accuracy and fidelity, (2) produce results in a timely manner, and (3) be able to analyze a broad range of network workloads. Most state-of-the-art HPC network simulation frameworks, however, are constrained in one or more of these areas. In this work, we present a simulation framework for modeling two important classes of networks usedmore » in today’s IBM and Cray supercomputers: torus and dragonfly networks. We use the Co-Design of Multi-layer Exascale Storage Architecture (CODES) simulation framework to simulate these network topologies at a flit-level detail using the Rensselaer Optimistic Simulation System (ROSS) for parallel discrete-event simulation. Our simulation framework meets all the requirements of a practical network simulation and can assist network designers in design space exploration. First, it uses validated and detailed flit-level network models to provide an accurate and high-fidelity network simulation. Second, instead of relying on serial time-stepped or traditional conservative discrete-event simulations that limit simulation scalability and efficiency, we use the optimistic event-scheduling capability of ROSS to achieve efficient and scalable HPC network simulations on today’s high-performance cluster systems. Third, our models give network designers a choice in simulating a broad range of network workloads, including HPC application workloads using detailed network traces, an ability that is rarely offered in parallel with high-fidelity network simulations« less

Multilayer shallow water models with locally variable number of layers and semi-implicit time discretization

NASA Astrophysics Data System (ADS)

Bonaventura, Luca; Fernández-Nieto, Enrique D.; Garres-Díaz, José; Narbona-Reina, Gladys

2018-07-01

We propose an extension of the discretization approaches for multilayer shallow water models, aimed at making them more flexible and efficient for realistic applications to coastal flows. A novel discretization approach is proposed, in which the number of vertical layers and their distribution are allowed to change in different regions of the computational domain. Furthermore, semi-implicit schemes are employed for the time discretization, leading to a significant efficiency improvement for subcritical regimes. We show that, in the typical regimes in which the application of multilayer shallow water models is justified, the resulting discretization does not introduce any major spurious feature and allows again to reduce substantially the computational cost in areas with complex bathymetry. As an example of the potential of the proposed technique, an application to a sediment transport problem is presented, showing a remarkable improvement with respect to standard discretization approaches.

40 CFR 51.353 - Network type and program evaluation.

Code of Federal Regulations, 2013 CFR

2013-07-01

..., decentralized, or a hybrid of the two at the State's discretion, but shall be demonstrated to achieve the same... § 51.351 or 51.352 of this subpart. For decentralized programs other than those meeting the design.... (a) Presumptive equivalency. A decentralized network consisting of stations that only perform...

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ng, B

This survey gives an overview of popular generative models used in the modeling of stochastic temporal systems. In particular, this survey is organized into two parts. The first part discusses the discrete-time representations of dynamic Bayesian networks and dynamic relational probabilistic models, while the second part discusses the continuous-time representation of continuous-time Bayesian networks.

Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment

ERIC Educational Resources Information Center

Johnson, Samuel G. B.; Ahn, Woo-kyoung

2015-01-01

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge--an interconnected causal "network," where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms--causal "islands"--such that events in different…

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

NASA Astrophysics Data System (ADS)

Hyman, J. D.; Aldrich, G.; Viswanathan, H.; Makedonska, N.; Karra, S.

2016-08-01

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semicorrelation, and noncorrelation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected so that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. These observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

NASA Astrophysics Data System (ADS)

Hyman, J.; Aldrich, G. A.; Viswanathan, H. S.; Makedonska, N.; Karra, S.

2016-12-01

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semi-correlation, and non-correlation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected so that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same.We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. These observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.

Shift-invariant discrete wavelet transform analysis for retinal image classification.

PubMed

Khademi, April; Krishnan, Sridhar

2007-12-01

This work involves retinal image classification and a novel analysis system was developed. From the compressed domain, the proposed scheme extracts textural features from wavelet coefficients, which describe the relative homogeneity of localized areas of the retinal images. Since the discrete wavelet transform (DWT) is shift-variant, a shift-invariant DWT was explored to ensure that a robust feature set was extracted. To combat the small database size, linear discriminant analysis classification was used with the leave one out method. 38 normal and 48 abnormal (exudates, large drusens, fine drusens, choroidal neovascularization, central vein and artery occlusion, histoplasmosis, arteriosclerotic retinopathy, hemi-central retinal vein occlusion and more) were used and a specificity of 79% and sensitivity of 85.4% were achieved (the average classification rate is 82.2%). The success of the system can be accounted to the highly robust feature set which included translation, scale and semi-rotational, features. Additionally, this technique is database independent since the features were specifically tuned to the pathologies of the human eye.

The Knowledge Program: an Innovative, Comprehensive Electronic Data Capture System and Warehouse

PubMed Central

Katzan, Irene; Speck, Micheal; Dopler, Chris; Urchek, John; Bielawski, Kay; Dunphy, Cheryl; Jehi, Lara; Bae, Charles; Parchman, Alandra

2011-01-01

Data contained in the electronic health record (EHR) present a tremendous opportunity to improve quality-of-care and enhance research capabilities. However, the EHR is not structured to provide data for such purposes: most clinical information is entered as free text and content varies substantially between providers. Discrete information on patients’ functional status is typically not collected. Data extraction tools are often unavailable. We have developed the Knowledge Program (KP), a comprehensive initiative to improve the collection of discrete clinical information into the EHR and the retrievability of data for use in research, quality, and patient care. A distinct feature of the KP is the systematic collection of patient-reported outcomes, which is captured discretely, allowing more refined analyses of care outcomes. The KP capitalizes on features of the Epic EHR and utilizes an external IT infrastructure distinct from Epic for enhanced functionality. Here, we describe the development and implementation of the KP. PMID:22195124

Bayesian estimation of the discrete coefficient of determination.

PubMed

Chen, Ting; Braga-Neto, Ulisses M

2016-12-01

The discrete coefficient of determination (CoD) measures the nonlinear interaction between discrete predictor and target variables and has had far-reaching applications in Genomic Signal Processing. Previous work has addressed the inference of the discrete CoD using classical parametric and nonparametric approaches. In this paper, we introduce a Bayesian framework for the inference of the discrete CoD. We derive analytically the optimal minimum mean-square error (MMSE) CoD estimator, as well as a CoD estimator based on the Optimal Bayesian Predictor (OBP). For the latter estimator, exact expressions for its bias, variance, and root-mean-square (RMS) are given. The accuracy of both Bayesian CoD estimators with non-informative and informative priors, under fixed or random parameters, is studied via analytical and numerical approaches. We also demonstrate the application of the proposed Bayesian approach in the inference of gene regulatory networks, using gene-expression data from a previously published study on metastatic melanoma.

Adaptive Event-Triggered Control Based on Heuristic Dynamic Programming for Nonlinear Discrete-Time Systems.

PubMed

Dong, Lu; Zhong, Xiangnan; Sun, Changyin; He, Haibo

2017-07-01

This paper presents the design of a novel adaptive event-triggered control method based on the heuristic dynamic programming (HDP) technique for nonlinear discrete-time systems with unknown system dynamics. In the proposed method, the control law is only updated when the event-triggered condition is violated. Compared with the periodic updates in the traditional adaptive dynamic programming (ADP) control, the proposed method can reduce the computation and transmission cost. An actor-critic framework is used to learn the optimal event-triggered control law and the value function. Furthermore, a model network is designed to estimate the system state vector. The main contribution of this paper is to design a new trigger threshold for discrete-time systems. A detailed Lyapunov stability analysis shows that our proposed event-triggered controller can asymptotically stabilize the discrete-time systems. Finally, we test our method on two different discrete-time systems, and the simulation results are included.

A discrete control model of PLANT

NASA Technical Reports Server (NTRS)

Mitchell, C. M.

1985-01-01

A model of the PLANT system using the discrete control modeling techniques developed by Miller is described. Discrete control models attempt to represent in a mathematical form how a human operator might decompose a complex system into simpler parts and how the control actions and system configuration are coordinated so that acceptable overall system performance is achieved. Basic questions include knowledge representation, information flow, and decision making in complex systems. The structure of the model is a general hierarchical/heterarchical scheme which structurally accounts for coordination and dynamic focus of attention. Mathematically, the discrete control model is defined in terms of a network of finite state systems. Specifically, the discrete control model accounts for how specific control actions are selected from information about the controlled system, the environment, and the context of the situation. The objective is to provide a plausible and empirically testable accounting and, if possible, explanation of control behavior.

Identification of a core-periphery structure among participants of a business climate survey. An investigation based on the ZEW survey data

NASA Astrophysics Data System (ADS)

Stolzenburg, U.; Lux, T.

2011-12-01

Processes of social opinion formation might be dominated by a set of closely connected agents who constitute the cohesive `core' of a network and have a higher influence on the overall outcome of the process than those agents in the more sparsely connected `periphery'. Here we explore whether such a perspective could shed light on the dynamics of a well known economic sentiment index. To this end, we hypothesize that the respondents of the survey under investigation form a core-periphery network, and we identify those agents that define the core (in a discrete setting) or the proximity of each agent to the core (in a continuous setting). As it turns out, there is significant correlation between the so identified cores of different survey questions. Both the discrete and the continuous cores allow an almost perfect replication of the original series with a reduced data set of core members or weighted entries according to core proximity. Using a monthly time series on industrial production in Germany, we also compared experts' predictions with the real economic development. The core members identified in the discrete setting showed significantly better prediction capabilities than those agents assigned to the periphery of the network.

Discrete-time moment closure models for epidemic spreading in populations of interacting individuals.

PubMed

Frasca, Mattia; Sharkey, Kieran J

2016-06-21

Understanding the dynamics of spread of infectious diseases between individuals is essential for forecasting the evolution of an epidemic outbreak or for defining intervention policies. The problem is addressed by many approaches including stochastic and deterministic models formulated at diverse scales (individuals, populations) and different levels of detail. Here we consider discrete-time SIR (susceptible-infectious-removed) dynamics propagated on contact networks. We derive a novel set of 'discrete-time moment equations' for the probability of the system states at the level of individual nodes and pairs of nodes. These equations form a set which we close by introducing appropriate approximations of the joint probabilities appearing in them. For the example case of SIR processes, we formulate two types of model, one assuming statistical independence at the level of individuals and one at the level of pairs. From the pair-based model we then derive a model at the level of the population which captures the behavior of epidemics on homogeneous random networks. With respect to their continuous-time counterparts, the models include a larger number of possible transitions from one state to another and joint probabilities with a larger number of individuals. The approach is validated through numerical simulation over different network topologies. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Biometric feature embedding using robust steganography technique

NASA Astrophysics Data System (ADS)

Rashid, Rasber D.; Sellahewa, Harin; Jassim, Sabah A.

2013-05-01

This paper is concerned with robust steganographic techniques to hide and communicate biometric data in mobile media objects like images, over open networks. More specifically, the aim is to embed binarised features extracted using discrete wavelet transforms and local binary patterns of face images as a secret message in an image. The need for such techniques can arise in law enforcement, forensics, counter terrorism, internet/mobile banking and border control. What differentiates this problem from normal information hiding techniques is the added requirement that there should be minimal effect on face recognition accuracy. We propose an LSB-Witness embedding technique in which the secret message is already present in the LSB plane but instead of changing the cover image LSB values, the second LSB plane will be changed to stand as a witness/informer to the receiver during message recovery. Although this approach may affect the stego quality, it is eliminating the weakness of traditional LSB schemes that is exploited by steganalysis techniques for LSB, such as PoV and RS steganalysis, to detect the existence of secrete message. Experimental results show that the proposed method is robust against PoV and RS attacks compared to other variants of LSB. We also discussed variants of this approach and determine capacity requirements for embedding face biometric feature vectors while maintain accuracy of face recognition.

Dynamic detection-rate-based bit allocation with genuine interval concealment for binary biometric representation.

PubMed

Lim, Meng-Hui; Teoh, Andrew Beng Jin; Toh, Kar-Ann

2013-06-01

Biometric discretization is a key component in biometric cryptographic key generation. It converts an extracted biometric feature vector into a binary string via typical steps such as segmentation of each feature element into a number of labeled intervals, mapping of each interval-captured feature element onto a binary space, and concatenation of the resulted binary output of all feature elements into a binary string. Currently, the detection rate optimized bit allocation (DROBA) scheme is one of the most effective biometric discretization schemes in terms of its capability to assign binary bits dynamically to user-specific features with respect to their discriminability. However, we learn that DROBA suffers from potential discriminative feature misdetection and underdiscretization in its bit allocation process. This paper highlights such drawbacks and improves upon DROBA based on a novel two-stage algorithm: 1) a dynamic search method to efficiently recapture such misdetected features and to optimize the bit allocation of underdiscretized features and 2) a genuine interval concealment technique to alleviate crucial information leakage resulted from the dynamic search. Improvements in classification accuracy on two popular face data sets vindicate the feasibility of our approach compared with DROBA.

Anomaly Detection in Dynamic Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Turcotte, Melissa

2014-10-14

Anomaly detection in dynamic communication networks has many important security applications. These networks can be extremely large and so detecting any changes in their structure can be computationally challenging; hence, computationally fast, parallelisable methods for monitoring the network are paramount. For this reason the methods presented here use independent node and edge based models to detect locally anomalous substructures within communication networks. As a first stage, the aim is to detect changes in the data streams arising from node or edge communications. Throughout the thesis simple, conjugate Bayesian models for counting processes are used to model these data streams. Amore » second stage of analysis can then be performed on a much reduced subset of the network comprising nodes and edges which have been identified as potentially anomalous in the first stage. The first method assumes communications in a network arise from an inhomogeneous Poisson process with piecewise constant intensity. Anomaly detection is then treated as a changepoint problem on the intensities. The changepoint model is extended to incorporate seasonal behavior inherent in communication networks. This seasonal behavior is also viewed as a changepoint problem acting on a piecewise constant Poisson process. In a static time frame, inference is made on this extended model via a Gibbs sampling strategy. In a sequential time frame, where the data arrive as a stream, a novel, fast Sequential Monte Carlo (SMC) algorithm is introduced to sample from the sequence of posterior distributions of the change points over time. A second method is considered for monitoring communications in a large scale computer network. The usage patterns in these types of networks are very bursty in nature and don’t fit a Poisson process model. For tractable inference, discrete time models are considered, where the data are aggregated into discrete time periods and probability models are fitted to the communication counts. In a sequential analysis, anomalous behavior is then identified from outlying behavior with respect to the fitted predictive probability models. Seasonality is again incorporated into the model and is treated as a changepoint model on the transition probabilities of a discrete time Markov process. Second stage analytics are then developed which combine anomalous edges to identify anomalous substructures in the network.« less

Fascin- and α-Actinin-Bundled Networks Contain Intrinsic Structural Features that Drive Protein Sorting.

PubMed

Winkelman, Jonathan D; Suarez, Cristian; Hocky, Glen M; Harker, Alyssa J; Morganthaler, Alisha N; Christensen, Jenna R; Voth, Gregory A; Bartles, James R; Kovar, David R

2016-10-24

Cells assemble and maintain functionally distinct actin cytoskeleton networks with various actin filament organizations and dynamics through the coordinated action of different sets of actin-binding proteins. The biochemical and functional properties of diverse actin-binding proteins, both alone and in combination, have been increasingly well studied. Conversely, how different sets of actin-binding proteins properly sort to distinct actin filament networks in the first place is not nearly as well understood. Actin-binding protein sorting is critical for the self-organization of diverse dynamic actin cytoskeleton networks within a common cytoplasm. Using in vitro reconstitution techniques including biomimetic assays and single-molecule multi-color total internal reflection fluorescence microscopy, we discovered that sorting of the prominent actin-bundling proteins fascin and α-actinin to distinct networks is an intrinsic behavior, free of complicated cellular signaling cascades. When mixed, fascin and α-actinin mutually exclude each other by promoting their own recruitment and inhibiting recruitment of the other, resulting in the formation of distinct fascin- or α-actinin-bundled domains. Subdiffraction-resolution light microscopy and negative-staining electron microscopy revealed that fascin domains are densely packed, whereas α-actinin domains consist of widely spaced parallel actin filaments. Importantly, other actin-binding proteins such as fimbrin and espin show high specificity between these two bundle types within the same reaction. Here we directly observe that fascin and α-actinin intrinsically segregate to discrete bundled domains that are specifically recognized by other actin-binding proteins. Copyright © 2016 Elsevier Ltd. All rights reserved.

Oscillations during observations: Dynamic oscillatory networks serving visuospatial attention.

PubMed

Wiesman, Alex I; Heinrichs-Graham, Elizabeth; Proskovec, Amy L; McDermott, Timothy J; Wilson, Tony W

2017-10-01

The dynamic allocation of neural resources to discrete features within a visual scene enables us to react quickly and accurately to salient environmental circumstances. A network of bilateral cortical regions is known to subserve such visuospatial attention functions; however the oscillatory and functional connectivity dynamics of information coding within this network are not fully understood. Particularly, the coding of information within prototypical attention-network hubs and the subsecond functional connections formed between these hubs have not been adequately characterized. Herein, we use the precise temporal resolution of magnetoencephalography (MEG) to define spectrally specific functional nodes and connections that underlie the deployment of attention in visual space. Twenty-three healthy young adults completed a visuospatial discrimination task designed to elicit multispectral activity in visual cortex during MEG, and the resulting data were preprocessed and reconstructed in the time-frequency domain. Oscillatory responses were projected to the cortical surface using a beamformer, and time series were extracted from peak voxels to examine their temporal evolution. Dynamic functional connectivity was then computed between nodes within each frequency band of interest. We find that visual attention network nodes are defined functionally by oscillatory frequency, that the allocation of attention to the visual space dynamically modulates functional connectivity between these regions on a millisecond timescale, and that these modulations significantly correlate with performance on a spatial discrimination task. We conclude that functional hubs underlying visuospatial attention are segregated not only anatomically but also by oscillatory frequency, and importantly that these oscillatory signatures promote dynamic communication between these hubs. Hum Brain Mapp 38:5128-5140, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Compressive-sampling-based positioning in wireless body area networks.

PubMed

Banitalebi-Dehkordi, Mehdi; Abouei, Jamshid; Plataniotis, Konstantinos N

2014-01-01

Recent achievements in wireless technologies have opened up enormous opportunities for the implementation of ubiquitous health care systems in providing rich contextual information and warning mechanisms against abnormal conditions. This helps with the automatic and remote monitoring/tracking of patients in hospitals and facilitates and with the supervision of fragile, elderly people in their own domestic environment through automatic systems to handle the remote drug delivery. This paper presents a new modeling and analysis framework for the multipatient positioning in a wireless body area network (WBAN) which exploits the spatial sparsity of patients and a sparse fast Fourier transform (FFT)-based feature extraction mechanism for monitoring of patients and for reporting the movement tracking to a central database server containing patient vital information. The main goal of this paper is to achieve a high degree of accuracy and resolution in the patient localization with less computational complexity in the implementation using the compressive sensing theory. We represent the patients' positions as a sparse vector obtained by the discrete segmentation of the patient movement space in a circular grid. To estimate this vector, a compressive-sampling-based two-level FFT (CS-2FFT) feature vector is synthesized for each received signal from the biosensors embedded on the patient's body at each grid point. This feature extraction process benefits in the combination of both short-time and long-time properties of the received signals. The robustness of the proposed CS-2FFT-based algorithm in terms of the average positioning error is numerically evaluated using the realistic parameters in the IEEE 802.15.6-WBAN standard in the presence of additive white Gaussian noise. Due to the circular grid pattern and the CS-2FFT feature extraction method, the proposed scheme represents a significant reduction in the computational complexity, while improving the level of the resolution and the localization accuracy when compared to some classical CS-based positioning algorithms.

An efficient and secure partial image encryption for wireless multimedia sensor networks using discrete wavelet transform, chaotic maps and substitution box

NASA Astrophysics Data System (ADS)

Khan, Muazzam A.; Ahmad, Jawad; Javaid, Qaisar; Saqib, Nazar A.

2017-03-01

Wireless Sensor Networks (WSN) is widely deployed in monitoring of some physical activity and/or environmental conditions. Data gathered from WSN is transmitted via network to a central location for further processing. Numerous applications of WSN can be found in smart homes, intelligent buildings, health care, energy efficient smart grids and industrial control systems. In recent years, computer scientists has focused towards findings more applications of WSN in multimedia technologies, i.e. audio, video and digital images. Due to bulky nature of multimedia data, WSN process a large volume of multimedia data which significantly increases computational complexity and hence reduces battery time. With respect to battery life constraints, image compression in addition with secure transmission over a wide ranged sensor network is an emerging and challenging task in Wireless Multimedia Sensor Networks. Due to the open nature of the Internet, transmission of data must be secure through a process known as encryption. As a result, there is an intensive demand for such schemes that is energy efficient as well as highly secure since decades. In this paper, discrete wavelet-based partial image encryption scheme using hashing algorithm, chaotic maps and Hussain's S-Box is reported. The plaintext image is compressed via discrete wavelet transform and then the image is shuffled column-wise and row wise-wise via Piece-wise Linear Chaotic Map (PWLCM) and Nonlinear Chaotic Algorithm, respectively. To get higher security, initial conditions for PWLCM are made dependent on hash function. The permuted image is bitwise XORed with random matrix generated from Intertwining Logistic map. To enhance the security further, final ciphertext is obtained after substituting all elements with Hussain's substitution box. Experimental and statistical results confirm the strength of the anticipated scheme.

Decision support system for diabetic retinopathy using discrete wavelet transform.

PubMed

Noronha, K; Acharya, U R; Nayak, K P; Kamath, S; Bhandary, S V

2013-03-01

Prolonged duration of the diabetes may affect the tiny blood vessels of the retina causing diabetic retinopathy. Routine eye screening of patients with diabetes helps to detect diabetic retinopathy at the early stage. It is very laborious and time-consuming for the doctors to go through many fundus images continuously. Therefore, decision support system for diabetic retinopathy detection can reduce the burden of the ophthalmologists. In this work, we have used discrete wavelet transform and support vector machine classifier for automated detection of normal and diabetic retinopathy classes. The wavelet-based decomposition was performed up to the second level, and eight energy features were extracted. Two energy features from the approximation coefficients of two levels and six energy values from the details in three orientations (horizontal, vertical and diagonal) were evaluated. These features were fed to the support vector machine classifier with various kernel functions (linear, radial basis function, polynomial of orders 2 and 3) to evaluate the highest classification accuracy. We obtained the highest average classification accuracy, sensitivity and specificity of more than 99% with support vector machine classifier (polynomial kernel of order 3) using three discrete wavelet transform features. We have also proposed an integrated index called Diabetic Retinopathy Risk Index using clinically significant wavelet energy features to identify normal and diabetic retinopathy classes using just one number. We believe that this (Diabetic Retinopathy Risk Index) can be used as an adjunct tool by the doctors during the eye screening to cross-check their diagnosis.

On Efficient Deployment of Wireless Sensors for Coverage and Connectivity in Constrained 3D Space.

PubMed

Wu, Chase Q; Wang, Li

2017-10-10

Sensor networks have been used in a rapidly increasing number of applications in many fields. This work generalizes a sensor deployment problem to place a minimum set of wireless sensors at candidate locations in constrained 3D space to k -cover a given set of target objects. By exhausting the combinations of discreteness/continuousness constraints on either sensor locations or target objects, we formulate four classes of sensor deployment problems in 3D space: deploy sensors at Discrete/Continuous Locations (D/CL) to cover Discrete/Continuous Targets (D/CT). We begin with the design of an approximate algorithm for DLDT and then reduce DLCT, CLDT, and CLCT to DLDT by discretizing continuous sensor locations or target objects into a set of divisions without sacrificing sensing precision. Furthermore, we consider a connected version of each problem where the deployed sensors must form a connected network, and design an approximation algorithm to minimize the number of deployed sensors with connectivity guarantee. For performance comparison, we design and implement an optimal solution and a genetic algorithm (GA)-based approach. Extensive simulation results show that the proposed deployment algorithms consistently outperform the GA-based heuristic and achieve a close-to-optimal performance in small-scale problem instances and a significantly superior overall performance than the theoretical upper bound.

Modeling the growth and interaction of stylolite networks, using the discrete element method for pressure solution

NASA Astrophysics Data System (ADS)

Makedonska, N.; Sparks, D. W.; Aharonov, E.

2012-12-01

Pressure solution (also termed chemical compaction) is considered the most important ductile deformation mechanism operating in the Earth's upper crust. This mechanism is a major player in a variety of geological processes, including evolution of sedimentary basins, hydrocarbon reservoirs, aquifers, earthquake recurrence cycles, and fault healing. Pressure solution in massive rocks often localizes into solution seams or stylolites. Field observations of stylolites often show elastic/brittle interactions in regions between pressure solution features, including and shear fractures, veins and pull-apart features. To understand these interactions, we use a grain-scale model based on the Discrete Element Method that allows granular dissolution at stressed contacts between grains. The new model captures both the slow chemical compaction process and the more abrupt brittle fracturing and sliding between grains. We simulate a sample of rock as a collection of particles, each representing either a grain or a unit of rock, bonded to each other with breakable cement. We apply external stresses to this sample, and calculate elastic and frictional interactions between the grains. Dissolution is modeled by an irreversible penetration of contacting grains into each other at a rate that depends on the contact stress and an adjustable rate constant. Experiments have shown that dissolution rates at grain contacts are greatly enhanced when there is a mineralogical contrast. Therefore, we dissolution rate constant can be increased to account for an amount of impurities (e.g. clay in a quartz or calcite sandstone) that can accumulate on dissolving contacts. This approach allows large compaction and shear strains within the rock, while allowing examination of local grain-scale heterogeneity. For example, we will describe the effect of pressure solution on the distribution of contact forces magnitudes and orientations. Contact forces in elastic granular packings are inherently heteregeneous, but stress-dependent dissolution tends to equalize them. We apply our model to the simulation of stylolite networks, particularly the interaction of stylolite tips. The stress concentrations from these tips are transmitted through the intervening rock, which can cause elastic strain, brittle damage and frictional sliding. Our model shows that grain rearrangement and compaction rate depend on the surface friction coefficient of grains. Simulation results show the development of shear zones between stylolites, and a high porosity process zone at the tips of stylolites. These features, which have been observed in field studies, are modeled and predicted for the first time. This modeling tool holds a promise to provide many new insights regarding the coupling between pressure solution and brittle deformation, i.e. between mechanical and chemical compaction.

Applying differential dynamic logic to reconfigurable biological networks.

PubMed

Figueiredo, Daniel; Martins, Manuel A; Chaves, Madalena

2017-09-01

Qualitative and quantitative modeling frameworks are widely used for analysis of biological regulatory networks, the former giving a preliminary overview of the system's global dynamics and the latter providing more detailed solutions. Another approach is to model biological regulatory networks as hybrid systems, i.e., systems which can display both continuous and discrete dynamic behaviors. Actually, the development of synthetic biology has shown that this is a suitable way to think about biological systems, which can often be constructed as networks with discrete controllers, and present hybrid behaviors. In this paper we discuss this approach as a special case of the reconfigurability paradigm, well studied in Computer Science (CS). In CS there are well developed computational tools to reason about hybrid systems. We argue that it is worth applying such tools in a biological context. One interesting tool is differential dynamic logic (dL), which has recently been developed by Platzer and applied to many case-studies. In this paper we discuss some simple examples of biological regulatory networks to illustrate how dL can be used as an alternative, or also as a complement to methods already used. Copyright © 2017 Elsevier Inc. All rights reserved.

Hybrid Discrete-Continuous Markov Decision Processes

NASA Technical Reports Server (NTRS)

Feng, Zhengzhu; Dearden, Richard; Meuleau, Nicholas; Washington, Rich

2003-01-01

This paper proposes a Markov decision process (MDP) model that features both discrete and continuous state variables. We extend previous work by Boyan and Littman on the mono-dimensional time-dependent MDP to multiple dimensions. We present the principle of lazy discretization, and piecewise constant and linear approximations of the model. Having to deal with several continuous dimensions raises several new problems that require new solutions. In the (piecewise) linear case, we use techniques from partially- observable MDPs (POMDPS) to represent value functions as sets of linear functions attached to different partitions of the state space.

Discrete-event simulation of a wide-area health care network.

PubMed Central

McDaniel, J G

1995-01-01

OBJECTIVE: Predict the behavior and estimate the telecommunication cost of a wide-area message store-and-forward network for health care providers that uses the telephone system. DESIGN: A tool with which to perform large-scale discrete-event simulations was developed. Network models for star and mesh topologies were constructed to analyze the differences in performances and telecommunication costs. The distribution of nodes in the network models approximates the distribution of physicians, hospitals, medical labs, and insurers in the Province of Saskatchewan, Canada. Modeling parameters were based on measurements taken from a prototype telephone network and a survey conducted at two medical clinics. Simulation studies were conducted for both topologies. RESULTS: For either topology, the telecommunication cost of a network in Saskatchewan is projected to be less than $100 (Canadian) per month per node. The estimated telecommunication cost of the star topology is approximately half that of the mesh. Simulations predict that a mean end-to-end message delivery time of two hours or less is achievable at this cost. A doubling of the data volume results in an increase of less than 50% in the mean end-to-end message transfer time. CONCLUSION: The simulation models provided an estimate of network performance and telecommunication cost in a specific Canadian province. At the expected operating point, network performance appeared to be relatively insensitive to increases in data volume. Similar results might be anticipated in other rural states and provinces in North America where a telephone-based network is desired. PMID:7583646

Improving link prediction in complex networks by adaptively exploiting multiple structural features of networks

NASA Astrophysics Data System (ADS)

Ma, Chuang; Bao, Zhong-Kui; Zhang, Hai-Feng

2017-10-01

So far, many network-structure-based link prediction methods have been proposed. However, these methods only highlight one or two structural features of networks, and then use the methods to predict missing links in different networks. The performances of these existing methods are not always satisfied in all cases since each network has its unique underlying structural features. In this paper, by analyzing different real networks, we find that the structural features of different networks are remarkably different. In particular, even in the same network, their inner structural features are utterly different. Therefore, more structural features should be considered. However, owing to the remarkably different structural features, the contributions of different features are hard to be given in advance. Inspired by these facts, an adaptive fusion model regarding link prediction is proposed to incorporate multiple structural features. In the model, a logistic function combing multiple structural features is defined, then the weight of each feature in the logistic function is adaptively determined by exploiting the known structure information. Last, we use the "learnt" logistic function to predict the connection probabilities of missing links. According to our experimental results, we find that the performance of our adaptive fusion model is better than many similarity indices.

An outer approximation method for the road network design problem

PubMed Central

2018-01-01

Best investment in the road infrastructure or the network design is perceived as a fundamental and benchmark problem in transportation. Given a set of candidate road projects with associated costs, finding the best subset with respect to a limited budget is known as a bilevel Discrete Network Design Problem (DNDP) of NP-hard computationally complexity. We engage with the complexity with a hybrid exact-heuristic methodology based on a two-stage relaxation as follows: (i) the bilevel feature is relaxed to a single-level problem by taking the network performance function of the upper level into the user equilibrium traffic assignment problem (UE-TAP) in the lower level as a constraint. It results in a mixed-integer nonlinear programming (MINLP) problem which is then solved using the Outer Approximation (OA) algorithm (ii) we further relax the multi-commodity UE-TAP to a single-commodity MILP problem, that is, the multiple OD pairs are aggregated to a single OD pair. This methodology has two main advantages: (i) the method is proven to be highly efficient to solve the DNDP for a large-sized network of Winnipeg, Canada. The results suggest that within a limited number of iterations (as termination criterion), global optimum solutions are quickly reached in most of the cases; otherwise, good solutions (close to global optimum solutions) are found in early iterations. Comparative analysis of the networks of Gao and Sioux-Falls shows that for such a non-exact method the global optimum solutions are found in fewer iterations than those found in some analytically exact algorithms in the literature. (ii) Integration of the objective function among the constraints provides a commensurate capability to tackle the multi-objective (or multi-criteria) DNDP as well. PMID:29590111

Vpliv topoloskih lastnosti kompleksnih mrez in dinamicnih lastnosti sklopljenih celicnih oscilatorjev na kolektivno dinamiko

NASA Astrophysics Data System (ADS)

Markovic, Rene

This doctor thesis is both theoretical and applicative. In the theoretical part of the thesis, we examine how the interplay of dynamical features of oscillators and structural properties of complex networks affect the collective behavior of the system. We show, that weakly dissipative and flexible oscillators synchronize best in a broad scale network topology, whereas on the other hand strongly dissipative and rigid oscillators exhibit maximal synchronization in a scale-free network topology. We provide an analytical explanation for this phenomenon and validate it by implementing various continuous as well as discrete mathematical models that exhibit different levels of dynamical complexity. In the continuation, we additionally investigate how speed of signal transmission in the network affects the collective dynamic of the system. Our results show that besides an optimal network topology, also an optimal information transmission speed exists, at which the system reaches the highest degree of global synchronization. In the second part we apply the findings and the methodology from our theoretical studies to the examination of the collective pancreatic beta cell activity in the islets of Langerhans, which represents the main mechanism for the regulation of blood glucose homeostasis by the secretion of the hormone insulin. We show that the beta cells dynamics is not synchronized on the global scale of the whole islets. Instead, the cells form local clusters of synchronized activity which tend to get less segregated under higher stimulatory glucose concentrations. Furthermore, higher glucose concentrations also lead to the presence of broad scale small world connectivity patterns in the functional beta cell network. The main findings thereby shed light on the physiology and collective behavior of the islets of Langerhans and point out the possibilities of pathological changes associated with changes in the intercellular communication pathways.

An outer approximation method for the road network design problem.

PubMed

Asadi Bagloee, Saeed; Sarvi, Majid

2018-01-01

Best investment in the road infrastructure or the network design is perceived as a fundamental and benchmark problem in transportation. Given a set of candidate road projects with associated costs, finding the best subset with respect to a limited budget is known as a bilevel Discrete Network Design Problem (DNDP) of NP-hard computationally complexity. We engage with the complexity with a hybrid exact-heuristic methodology based on a two-stage relaxation as follows: (i) the bilevel feature is relaxed to a single-level problem by taking the network performance function of the upper level into the user equilibrium traffic assignment problem (UE-TAP) in the lower level as a constraint. It results in a mixed-integer nonlinear programming (MINLP) problem which is then solved using the Outer Approximation (OA) algorithm (ii) we further relax the multi-commodity UE-TAP to a single-commodity MILP problem, that is, the multiple OD pairs are aggregated to a single OD pair. This methodology has two main advantages: (i) the method is proven to be highly efficient to solve the DNDP for a large-sized network of Winnipeg, Canada. The results suggest that within a limited number of iterations (as termination criterion), global optimum solutions are quickly reached in most of the cases; otherwise, good solutions (close to global optimum solutions) are found in early iterations. Comparative analysis of the networks of Gao and Sioux-Falls shows that for such a non-exact method the global optimum solutions are found in fewer iterations than those found in some analytically exact algorithms in the literature. (ii) Integration of the objective function among the constraints provides a commensurate capability to tackle the multi-objective (or multi-criteria) DNDP as well.

Neural/Bayes network predictor for inheritable cardiac disease pathogenicity and phenotype.

PubMed

Burghardt, Thomas P; Ajtai, Katalin

2018-04-11

The cardiac muscle sarcomere contains multiple proteins contributing to contraction energy transduction and its regulation during a heartbeat. Inheritable heart disease mutants affect most of them but none more frequently than the ventricular myosin motor and cardiac myosin binding protein c (mybpc3). These co-localizing proteins have mybpc3 playing a regulatory role to the energy transducing motor. Residue substitution and functional domain assignment of each mutation in the protein sequence decides, under the direction of a sensible disease model, phenotype and pathogenicity. The unknown model mechanism is decided here using a method combing neural and Bayes networks. Missense single nucleotide polymorphisms (SNPs) are clues for the disease mechanism summarized in an extensive database collecting mutant sequence location and residue substitution as independent variables that imply the dependent disease phenotype and pathogenicity characteristics in 4 dimensional data points (4ddps). The SNP database contains entries with the majority having one or both dependent data entries unfulfilled. A neural network relating causes (mutant residue location and substitution) and effects (phenotype and pathogenicity) is trained, validated, and optimized using fulfilled 4ddps. It then predicts unfulfilled 4ddps providing the implicit disease model. A discrete Bayes network interprets fulfilled and predicted 4ddps with conditional probabilities for phenotype and pathogenicity given mutation location and residue substitution thus relating the neural network implicit model to explicit features of the motor and mybpc3 sequence and structural domains. Neural/Bayes network forecasting automates disease mechanism modeling by leveraging the world wide human missense SNP database that is in place and expanding. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Discrete-state phasor neural networks

NASA Astrophysics Data System (ADS)

Noest, André J.

1988-08-01

An associative memory network with local variables assuming one of q equidistant positions on the unit circle (q-state phasors) is introduced, and its recall behavior is solved exactly for any q when the interactions are sparse and asymmetric. Such models can describe natural or artifical networks of (neuro-)biological, chemical, or electronic limit-cycle oscillators with q-fold instead of circular symmetry, or similar optical computing devices using a phase-encoded data representation.

Regional Seismic Arrays and Nuclear Test Ban Verification

DTIC Science & Technology

1990-12-01

estimation has been difficult to automate, at least for regional and teleseismic signals. A neural network approach might be applicable here. The data must...use of trained neural networks . Of the 95 events examined, 66 were selected for the classification study based on high signal-to-noise ratio and...the International Joint Conference on Neural Networks , Washington, D.C., June, 1989. Menke, W. Geophysical Data Analysis : Discrete Inverse Theory

Economic Feasibility of Wireless Sensor Network-Based Service Provision in a Duopoly Setting with a Monopolist Operator

PubMed Central

Romero, Julián; Sacoto-Cabrera, Erwin J.

2017-01-01

We analyze the feasibility of providing Wireless Sensor Network-data-based services in an Internet of Things scenario from an economical point of view. The scenario has two competing service providers with their own private sensor networks, a network operator and final users. The scenario is analyzed as two games using game theory. In the first game, sensors decide to subscribe or not to the network operator to upload the collected sensing-data, based on a utility function related to the mean service time and the price charged by the operator. In the second game, users decide to subscribe or not to the sensor-data-based service of the service providers based on a Logit discrete choice model related to the quality of the data collected and the subscription price. The sinks and users subscription stages are analyzed using population games and discrete choice models, while network operator and service providers pricing stages are analyzed using optimization and Nash equilibrium concepts respectively. The model is shown feasible from an economic point of view for all the actors if there are enough interested final users and opens the possibility of developing more efficient models with different types of services. PMID:29186847

Using Neural Networks in the Mapping of Mixed Discrete/Continuous Design Spaces With Application to Structural Design

DTIC Science & Technology

1994-02-01

desired that the problem to which the design space mapping techniques were applied be easily analyzed, yet provide a design space with realistic complexity...consistent fully stressed solution. 3 DESIGN SPACE MAPPING In order to reduce the computational expense required to optimize design spaces, neural networks...employed in this study. Some of the issues involved in using neural networks to do design space mapping are how to configure the neural network, how much

Using Discrete Loss Functions and Weighted Kappa for Classification: An Illustration Based on Bayesian Network Analysis

ERIC Educational Resources Information Center

Zwick, Rebecca; Lenaburg, Lubella

2009-01-01

In certain data analyses (e.g., multiple discriminant analysis and multinomial log-linear modeling), classification decisions are made based on the estimated posterior probabilities that individuals belong to each of several distinct categories. In the Bayesian network literature, this type of classification is often accomplished by assigning…

Retaining both discrete and smooth features in 1D and 2D NMR relaxation and diffusion experiments

NASA Astrophysics Data System (ADS)

Reci, A.; Sederman, A. J.; Gladden, L. F.

2017-11-01

A new method of regularization of 1D and 2D NMR relaxation and diffusion experiments is proposed and a robust algorithm for its implementation is introduced. The new form of regularization, termed the Modified Total Generalized Variation (MTGV) regularization, offers a compromise between distinguishing discrete and smooth features in the reconstructed distributions. The method is compared to the conventional method of Tikhonov regularization and the recently proposed method of L1 regularization, when applied to simulated data of 1D spin-lattice relaxation, T1, 1D spin-spin relaxation, T2, and 2D T1-T2 NMR experiments. A range of simulated distributions composed of two lognormally distributed peaks were studied. The distributions differed with regard to the variance of the peaks, which were designed to investigate a range of distributions containing only discrete, only smooth or both features in the same distribution. Three different signal-to-noise ratios were studied: 2000, 200 and 20. A new metric is proposed to compare the distributions reconstructed from the different regularization methods with the true distributions. The metric is designed to penalise reconstructed distributions which show artefact peaks. Based on this metric, MTGV regularization performs better than Tikhonov and L1 regularization in all cases except when the distribution is known to only comprise of discrete peaks, in which case L1 regularization is slightly more accurate than MTGV regularization.

Fast state estimation subject to random data loss in discrete-time nonlinear stochastic systems

NASA Astrophysics Data System (ADS)

Mahdi Alavi, S. M.; Saif, Mehrdad

2013-12-01

This paper focuses on the design of the standard observer in discrete-time nonlinear stochastic systems subject to random data loss. By the assumption that the system response is incrementally bounded, two sufficient conditions are subsequently derived that guarantee exponential mean-square stability and fast convergence of the estimation error for the problem at hand. An efficient algorithm is also presented to obtain the observer gain. Finally, the proposed methodology is employed for monitoring the Continuous Stirred Tank Reactor (CSTR) via a wireless communication network. The effectiveness of the designed observer is extensively assessed by using an experimental tested-bed that has been fabricated for performance evaluation of the over wireless-network estimation techniques under realistic radio channel conditions.

Discrete Radon transform has an exact, fast inverse and generalizes to operations other than sums along lines

PubMed Central

Press, William H.

2006-01-01

Götz, Druckmüller, and, independently, Brady have defined a discrete Radon transform (DRT) that sums an image's pixel values along a set of aptly chosen discrete lines, complete in slope and intercept. The transform is fast, O(N2log N) for an N × N image; it uses only addition, not multiplication or interpolation, and it admits a fast, exact algorithm for the adjoint operation, namely backprojection. This paper shows that the transform additionally has a fast, exact (although iterative) inverse. The inverse reproduces to machine accuracy the pixel-by-pixel values of the original image from its DRT, without artifacts or a finite point-spread function. Fourier or fast Fourier transform methods are not used. The inverse can also be calculated from sampled sinograms and is well conditioned in the presence of noise. Also introduced are generalizations of the DRT that combine pixel values along lines by operations other than addition. For example, there is a fast transform that calculates median values along all discrete lines and is able to detect linear features at low signal-to-noise ratios in the presence of pointlike clutter features of arbitrarily large amplitude. PMID:17159155

Discrete Radon transform has an exact, fast inverse and generalizes to operations other than sums along lines.

PubMed

Press, William H

2006-12-19

Götz, Druckmüller, and, independently, Brady have defined a discrete Radon transform (DRT) that sums an image's pixel values along a set of aptly chosen discrete lines, complete in slope and intercept. The transform is fast, O(N2log N) for an N x N image; it uses only addition, not multiplication or interpolation, and it admits a fast, exact algorithm for the adjoint operation, namely backprojection. This paper shows that the transform additionally has a fast, exact (although iterative) inverse. The inverse reproduces to machine accuracy the pixel-by-pixel values of the original image from its DRT, without artifacts or a finite point-spread function. Fourier or fast Fourier transform methods are not used. The inverse can also be calculated from sampled sinograms and is well conditioned in the presence of noise. Also introduced are generalizations of the DRT that combine pixel values along lines by operations other than addition. For example, there is a fast transform that calculates median values along all discrete lines and is able to detect linear features at low signal-to-noise ratios in the presence of pointlike clutter features of arbitrarily large amplitude.

Integrated simulation of continuous-scale and discrete-scale radiative transfer in metal foams

NASA Astrophysics Data System (ADS)

Xia, Xin-Lin; Li, Yang; Sun, Chuang; Ai, Qing; Tan, He-Ping

2018-06-01

A novel integrated simulation of radiative transfer in metal foams is presented. It integrates the continuous-scale simulation with the direct discrete-scale simulation in a single computational domain. It relies on the coupling of the real discrete-scale foam geometry with the equivalent continuous-scale medium through a specially defined scale-coupled zone. This zone holds continuous but nonhomogeneous volumetric radiative properties. The scale-coupled approach is compared to the traditional continuous-scale approach using volumetric radiative properties in the equivalent participating medium and to the direct discrete-scale approach employing the real 3D foam geometry obtained by computed tomography. All the analyses are based on geometrical optics. The Monte Carlo ray-tracing procedure is used for computations of the absorbed radiative fluxes and the apparent radiative behaviors of metal foams. The results obtained by the three approaches are in tenable agreement. The scale-coupled approach is fully validated in calculating the apparent radiative behaviors of metal foams composed of very absorbing to very reflective struts and that composed of very rough to very smooth struts. This new approach leads to a reduction in computational time by approximately one order of magnitude compared to the direct discrete-scale approach. Meanwhile, it can offer information on the local geometry-dependent feature and at the same time the equivalent feature in an integrated simulation. This new approach is promising to combine the advantages of the continuous-scale approach (rapid calculations) and direct discrete-scale approach (accurate prediction of local radiative quantities).

Adaptive hybrid simulations for multiscale stochastic reaction networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hepp, Benjamin; Gupta, Ankit; Khammash, Mustafa

2015-01-21

The probability distribution describing the state of a Stochastic Reaction Network (SRN) evolves according to the Chemical Master Equation (CME). It is common to estimate its solution using Monte Carlo methods such as the Stochastic Simulation Algorithm (SSA). In many cases, these simulations can take an impractical amount of computational time. Therefore, many methods have been developed that approximate sample paths of the underlying stochastic process and estimate the solution of the CME. A prominent class of these methods include hybrid methods that partition the set of species and the set of reactions into discrete and continuous subsets. Such amore » partition separates the dynamics into a discrete and a continuous part. Simulating such a stochastic process can be computationally much easier than simulating the exact discrete stochastic process with SSA. Moreover, the quasi-stationary assumption to approximate the dynamics of fast subnetworks can be applied for certain classes of networks. However, as the dynamics of a SRN evolves, these partitions may have to be adapted during the simulation. We develop a hybrid method that approximates the solution of a CME by automatically partitioning the reactions and species sets into discrete and continuous components and applying the quasi-stationary assumption on identifiable fast subnetworks. Our method does not require any user intervention and it adapts to exploit the changing timescale separation between reactions and/or changing magnitudes of copy-numbers of constituent species. We demonstrate the efficiency of the proposed method by considering examples from systems biology and showing that very good approximations to the exact probability distributions can be achieved in significantly less computational time. This is especially the case for systems with oscillatory dynamics, where the system dynamics change considerably throughout the time-period of interest.« less

Adaptive hybrid simulations for multiscale stochastic reaction networks.

PubMed

Hepp, Benjamin; Gupta, Ankit; Khammash, Mustafa

2015-01-21

The probability distribution describing the state of a Stochastic Reaction Network (SRN) evolves according to the Chemical Master Equation (CME). It is common to estimate its solution using Monte Carlo methods such as the Stochastic Simulation Algorithm (SSA). In many cases, these simulations can take an impractical amount of computational time. Therefore, many methods have been developed that approximate sample paths of the underlying stochastic process and estimate the solution of the CME. A prominent class of these methods include hybrid methods that partition the set of species and the set of reactions into discrete and continuous subsets. Such a partition separates the dynamics into a discrete and a continuous part. Simulating such a stochastic process can be computationally much easier than simulating the exact discrete stochastic process with SSA. Moreover, the quasi-stationary assumption to approximate the dynamics of fast subnetworks can be applied for certain classes of networks. However, as the dynamics of a SRN evolves, these partitions may have to be adapted during the simulation. We develop a hybrid method that approximates the solution of a CME by automatically partitioning the reactions and species sets into discrete and continuous components and applying the quasi-stationary assumption on identifiable fast subnetworks. Our method does not require any user intervention and it adapts to exploit the changing timescale separation between reactions and/or changing magnitudes of copy-numbers of constituent species. We demonstrate the efficiency of the proposed method by considering examples from systems biology and showing that very good approximations to the exact probability distributions can be achieved in significantly less computational time. This is especially the case for systems with oscillatory dynamics, where the system dynamics change considerably throughout the time-period of interest.

Statistical Inferences from the Topology of Complex Networks

DTIC Science & Technology

2016-10-04

stable, does not lose any information, has continuous and discrete versions, and obeys a strong law of large numbers and a central limit theorem. The...paper (with J.A. Scott) “Categorification of persistent homology” [7] in the journal Discrete and Computational Geome- try and the paper “Metrics for...Generalized Persistence Modules” (with J.A. Scott and V. de Silva) in the journal Foundations of Computational Math - ematics [5]. These papers develop

A New ’Availability-Payment’ Model for Pricing Performance-Based Logistics Contracts

DTIC Science & Technology

2014-04-30

maintenance network connected to the inventory and Original Equipment Manufacturer (OEM) used in this paper. The input to the Petri net in Figure 2 is the...contract structures. The model developed in this paper uses an affine controller to drive a discrete event simulator ( Petri net ) that produces...discrete event simulator ( Petri net ) that produces availability and cost measures. The model is used to explore the optimum availability assessment

Stochastic flux analysis of chemical reaction networks

PubMed Central

2013-01-01

Background Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes. Results We describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology. Conclusions We provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network. PMID:24314153

Stochastic flux analysis of chemical reaction networks.

PubMed

Kahramanoğulları, Ozan; Lynch, James F

2013-12-07

Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes. We describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology. We provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network.

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

DOE PAGES

Hyman, Jeffrey De'Haven; Aldrich, Garrett Allen; Viswanathan, Hari S.; ...

2016-08-01

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semicorrelation, and noncorrelation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected somore » that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. Lastly, these observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.« less

Fracture size and transmissivity correlations: Implications for transport simulations in sparse three-dimensional discrete fracture networks following a truncated power law distribution of fracture size

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hyman, Jeffrey De'Haven; Aldrich, Garrett Allen; Viswanathan, Hari S.

We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semicorrelation, and noncorrelation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated power law distribution. The distribution of transmissivities are selected somore » that the mean transmissivity of the fracture networks are the same and the distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. Lastly, these observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics.« less

An integrative approach for analyzing hundreds of neurons in task performing mice using wide-field calcium imaging.

PubMed

Mohammed, Ali I; Gritton, Howard J; Tseng, Hua-an; Bucklin, Mark E; Yao, Zhaojie; Han, Xue

2016-02-08

Advances in neurotechnology have been integral to the investigation of neural circuit function in systems neuroscience. Recent improvements in high performance fluorescent sensors and scientific CMOS cameras enables optical imaging of neural networks at a much larger scale. While exciting technical advances demonstrate the potential of this technique, further improvement in data acquisition and analysis, especially those that allow effective processing of increasingly larger datasets, would greatly promote the application of optical imaging in systems neuroscience. Here we demonstrate the ability of wide-field imaging to capture the concurrent dynamic activity from hundreds to thousands of neurons over millimeters of brain tissue in behaving mice. This system allows the visualization of morphological details at a higher spatial resolution than has been previously achieved using similar functional imaging modalities. To analyze the expansive data sets, we developed software to facilitate rapid downstream data processing. Using this system, we show that a large fraction of anatomically distinct hippocampal neurons respond to discrete environmental stimuli associated with classical conditioning, and that the observed temporal dynamics of transient calcium signals are sufficient for exploring certain spatiotemporal features of large neural networks.

The Environmental Impact of Cambodia's Ancient City of Mahendraparvata (Phnom Kulen)

PubMed Central

Penny, Dan; Chevance, Jean-Baptiste; Tang, David; De Greef, Stéphane

2014-01-01

The Khmer kingdom, whose capital was at Angkor from the 9th to the 14th-15th century, was founded in 802 by king Jayavarman II in a city called Mahandraparvata, on Phnom Kulen. Virtually nothing more is known of Mahandraparvata from the epigraphic sources, but systematic archaeological survey and excavation have identified an array of cultural features that point to a more extensive and enduring settlement than the historical record indicates. Recent remote sensing data have revolutionized our view, revealing the remains of a city with a complex and spatially extensive network of urban infrastructure. Here, we present a record of vegetation change and soil erosion from within that urban network, dating from the 8th century CE. Our findings indicate approximately 400 years of intensive land use, punctuated by discrete periods of intense erosion beginning in the mid 9th century and ending in the late 11th century. A marked change in water management practices is apparent from the 12th century CE, with implications for water supply to Angkor itself. This is the first indication that settlement on Mahendraparvata was not only extensive, but also intensive and enduring, with a marked environmental impact. PMID:24416206

The environmental impact of Cambodia's ancient city of Mahendraparvata (Phnom Kulen).

PubMed

Penny, Dan; Chevance, Jean-Baptiste; Tang, David; De Greef, Stéphane

2014-01-01

The Khmer kingdom, whose capital was at Angkor from the 9(th) to the 14(th)-15(th) century, was founded in 802 by king Jayavarman II in a city called Mahandraparvata, on Phnom Kulen. Virtually nothing more is known of Mahandraparvata from the epigraphic sources, but systematic archaeological survey and excavation have identified an array of cultural features that point to a more extensive and enduring settlement than the historical record indicates. Recent remote sensing data have revolutionized our view, revealing the remains of a city with a complex and spatially extensive network of urban infrastructure. Here, we present a record of vegetation change and soil erosion from within that urban network, dating from the 8(th) century CE. Our findings indicate approximately 400 years of intensive land use, punctuated by discrete periods of intense erosion beginning in the mid 9(th) century and ending in the late 11(th) century. A marked change in water management practices is apparent from the 12(th) century CE, with implications for water supply to Angkor itself. This is the first indication that settlement on Mahendraparvata was not only extensive, but also intensive and enduring, with a marked environmental impact.

Millimeter-scale epileptiform spike propagation patterns and their relationship to seizures

PubMed Central

Vanleer, Ann C; Blanco, Justin A; Wagenaar, Joost B; Viventi, Jonathan; Contreras, Diego; Litt, Brian

2016-01-01

Objective Current mapping of epileptic networks in patients prior to epilepsy surgery utilizes electrode arrays with sparse spatial sampling (∼1.0 cm inter-electrode spacing). Recent research demonstrates that sub-millimeter, cortical-column-scale domains have a role in seizure generation that may be clinically significant. We use high-resolution, active, flexible surface electrode arrays with 500 μm inter-electrode spacing to explore epileptiform local field potential spike propagation patterns in two dimensions recorded from subdural micro-electrocorticographic signals in vivo in cat. In this study, we aimed to develop methods to quantitatively characterize the spatiotemporal dynamics of epileptiform activity at high-resolution. Approach We topically administered a GABA-antagonist, picrotoxin, to induce acute neocortical epileptiform activity leading up to discrete electrographic seizures. We extracted features from local field potential spikes to characterize spatiotemporal patterns in these events. We then tested the hypothesis that two dimensional spike patterns during seizures were different from those between seizures. Main results We showed that spatially correlated events can be used to distinguish ictal versus interictal spikes. Significance We conclude that sub-millimeter-scale spatiotemporal spike patterns reveal network dynamics that are invisible to standard clinical recordings and contain information related to seizure-state. PMID:26859260

Fracture-permeability behavior of shale

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carey, J. William; Lei, Zhou; Rougier, Esteban

The fracture-permeability behavior of Utica shale, an important play for shale gas and oil, was investigated using a triaxial coreflood device and X-ray tomography in combination with finite-discrete element modeling (FDEM). Fractures generated in both compression and in a direct-shear configuration allowed permeability to be measured across the faces of cylindrical core. Shale with bedding planes perpendicular to direct-shear loading developed complex fracture networks and peak permeability of 30 mD that fell to 5 mD under hydrostatic conditions. Shale with bedding planes parallel to shear loading developed simple fractures with peak permeability as high as 900 mD. In addition tomore » the large anisotropy in fracture permeability, the amount of deformation required to initiate fractures was greater for perpendicular layering (about 1% versus 0.4%), and in both cases activation of existing fractures are more likely sources of permeability in shale gas plays or damaged caprock in CO₂ sequestration because of the significant deformation required to form new fracture networks. FDEM numerical simulations were able to replicate the main features of the fracturing processes while showing the importance of fluid penetration into fractures as well as layering in determining fracture patterns.« less

Millimeter-scale epileptiform spike propagation patterns and their relationship to seizures

NASA Astrophysics Data System (ADS)

Vanleer, Ann C.; Blanco, Justin A.; Wagenaar, Joost B.; Viventi, Jonathan; Contreras, Diego; Litt, Brian

2016-04-01

Objective. Current mapping of epileptic networks in patients prior to epilepsy surgery utilizes electrode arrays with sparse spatial sampling (∼1.0 cm inter-electrode spacing). Recent research demonstrates that sub-millimeter, cortical-column-scale domains have a role in seizure generation that may be clinically significant. We use high-resolution, active, flexible surface electrode arrays with 500 μm inter-electrode spacing to explore epileptiform local field potential (LFP) spike propagation patterns in two dimensions recorded from subdural micro-electrocorticographic signals in vivo in cat. In this study, we aimed to develop methods to quantitatively characterize the spatiotemporal dynamics of epileptiform activity at high-resolution. Approach. We topically administered a GABA-antagonist, picrotoxin, to induce acute neocortical epileptiform activity leading up to discrete electrographic seizures. We extracted features from LFP spikes to characterize spatiotemporal patterns in these events. We then tested the hypothesis that two-dimensional spike patterns during seizures were different from those between seizures. Main results. We showed that spatially correlated events can be used to distinguish ictal versus interictal spikes. Significance. We conclude that sub-millimeter-scale spatiotemporal spike patterns reveal network dynamics that are invisible to standard clinical recordings and contain information related to seizure-state.

Real-time parameter optimization based on neural network for smart injection molding

NASA Astrophysics Data System (ADS)

Lee, H.; Liau, Y.; Ryu, K.

2018-03-01

The manufacturing industry has been facing several challenges, including sustainability, performance and quality of production. Manufacturers attempt to enhance the competitiveness of companies by implementing CPS (Cyber-Physical Systems) through the convergence of IoT(Internet of Things) and ICT(Information & Communication Technology) in the manufacturing process level. Injection molding process has a short cycle time and high productivity. This features have been making it suitable for mass production. In addition, this process is used to produce precise parts in various industry fields such as automobiles, optics and medical devices. Injection molding process has a mixture of discrete and continuous variables. In order to optimized the quality, variables that is generated in the injection molding process must be considered. Furthermore, Optimal parameter setting is time-consuming work to predict the optimum quality of the product. Since the process parameter cannot be easily corrected during the process execution. In this research, we propose a neural network based real-time process parameter optimization methodology that sets optimal process parameters by using mold data, molding machine data, and response data. This paper is expected to have academic contribution as a novel study of parameter optimization during production compare with pre - production parameter optimization in typical studies.

Fracture-permeability behavior of shale

DOE PAGES

Carey, J. William; Lei, Zhou; Rougier, Esteban; ...

2015-05-08

The fracture-permeability behavior of Utica shale, an important play for shale gas and oil, was investigated using a triaxial coreflood device and X-ray tomography in combination with finite-discrete element modeling (FDEM). Fractures generated in both compression and in a direct-shear configuration allowed permeability to be measured across the faces of cylindrical core. Shale with bedding planes perpendicular to direct-shear loading developed complex fracture networks and peak permeability of 30 mD that fell to 5 mD under hydrostatic conditions. Shale with bedding planes parallel to shear loading developed simple fractures with peak permeability as high as 900 mD. In addition tomore » the large anisotropy in fracture permeability, the amount of deformation required to initiate fractures was greater for perpendicular layering (about 1% versus 0.4%), and in both cases activation of existing fractures are more likely sources of permeability in shale gas plays or damaged caprock in CO₂ sequestration because of the significant deformation required to form new fracture networks. FDEM numerical simulations were able to replicate the main features of the fracturing processes while showing the importance of fluid penetration into fractures as well as layering in determining fracture patterns.« less

Parallel numerical modeling of hybrid-dimensional compositional non-isothermal Darcy flows in fractured porous media

NASA Astrophysics Data System (ADS)

Xing, F.; Masson, R.; Lopez, S.

2017-09-01

This paper introduces a new discrete fracture model accounting for non-isothermal compositional multiphase Darcy flows and complex networks of fractures with intersecting, immersed and non-immersed fractures. The so called hybrid-dimensional model using a 2D model in the fractures coupled with a 3D model in the matrix is first derived rigorously starting from the equi-dimensional matrix fracture model. Then, it is discretized using a fully implicit time integration combined with the Vertex Approximate Gradient (VAG) finite volume scheme which is adapted to polyhedral meshes and anisotropic heterogeneous media. The fully coupled systems are assembled and solved in parallel using the Single Program Multiple Data (SPMD) paradigm with one layer of ghost cells. This strategy allows for a local assembly of the discrete systems. An efficient preconditioner is implemented to solve the linear systems at each time step and each Newton type iteration of the simulation. The numerical efficiency of our approach is assessed on different meshes, fracture networks, and physical settings in terms of parallel scalability, nonlinear convergence and linear convergence.

Entropy-conservative spatial discretization of the multidimensional quasi-gasdynamic system of equations

NASA Astrophysics Data System (ADS)

Zlotnik, A. A.

2017-04-01

The multidimensional quasi-gasdynamic system written in the form of mass, momentum, and total energy balance equations for a perfect polytropic gas with allowance for a body force and a heat source is considered. A new conservative symmetric spatial discretization of these equations on a nonuniform rectangular grid is constructed (with the basic unknown functions—density, velocity, and temperature—defined on a common grid and with fluxes and viscous stresses defined on staggered grids). Primary attention is given to the analysis of entropy behavior: the discretization is specially constructed so that the total entropy does not decrease. This is achieved via a substantial revision of the standard discretization and applying numerous original features. A simplification of the constructed discretization serves as a conservative discretization with nondecreasing total entropy for the simpler quasi-hydrodynamic system of equations. In the absence of regularizing terms, the results also hold for the Navier-Stokes equations of a viscous compressible heat-conducting gas.

Superelastic and pH-Responsive Degradable Dendrimer Cryogels Prepared by Cryo-aza-Michael Addition Reaction.

PubMed

Wang, Juan; Yang, Hu

2018-05-08

Dendrimers exhibit super atomistic features by virtue of their well-defined discrete quantized nanoscale structures. Here, we show that hyperbranched amine-terminated polyamidoamine (PAMAM) dendrimer G4.0 reacts with linear polyethylene glycol (PEG) diacrylate (575 g/mol) via the aza-Michael addition reaction at a subzero temperature (-20 °C), namely cryo-aza-Michael addition, to form a macroporous superelastic network, i.e., dendrimer cryogel. Dendrimer cryogels exhibit biologically relevant Young's modulus, high compression elasticity and super resilience at ambient temperature. Furthermore, the dendrimer cryogels exhibit excellent rebound performance and do not show significant stress relaxation under cyclic deformation over a wide temperature range (-80 to 100 °C). The obtained dendrimer cryogels are stable at acidic pH but degrade quickly at physiological pH through self-triggered degradation. Taken together, dendrimer cryogels represent a new class of scaffolds with properties suitable for biomedical applications.

Digital microfluidics: A promising technique for biochemical applications

NASA Astrophysics Data System (ADS)

Wang, He; Chen, Liguo; Sun, Lining

2017-12-01

Digital microfluidics (DMF) is a versatile microfluidics technology that has significant application potential in the areas of automation and miniaturization. In DMF, discrete droplets containing samples and reagents are controlled to implement a series of operations via electrowetting-on-dielectric. This process works by applying electrical potentials to an array of electrodes coated with a hydrophobic dielectric layer. Unlike microchannels, DMF facilitates precise control over multiple reaction processes without using complex pump, microvalve, and tubing networks. DMF also presents other distinct features, such as portability, less sample consumption, shorter chemical reaction time, flexibility, and easier combination with other technology types. Due to its unique advantages, DMF has been applied to a broad range of fields (e.g., chemistry, biology, medicine, and environment). This study reviews the basic principles of droplet actuation, configuration design, and fabrication of the DMF device, as well as discusses the latest progress in DMF from the biochemistry perspective.

SNAP/SHOT Your Ability to Support That Next Application.

ERIC Educational Resources Information Center

Jones, Ernest L.

SNAP/SHOT (System Network Analysis Program-Simulated Host Overview Technique) is a discrete simulation of a network and/or host model available through IBM at the Raleigh System Center. The simulator provides an analysis of a total IBM Communications System. Input data must be obtained from RMF, SMF, and the CICS Analyzer to determine the existing…

Codimension-Two Bifurcation, Chaos and Control in a Discrete-Time Information Diffusion Model

NASA Astrophysics Data System (ADS)

Ren, Jingli; Yu, Liping

2016-12-01

In this paper, we present a discrete model to illustrate how two pieces of information interact with online social networks and investigate the dynamics of discrete-time information diffusion model in three types: reverse type, intervention type and mutualistic type. It is found that the model has orbits with period 2, 4, 6, 8, 12, 16, 20, 30, quasiperiodic orbit, and undergoes heteroclinic bifurcation near 1:2 point, a homoclinic structure near 1:3 resonance point and an invariant cycle bifurcated by period 4 orbit near 1:4 resonance point. Moreover, in order to regulate information diffusion process and information security, we give two control strategies, the hybrid control method and the feedback controller of polynomial functions, to control chaos, flip bifurcation, 1:2, 1:3 and 1:4 resonances, respectively, in the two-dimensional discrete system.

The electrical behavior of GaAs-insulator interfaces - A discrete energy interface state model

NASA Technical Reports Server (NTRS)

Kazior, T. E.; Lagowski, J.; Gatos, H. C.

1983-01-01

The relationship between the electrical behavior of GaAs Metal Insulator Semiconductor (MIS) structures and the high density discrete energy interface states (0.7 and 0.9 eV below the conduction band) was investigated utilizing photo- and thermal emission from the interface states in conjunction with capacitance measurements. It was found that all essential features of the anomalous behavior of GaAs MIS structures, such as the frequency dispersion and the C-V hysteresis, can be explained on the basis of nonequilibrium charging and discharging of the high density discrete energy interface states.

Plasma plume oscillations monitoring during laser welding of stainless steel by discrete wavelet transform application.

PubMed

Sibillano, Teresa; Ancona, Antonio; Rizzi, Domenico; Lupo, Valentina; Tricarico, Luigi; Lugarà, Pietro Mario

2010-01-01

The plasma optical radiation emitted during CO2 laser welding of stainless steel samples has been detected with a Si-PIN photodiode and analyzed under different process conditions. The discrete wavelet transform (DWT) has been used to decompose the optical signal into various discrete series of sequences over different frequency bands. The results show that changes of the process settings may yield different signal features in the range of frequencies between 200 Hz and 30 kHz. Potential applications of this method to monitor in real time the laser welding processes are also discussed.

Studies on thermokinetic of Chlorella pyrenoidosa devolatilization via different models.

PubMed

Chen, Zhihua; Lei, Jianshen; Li, Yunbei; Su, Xianfa; Hu, Zhiquan; Guo, Dabin

2017-11-01

The thermokinetics of Chlorella pyrenoidosa (CP) devolatilization were investigated based on iso-conversional model and different distributed activation energy models (DAEM). Iso-conversional process result showed that CP devolatilization roughly followed a single-step with mechanism function of f(α)=(1-α) 3 , and kinetic parameters pair of E 0 =180.5kJ/mol and A 0 =1.5E+13s -1 . Logistic distribution was the most suitable activation energy distribution function for CP devolatilization. Although reaction order n=3.3 was in accordance with iso-conversional process, Logistic DAEM could not detail the weight loss features since it presented as single-step reaction. The un-uniform feature of activation energy distribution in Miura-Maki DAEM, and weight fraction distribution in discrete DAEM reflected weight loss features. Due to the un-uniform distribution of activation and weight fraction, Miura-Maki DAEM and discreted DAEM could describe weight loss features. Copyright © 2017 Elsevier Ltd. All rights reserved.

A discrete event simulation model for evaluating the performances of an m/g/c/c state dependent queuing system.

PubMed

Khalid, Ruzelan; Nawawi, Mohd Kamal M; Kawsar, Luthful A; Ghani, Noraida A; Kamil, Anton A; Mustafa, Adli

2013-01-01

M/G/C/C state dependent queuing networks consider service rates as a function of the number of residing entities (e.g., pedestrians, vehicles, and products). However, modeling such dynamic rates is not supported in modern Discrete Simulation System (DES) software. We designed an approach to cater this limitation and used it to construct the M/G/C/C state-dependent queuing model in Arena software. Using the model, we have evaluated and analyzed the impacts of various arrival rates to the throughput, the blocking probability, the expected service time and the expected number of entities in a complex network topology. Results indicated that there is a range of arrival rates for each network where the simulation results fluctuate drastically across replications and this causes the simulation results and analytical results exhibit discrepancies. Detail results that show how tally the simulation results and the analytical results in both abstract and graphical forms and some scientific justifications for these have been documented and discussed.

Stability analysis for discrete-time stochastic memristive neural networks with both leakage and probabilistic delays.

PubMed

Liu, Hongjian; Wang, Zidong; Shen, Bo; Huang, Tingwen; Alsaadi, Fuad E

2018-06-01

This paper is concerned with the globally exponential stability problem for a class of discrete-time stochastic memristive neural networks (DSMNNs) with both leakage delays as well as probabilistic time-varying delays. For the probabilistic delays, a sequence of Bernoulli distributed random variables is utilized to determine within which intervals the time-varying delays fall at certain time instant. The sector-bounded activation function is considered in the addressed DSMNN. By taking into account the state-dependent characteristics of the network parameters and choosing an appropriate Lyapunov-Krasovskii functional, some sufficient conditions are established under which the underlying DSMNN is globally exponentially stable in the mean square. The derived conditions are made dependent on both the leakage and the probabilistic delays, and are therefore less conservative than the traditional delay-independent criteria. A simulation example is given to show the effectiveness of the proposed stability criterion. Copyright © 2018 Elsevier Ltd. All rights reserved.

Backpropagation and ordered derivatives in the time scales calculus.

PubMed

Seiffertt, John; Wunsch, Donald C

2010-08-01

Backpropagation is the most widely used neural network learning technique. It is based on the mathematical notion of an ordered derivative. In this paper, we present a formulation of ordered derivatives and the backpropagation training algorithm using the important emerging area of mathematics known as the time scales calculus. This calculus, with its potential for application to a wide variety of inter-disciplinary problems, is becoming a key area of mathematics. It is capable of unifying continuous and discrete analysis within one coherent theoretical framework. Using this calculus, we present here a generalization of backpropagation which is appropriate for cases beyond the specifically continuous or discrete. We develop a new multivariate chain rule of this calculus, define ordered derivatives on time scales, prove a key theorem about them, and derive the backpropagation weight update equations for a feedforward multilayer neural network architecture. By drawing together the time scales calculus and the area of neural network learning, we present the first connection of two major fields of research.

Modeling discrete combinatorial systems as alphabetic bipartite networks: theory and applications.

PubMed

Choudhury, Monojit; Ganguly, Niloy; Maiti, Abyayananda; Mukherjee, Animesh; Brusch, Lutz; Deutsch, Andreas; Peruani, Fernando

2010-03-01

Genes and human languages are discrete combinatorial systems (DCSs), in which the basic building blocks are finite sets of elementary units: nucleotides or codons in a DNA sequence, and letters or words in a language. Different combinations of these finite units give rise to potentially infinite numbers of genes or sentences. This type of DCSs can be represented as an alphabetic bipartite network (ABN) where there are two kinds of nodes, one type represents the elementary units while the other type represents their combinations. Here, we extend and generalize recent analytical findings for ABNs derived in [Peruani, Europhys. Lett. 79, 28001 (2007)] and empirically investigate two real world systems in terms of ABNs, the codon gene and the phoneme-language network. The one-mode projections onto the elementary basic units are also studied theoretically as well as in real world ABNs. We propose the use of ABNs as a means for inferring the mechanisms underlying the growth of real world DCSs.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Hang, E-mail: hangchen@mit.edu; Thill, Peter; Cao, Jianshu

In biochemical systems, intrinsic noise may drive the system switch from one stable state to another. We investigate how kinetic switching between stable states in a bistable network is influenced by dynamic disorder, i.e., fluctuations in the rate coefficients. Using the geometric minimum action method, we first investigate the optimal transition paths and the corresponding minimum actions based on a genetic toggle switch model in which reaction coefficients draw from a discrete probability distribution. For the continuous probability distribution of the rate coefficient, we then consider two models of dynamic disorder in which reaction coefficients undergo different stochastic processes withmore » the same stationary distribution. In one, the kinetic parameters follow a discrete Markov process and in the other they follow continuous Langevin dynamics. We find that regulation of the parameters modulating the dynamic disorder, as has been demonstrated to occur through allosteric control in bistable networks in the immune system, can be crucial in shaping the statistics of optimal transition paths, transition probabilities, and the stationary probability distribution of the network.« less

Modelling the Preferences of Students for Alternative Assignment Designs Using the Discrete Choice Experiment Methodology

ERIC Educational Resources Information Center

Kennelly, Brendan; Flannery, Darragh; Considine, John; Doherty, Edel; Hynes, Stephen

2014-01-01

This paper outlines how a discrete choice experiment (DCE) can be used to learn more about how students are willing to trade off various features of assignments such as the nature and timing of feedback and the method used to submit assignments. A DCE identifies plausible levels of the key attributes of a good or service and then presents the…

Analytic method for calculating properties of random walks on networks

NASA Technical Reports Server (NTRS)

Goldhirsch, I.; Gefen, Y.

1986-01-01

A method for calculating the properties of discrete random walks on networks is presented. The method divides complex networks into simpler units whose contribution to the mean first-passage time is calculated. The simplified network is then further iterated. The method is demonstrated by calculating mean first-passage times on a segment, a segment with a single dangling bond, a segment with many dangling bonds, and a looplike structure. The results are analyzed and related to the applicability of the Einstein relation between conductance and diffusion.

Optimal Discrete Event Supervisory Control of Aircraft Gas Turbine Engines

NASA Technical Reports Server (NTRS)

Litt, Jonathan (Technical Monitor); Ray, Asok

2004-01-01

This report presents an application of the recently developed theory of optimal Discrete Event Supervisory (DES) control that is based on a signed real measure of regular languages. The DES control techniques are validated on an aircraft gas turbine engine simulation test bed. The test bed is implemented on a networked computer system in which two computers operate in the client-server mode. Several DES controllers have been tested for engine performance and reliability.

Fixed-time synchronization of memristor-based BAM neural networks with time-varying discrete delay.

PubMed

Chen, Chuan; Li, Lixiang; Peng, Haipeng; Yang, Yixian

2017-12-01

This paper is devoted to studying the fixed-time synchronization of memristor-based BAM neural networks (MBAMNNs) with discrete delay. Fixed-time synchronization means that synchronization can be achieved in a fixed time for any initial values of the considered systems. In the light of the double-layer structure of MBAMNNs, we design two similar feedback controllers. Based on Lyapunov stability theories, several criteria are established to guarantee that the drive and response MBAMNNs can realize synchronization in a fixed time. In particular, by changing the parameters of controllers, this fixed time can be adjusted to some desired value in advance, irrespective of the initial values of MBAMNNs. Numerical simulations are included to validate the derived results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Robust stochastic stability of discrete-time fuzzy Markovian jump neural networks.

PubMed

Arunkumar, A; Sakthivel, R; Mathiyalagan, K; Park, Ju H

2014-07-01

This paper focuses the issue of robust stochastic stability for a class of uncertain fuzzy Markovian jumping discrete-time neural networks (FMJDNNs) with various activation functions and mixed time delay. By employing the Lyapunov technique and linear matrix inequality (LMI) approach, a new set of delay-dependent sufficient conditions are established for the robust stochastic stability of uncertain FMJDNNs. More precisely, the parameter uncertainties are assumed to be time varying, unknown and norm bounded. The obtained stability conditions are established in terms of LMIs, which can be easily checked by using the efficient MATLAB-LMI toolbox. Finally, numerical examples with simulation result are provided to illustrate the effectiveness and less conservativeness of the obtained results. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Exponential synchronization of neural networks with discrete and distributed delays under time-varying sampling.

PubMed

Wu, Zheng-Guang; Shi, Peng; Su, Hongye; Chu, Jian

2012-09-01

This paper investigates the problem of master-slave synchronization for neural networks with discrete and distributed delays under variable sampling with a known upper bound on the sampling intervals. An improved method is proposed, which captures the characteristic of sampled-data systems. Some delay-dependent criteria are derived to ensure the exponential stability of the error systems, and thus the master systems synchronize with the slave systems. The desired sampled-data controller can be achieved by solving a set of linear matrix inequalitys, which depend upon the maximum sampling interval and the decay rate. The obtained conditions not only have less conservatism but also have less decision variables than existing results. Simulation results are given to show the effectiveness and benefits of the proposed methods.

Chaotification of complex networks with impulsive control.

PubMed

Guan, Zhi-Hong; Liu, Feng; Li, Juan; Wang, Yan-Wu

2012-06-01

This paper investigates the chaotification problem of complex dynamical networks (CDN) with impulsive control. Both the discrete and continuous cases are studied. The method is presented to drive all states of every node in CDN to chaos. The proposed impulsive control strategy is effective for both the originally stable and unstable CDN. The upper bound of the impulse intervals for originally stable networks is derived. Finally, the effectiveness of the theoretical results is verified by numerical examples.

Full Service ISDN Satellite (FSIS) network model for advanced ISDN satellite design and experiments

NASA Technical Reports Server (NTRS)

Pepin, Gerard R.

1992-01-01

The Full Service Integrated Services Digital Network (FSIS) network model for advanced satellite designs describes a model suitable for discrete event simulations. A top down model design uses the Advanced Communications Technology Satellite (ACTS) as its basis. The ACTS and the Interim Service ISDN Satellite (ISIS) perform ISDN protocol analyses and switching decisions in the terrestrial domain, whereas FSIS makes all its analyses and decisions on-board the ISDN satellite.

LMI designmethod for networked-based PID control

NASA Astrophysics Data System (ADS)

Souza, Fernando de Oliveira; Mozelli, Leonardo Amaral; de Oliveira, Maurício Carvalho; Palhares, Reinaldo Martinez

2016-10-01

In this paper, we propose a methodology for the design of networked PID controllers for second-order delayed processes using linear matrix inequalities. The proposed procedure takes into account time-varying delay on the plant, time-varying delays induced by the network and packed dropouts. The design is carried on entirely using a continuous-time model of the closed-loop system where time-varying delays are used to represent sampling and holding occurring in a discrete-time digital PID controller.

Defense Strategies for Asymmetric Networked Systems with Discrete Components.

PubMed

Rao, Nageswara S V; Ma, Chris Y T; Hausken, Kjell; He, Fei; Yau, David K Y; Zhuang, Jun

2018-05-03

We consider infrastructures consisting of a network of systems, each composed of discrete components. The network provides the vital connectivity between the systems and hence plays a critical, asymmetric role in the infrastructure operations. The individual components of the systems can be attacked by cyber and physical means and can be appropriately reinforced to withstand these attacks. We formulate the problem of ensuring the infrastructure performance as a game between an attacker and a provider, who choose the numbers of the components of the systems and network to attack and reinforce, respectively. The costs and benefits of attacks and reinforcements are characterized using the sum-form, product-form and composite utility functions, each composed of a survival probability term and a component cost term. We present a two-level characterization of the correlations within the infrastructure: (i) the aggregate failure correlation function specifies the infrastructure failure probability given the failure of an individual system or network, and (ii) the survival probabilities of the systems and network satisfy first-order differential conditions that capture the component-level correlations using multiplier functions. We derive Nash equilibrium conditions that provide expressions for individual system survival probabilities and also the expected infrastructure capacity specified by the total number of operational components. We apply these results to derive and analyze defense strategies for distributed cloud computing infrastructures using cyber-physical models.

Distributed fault detection over sensor networks with Markovian switching topologies

NASA Astrophysics Data System (ADS)

Ge, Xiaohua; Han, Qing-Long

2014-05-01

This paper deals with the distributed fault detection for discrete-time Markov jump linear systems over sensor networks with Markovian switching topologies. The sensors are scatteredly deployed in the sensor field and the fault detectors are physically distributed via a communication network. The system dynamics changes and sensing topology variations are modeled by a discrete-time Markov chain with incomplete mode transition probabilities. Each of these sensor nodes firstly collects measurement outputs from its all underlying neighboring nodes, processes these data in accordance with the Markovian switching topologies, and then transmits the processed data to the remote fault detector node. Network-induced delays and accumulated data packet dropouts are incorporated in the data transmission between the sensor nodes and the distributed fault detector nodes through the communication network. To generate localized residual signals, mode-independent distributed fault detection filters are proposed. By means of the stochastic Lyapunov functional approach, the residual system performance analysis is carried out such that the overall residual system is stochastically stable and the error between each residual signal and the fault signal is made as small as possible. Furthermore, a sufficient condition on the existence of the mode-independent distributed fault detection filters is derived in the simultaneous presence of incomplete mode transition probabilities, Markovian switching topologies, network-induced delays, and accumulated data packed dropouts. Finally, a stirred-tank reactor system is given to show the effectiveness of the developed theoretical results.

Defense Strategies for Asymmetric Networked Systems with Discrete Components

PubMed Central

Rao, Nageswara S. V.; Ma, Chris Y. T.; Hausken, Kjell; He, Fei; Yau, David K. Y.

2018-01-01

We consider infrastructures consisting of a network of systems, each composed of discrete components. The network provides the vital connectivity between the systems and hence plays a critical, asymmetric role in the infrastructure operations. The individual components of the systems can be attacked by cyber and physical means and can be appropriately reinforced to withstand these attacks. We formulate the problem of ensuring the infrastructure performance as a game between an attacker and a provider, who choose the numbers of the components of the systems and network to attack and reinforce, respectively. The costs and benefits of attacks and reinforcements are characterized using the sum-form, product-form and composite utility functions, each composed of a survival probability term and a component cost term. We present a two-level characterization of the correlations within the infrastructure: (i) the aggregate failure correlation function specifies the infrastructure failure probability given the failure of an individual system or network, and (ii) the survival probabilities of the systems and network satisfy first-order differential conditions that capture the component-level correlations using multiplier functions. We derive Nash equilibrium conditions that provide expressions for individual system survival probabilities and also the expected infrastructure capacity specified by the total number of operational components. We apply these results to derive and analyze defense strategies for distributed cloud computing infrastructures using cyber-physical models. PMID:29751588

Reinforcement learning design-based adaptive tracking control with less learning parameters for nonlinear discrete-time MIMO systems.

PubMed

Liu, Yan-Jun; Tang, Li; Tong, Shaocheng; Chen, C L Philip; Li, Dong-Juan

2015-01-01

Based on the neural network (NN) approximator, an online reinforcement learning algorithm is proposed for a class of affine multiple input and multiple output (MIMO) nonlinear discrete-time systems with unknown functions and disturbances. In the design procedure, two networks are provided where one is an action network to generate an optimal control signal and the other is a critic network to approximate the cost function. An optimal control signal and adaptation laws can be generated based on two NNs. In the previous approaches, the weights of critic and action networks are updated based on the gradient descent rule and the estimations of optimal weight vectors are directly adjusted in the design. Consequently, compared with the existing results, the main contributions of this paper are: 1) only two parameters are needed to be adjusted, and thus the number of the adaptation laws is smaller than the previous results and 2) the updating parameters do not depend on the number of the subsystems for MIMO systems and the tuning rules are replaced by adjusting the norms on optimal weight vectors in both action and critic networks. It is proven that the tracking errors, the adaptation laws, and the control inputs are uniformly bounded using Lyapunov analysis method. The simulation examples are employed to illustrate the effectiveness of the proposed algorithm.

Performance Evaluation Modeling of Network Sensors

NASA Technical Reports Server (NTRS)

Clare, Loren P.; Jennings, Esther H.; Gao, Jay L.

2003-01-01

Substantial benefits are promised by operating many spatially separated sensors collectively. Such systems are envisioned to consist of sensor nodes that are connected by a communications network. A simulation tool is being developed to evaluate the performance of networked sensor systems, incorporating such metrics as target detection probabilities, false alarms rates, and classification confusion probabilities. The tool will be used to determine configuration impacts associated with such aspects as spatial laydown, and mixture of different types of sensors (acoustic, seismic, imaging, magnetic, RF, etc.), and fusion architecture. The QualNet discrete-event simulation environment serves as the underlying basis for model development and execution. This platform is recognized for its capabilities in efficiently simulating networking among mobile entities that communicate via wireless media. We are extending QualNet's communications modeling constructs to capture the sensing aspects of multi-target sensing (analogous to multiple access communications), unimodal multi-sensing (broadcast), and multi-modal sensing (multiple channels and correlated transmissions). Methods are also being developed for modeling the sensor signal sources (transmitters), signal propagation through the media, and sensors (receivers) that are consistent with the discrete event paradigm needed for performance determination of sensor network systems. This work is supported under the Microsensors Technical Area of the Army Research Laboratory (ARL) Advanced Sensors Collaborative Technology Alliance.

Generalized network modeling of capillary-dominated two-phase flow

NASA Astrophysics Data System (ADS)

Raeini, Ali Q.; Bijeljic, Branko; Blunt, Martin J.

2018-02-01

We present a generalized network model for simulating capillary-dominated two-phase flow through porous media at the pore scale. Three-dimensional images of the pore space are discretized using a generalized network—described in a companion paper [A. Q. Raeini, B. Bijeljic, and M. J. Blunt, Phys. Rev. E 96, 013312 (2017), 10.1103/PhysRevE.96.013312]—which comprises pores that are divided into smaller elements called half-throats and subsequently into corners. Half-throats define the connectivity of the network at the coarsest level, connecting each pore to half-throats of its neighboring pores from their narrower ends, while corners define the connectivity of pore crevices. The corners are discretized at different levels for accurate calculation of entry pressures, fluid volumes, and flow conductivities that are obtained using direct simulation of flow on the underlying image. This paper discusses the two-phase flow model that is used to compute the averaged flow properties of the generalized network, including relative permeability and capillary pressure. We validate the model using direct finite-volume two-phase flow simulations on synthetic geometries, and then present a comparison of the model predictions with a conventional pore-network model and experimental measurements of relative permeability in the literature.

Designing a Text Messaging Intervention to Improve Physical Activity Behavior Among Low-Income Latino Patients With Diabetes: A Discrete-Choice Experiment, Los Angeles, 2014-2015.

PubMed

Ramirez, Magaly; Wu, Shinyi; Beale, Elizabeth

2016-12-22

Automated text messaging can deliver self-management education to activate self-care behaviors among people with diabetes. We demonstrated how a discrete-choice experiment was used to determine the features of a text-messaging intervention that are important to urban, low-income Latino patients with diabetes and that could support improvement in their physical activity behavior. In a discrete-choice experiment from December 2014 through August 2015 we conducted a survey to elicit information on patient preferences for 5 features of a text-messaging intervention. We described 2 hypothetical interventions and in 7 pairwise comparisons asked respondents to indicate which they preferred. Respondents (n = 125) were recruited in person from a diabetes management program of a safety-net ambulatory care clinic in Los Angeles; clinicians referred patients to the research assistant after routine clinic visits. Data were analyzed by using conditional logistic regression. We found 2 intervention features that were considered by the survey respondents to be important: 1) the frequency of text messaging and 2) physical activity behavior-change education (the former being more important than the latter). Physical activity goal setting, feedback on physical activity performance, and social support were not significantly important. A discrete-choice experiment is a feasible way to elicit information on patient preferences for a text-messaging intervention designed to support behavior change. However, discrepancies may exist between patients' stated preferences and their actual behavior. Future research should validate and expand our findings.

Recognition of pornographic web pages by classifying texts and images.

PubMed

Hu, Weiming; Wu, Ou; Chen, Zhouyao; Fu, Zhouyu; Maybank, Steve

2007-06-01

With the rapid development of the World Wide Web, people benefit more and more from the sharing of information. However, Web pages with obscene, harmful, or illegal content can be easily accessed. It is important to recognize such unsuitable, offensive, or pornographic Web pages. In this paper, a novel framework for recognizing pornographic Web pages is described. A C4.5 decision tree is used to divide Web pages, according to content representations, into continuous text pages, discrete text pages, and image pages. These three categories of Web pages are handled, respectively, by a continuous text classifier, a discrete text classifier, and an algorithm that fuses the results from the image classifier and the discrete text classifier. In the continuous text classifier, statistical and semantic features are used to recognize pornographic texts. In the discrete text classifier, the naive Bayes rule is used to calculate the probability that a discrete text is pornographic. In the image classifier, the object's contour-based features are extracted to recognize pornographic images. In the text and image fusion algorithm, the Bayes theory is used to combine the recognition results from images and texts. Experimental results demonstrate that the continuous text classifier outperforms the traditional keyword-statistics-based classifier, the contour-based image classifier outperforms the traditional skin-region-based image classifier, the results obtained by our fusion algorithm outperform those by either of the individual classifiers, and our framework can be adapted to different categories of Web pages.

Evidence for discrete landmark use by pigeons during homing.

PubMed

Mora, Cordula V; Ross, Jeremy D; Gorsevski, Peter V; Chowdhury, Budhaditya; Bingman, Verner P

2012-10-01

Considerable efforts have been made to investigate how homing pigeons (Columba livia f. domestica) are able to return to their loft from distant, unfamiliar sites while the mechanisms underlying navigation in familiar territory have received less attention. With the recent advent of global positioning system (GPS) data loggers small enough to be carried by pigeons, the role of visual environmental features in guiding navigation over familiar areas is beginning to be understood, yet, surprisingly, we still know very little about whether homing pigeons can rely on discrete, visual landmarks to guide navigation. To assess a possible role of discrete, visual landmarks in navigation, homing pigeons were first trained to home from a site with four wind turbines as salient landmarks as well as from a control site without any distinctive, discrete landmark features. The GPS-recorded flight paths of the pigeons on the last training release were straighter and more similar among birds from the turbine site compared with those from the control site. The pigeons were then released from both sites following a clock-shift manipulation. Vanishing bearings from the turbine site continued to be homeward oriented as 13 of 14 pigeons returned home. By contrast, at the control site the vanishing bearings were deflected in the expected clock-shift direction and only 5 of 13 pigeons returned home. Taken together, our results offer the first strong evidence that discrete, visual landmarks are one source of spatial information homing pigeons can utilize to navigate when flying over a familiar area.

A recursive method for calculating the total number of spanning trees and its applications in self-similar small-world scale-free network models

NASA Astrophysics Data System (ADS)

Ma, Fei; Su, Jing; Yao, Bing

2018-05-01

The problem of determining and calculating the number of spanning trees of any finite graph (model) is a great challenge, and has been studied in various fields, such as discrete applied mathematics, theoretical computer science, physics, chemistry and the like. In this paper, firstly, thank to lots of real-life systems and artificial networks built by all kinds of functions and combinations among some simpler and smaller elements (components), we discuss some helpful network-operation, including link-operation and merge-operation, to design more realistic and complicated network models. Secondly, we present a method for computing the total number of spanning trees. As an accessible example, we apply this method to space of trees and cycles respectively, and our results suggest that it is indeed a better one for such models. In order to reflect more widely practical applications and potentially theoretical significance, we study the enumerating method in some existing scale-free network models. On the other hand, we set up a class of new models displaying scale-free feature, that is to say, following P(k) k-γ, where γ is the degree exponent. Based on detailed calculation, the degree exponent γ of our deterministic scale-free models satisfies γ > 3. In the rest of our discussions, we not only calculate analytically the solutions of average path length, which indicates our models have small-world property being prevailing in amounts of complex systems, but also derive the number of spanning trees by means of the recursive method described in this paper, which clarifies our method is convenient to research these models.

An optimal modeling of multidimensional wave digital filtering network for free vibration analysis of symmetrically laminated composite FSDT plates

NASA Astrophysics Data System (ADS)

Tseng, Chien-Hsun

2015-02-01

The technique of multidimensional wave digital filtering (MDWDF) that builds on traveling wave formulation of lumped electrical elements, is successfully implemented on the study of dynamic responses of symmetrically laminated composite plate based on the first order shear deformation theory. The philosophy applied for the first time in this laminate mechanics relies on integration of certain principles involving modeling and simulation, circuit theory, and MD digital signal processing to provide a great variety of outstanding features. Especially benefited by the conservation of passivity gives rise to a nonlinear programming problem (NLP) for the issue of numerical stability of a MD discrete system. Adopting the augmented Lagrangian genetic algorithm, an effective optimization technique for rapidly achieving solution spaces of NLP models, numerical stability of the MDWDF network is well received at all time by the satisfaction of the Courant-Friedrichs-Levy stability criterion with the least restriction. In particular, optimum of the NLP has led to the optimality of the network in terms of effectively and accurately predicting the desired fundamental frequency, and thus to give an insight into the robustness of the network by looking at the distribution of system energies. To further explore the application of the optimum network, more numerical examples are engaged in efforts to achieve a qualitative understanding of the behavior of the laminar system. These are carried out by investigating various effects based on different stacking sequences, stiffness and span-to-thickness ratios, mode shapes and boundary conditions. Results are scrupulously validated by cross referencing with early published works, which show that the present method is in excellent agreement with other numerical and analytical methods.

Concurrent evolution of feature extractors and modular artificial neural networks

NASA Astrophysics Data System (ADS)

Hannak, Victor; Savakis, Andreas; Yang, Shanchieh Jay; Anderson, Peter

2009-05-01

This paper presents a new approach for the design of feature-extracting recognition networks that do not require expert knowledge in the application domain. Feature-Extracting Recognition Networks (FERNs) are composed of interconnected functional nodes (feurons), which serve as feature extractors, and are followed by a subnetwork of traditional neural nodes (neurons) that act as classifiers. A concurrent evolutionary process (CEP) is used to search the space of feature extractors and neural networks in order to obtain an optimal recognition network that simultaneously performs feature extraction and recognition. By constraining the hill-climbing search functionality of the CEP on specific parts of the solution space, i.e., individually limiting the evolution of feature extractors and neural networks, it was demonstrated that concurrent evolution is a necessary component of the system. Application of this approach to a handwritten digit recognition task illustrates that the proposed methodology is capable of producing recognition networks that perform in-line with other methods without the need for expert knowledge in image processing.

Questa Baseline and Premining Ground-Water Quality Investigation 18. Characterization of Brittle Structures in the Questa Caldera and Their Potential Influence on Bedrock Ground-Water Flow, Red River Valley, New Mexico

USGS Publications Warehouse

Caine, Jonathan S.

2006-01-01

This report presents a field-based characterization of fractured and faulted crystalline bedrock in the southern portion of the Questa caldera and its margin. The focus is (1) the identification and description of brittle geological structures and (2) speculation on the potential effects and controls that these structures might have on the potential fluxes of paleo to present-day ground water in relation to natural or mining-related metal and acid loads to surface and ground water. The entire study area is pervasively jointed with a few distinctive patterns such as orthogonal, oblique orthogonal, and conjugate joint sets. Joint intensity, the number of joints measured per unit line length, is high to extreme. Three types of fault zones are present that include partially silicified, low- and high-angle faults with well-developed damage zones and clay-rich cores and high-angle, unsilicified open faults. Conceptually, the joint networks can be thought of as providing the background porosity and permeability structure of the bedrock aquifer system. This background is cut by discrete entities such as the faults with clay-rich cores and open faults that may act as important hydrologic heterogeneities. The southern caldera margin runs parallel to the course of the Red River Valley, whose incision has left an extreme topographic gradient at high angles to the river. Many of the faults and fault intersections run parallel to this assumed hydraulic gradient; thus, these structures have great potential to provide paleo and present-day, discrete and anisotropic pathways for solute transport within the otherwise relatively low porosity and permeability bedrock background aquifer system. Although brittle fracture networks and faults are pervasive and complex, simple Darcy calculations are used to estimate the hydraulic conductivity and potential ground-water discharges of the bedrock aquifer, caldera margin, and other faults in order to gain insight into the potential contributions of these features to the ground-water and surface-water flow systems. These calculations show that, because all of these features are found along the Red River in the Cabin Springs-Columbine Park-Goat Hill fan area, their combined effect increases the probability that the bedrock aquifer ground-water flow system provides discharge to the Red River along this reach.

A description of discrete internal representation schemes for visual pattern discrimination.

PubMed

Foster, D H

1980-01-01

A general description of a class of schemes for pattern vision is outlined in which the visual system is assumed to form a discrete internal representation of the stimulus. These representations are discrete in that they are considered to comprise finite combinations of "components" which are selected from a fixed and finite repertoire, and which designate certain simple pattern properties or features. In the proposed description it is supposed that the construction of an internal representation is a probabilistic process. A relationship is then formulated associating the probability density functions governing this construction and performance in visually discriminating patterns when differences in pattern shape are small. Some questions related to the application of this relationship to the experimental investigation of discrete internal representations are briefly discussed.

Largenet2: an object-oriented programming library for simulating large adaptive networks.

PubMed

Zschaler, Gerd; Gross, Thilo

2013-01-15

The largenet2 C++ library provides an infrastructure for the simulation of large dynamic and adaptive networks with discrete node and link states. The library is released as free software. It is available at http://biond.github.com/largenet2. Largenet2 is licensed under the Creative Commons Attribution-NonCommercial 3.0 Unported License. gerd@biond.org

Adaptive enhanced sampling by force-biasing using neural networks

NASA Astrophysics Data System (ADS)

Guo, Ashley Z.; Sevgen, Emre; Sidky, Hythem; Whitmer, Jonathan K.; Hubbell, Jeffrey A.; de Pablo, Juan J.

2018-04-01

A machine learning assisted method is presented for molecular simulation of systems with rugged free energy landscapes. The method is general and can be combined with other advanced sampling techniques. In the particular implementation proposed here, it is illustrated in the context of an adaptive biasing force approach where, rather than relying on discrete force estimates, one can resort to a self-regularizing artificial neural network to generate continuous, estimated generalized forces. By doing so, the proposed approach addresses several shortcomings common to adaptive biasing force and other algorithms. Specifically, the neural network enables (1) smooth estimates of generalized forces in sparsely sampled regions, (2) force estimates in previously unexplored regions, and (3) continuous force estimates with which to bias the simulation, as opposed to biases generated at specific points of a discrete grid. The usefulness of the method is illustrated with three different examples, chosen to highlight the wide range of applicability of the underlying concepts. In all three cases, the new method is found to enhance considerably the underlying traditional adaptive biasing force approach. The method is also found to provide improvements over previous implementations of neural network assisted algorithms.

Incorporating social impact on new product adoption in choice modeing: A case study in green vehicles

DOE Office of Scientific and Technical Information (OSTI.GOV)

He, Lin; Wang, Mingxian; Chen, Wei

While discrete choice analysis is prevalent in capturing consumer preferences and describing their choice behaviors in product design, the traditional choice modeling approach assumes that each individual makes independent decisions, without considering the social impact. However, empirical studies show that choice is social - influenced by many factors beyond engineering performance of a product and consumer attributes. To alleviate this limitation, we propose a new choice modeling framework to capture the dynamic influence from social networks on consumer adoption of new products. By introducing social influence attributes into a choice utility function, social network simulation is integrated with the traditionalmore » discrete choice analysis in a three-stage process. Our study shows the need for considering social impact in forecasting new product adoption. Using hybrid electric vehicles as an example, our work illustrates the procedure of social network construction, social influence evaluation, and choice model estimation based on data from the National Household Travel Survey. Our study also demonstrates several interesting findings on the dynamic nature of new technology adoption and how social networks may influence hybrid electric vehicle adoption. (C) 2014 Elsevier Ltd. All rights reserved« less

A homogenization-based quasi-discrete method for the fracture of heterogeneous materials

NASA Astrophysics Data System (ADS)

Berke, P. Z.; Peerlings, R. H. J.; Massart, T. J.; Geers, M. G. D.

2014-05-01

The understanding and the prediction of the failure behaviour of materials with pronounced microstructural effects is of crucial importance. This paper presents a novel computational methodology for the handling of fracture on the basis of the microscale behaviour. The basic principles presented here allow the incorporation of an adaptive discretization scheme of the structure as a function of the evolution of strain localization in the underlying microstructure. The proposed quasi-discrete methodology bridges two scales: the scale of the material microstructure, modelled with a continuum type description; and the structural scale, where a discrete description of the material is adopted. The damaging material at the structural scale is divided into unit volumes, called cells, which are represented as a discrete network of points. The scale transition is inspired by computational homogenization techniques; however it does not rely on classical averaging theorems. The structural discrete equilibrium problem is formulated in terms of the underlying fine scale computations. Particular boundary conditions are developed on the scale of the material microstructure to address damage localization problems. The performance of this quasi-discrete method with the enhanced boundary conditions is assessed using different computational test cases. The predictions of the quasi-discrete scheme agree well with reference solutions obtained through direct numerical simulations, both in terms of crack patterns and load versus displacement responses.

A Combined Remote Sensing-Numerical Modelling Approach to the Stability Analysis of Delabole Slate Quarry, Cornwall, UK

NASA Astrophysics Data System (ADS)

Havaej, Mohsen; Coggan, John; Stead, Doug; Elmo, Davide

2016-04-01

Rock slope geometry and discontinuity properties are among the most important factors in realistic rock slope analysis yet they are often oversimplified in numerical simulations. This is primarily due to the difficulties in obtaining accurate structural and geometrical data as well as the stochastic representation of discontinuities. Recent improvements in both digital data acquisition and incorporation of discrete fracture network data into numerical modelling software have provided better tools to capture rock mass characteristics, slope geometries and digital terrain models allowing more effective modelling of rock slopes. Advantages of using improved data acquisition technology include safer and faster data collection, greater areal coverage, and accurate data geo-referencing far exceed limitations due to orientation bias and occlusion. A key benefit of a detailed point cloud dataset is the ability to measure and evaluate discontinuity characteristics such as orientation, spacing/intensity and persistence. This data can be used to develop a discrete fracture network which can be imported into the numerical simulations to study the influence of the stochastic nature of the discontinuities on the failure mechanism. We demonstrate the application of digital terrestrial photogrammetry in discontinuity characterization and distinct element simulations within a slate quarry. An accurately geo-referenced photogrammetry model is used to derive the slope geometry and to characterize geological structures. We first show how a discontinuity dataset, obtained from a photogrammetry model can be used to characterize discontinuities and to develop discrete fracture networks. A deterministic three-dimensional distinct element model is then used to investigate the effect of some key input parameters (friction angle, spacing and persistence) on the stability of the quarry slope model. Finally, adopting a stochastic approach, discrete fracture networks are used as input for 3D distinct element simulations to better understand the stochastic nature of the geological structure and its effect on the quarry slope failure mechanism. The numerical modelling results highlight the influence of discontinuity characteristics and kinematics on the slope failure mechanism and the variability in the size and shape of the failed blocks.

An Empirical Development of Parallelization Guidelines for Time-Driven Simulation

DTIC Science & Technology

1989-12-01

wives, who though not Cub fans, put on a good show during our trip, to waich some games . I would also like to recognize the help of my professors at...program parallelization. in this research effort a Ballistic Missile Defense (BMD) time driven simulation program, developed by DESE Research and...continuously, or continuously with discrete changes superimposed. The distinguishing feature of these simulations is the interaction between discretely

Rhythmic arm movements are less affected than discrete ones after a stroke.

PubMed

Leconte, Patricia; Orban de Xivry, Jean-Jacques; Stoquart, Gaëtan; Lejeune, Thierry; Ronsse, Renaud

2016-06-01

Recent reports indicate that rhythmic and discrete upper-limb movements are two different motor primitives which recruit, at least partially, distinct neural circuitries. In particular, rhythmic movements recruit a smaller cortical network than discrete movements. The goal of this paper is to compare the levels of disability in performing rhythmic and discrete movements after a stroke. More precisely, we tested the hypothesis that rhythmic movements should be less affected than discrete ones, because they recruit neural circuitries that are less likely to be damaged by the stroke. Eleven stroke patients and eleven age-matched control subjects performed discrete and rhythmic movements using an end-effector robot (REAplan). The rhythmic movement condition was performed with and without visual targets to further decrease cortical recruitment. Movement kinematics was analyzed through specific metrics, capturing the degree of smoothness and harmonicity. We reported three main observations: (1) the movement smoothness of the paretic arm was more severely degraded for discrete movements than rhythmic movements; (2) most of the patients performed rhythmic movements with a lower harmonicity than controls; and (3) visually guided rhythmic movements were more altered than non-visually guided rhythmic movements. These results suggest a hierarchy in the levels of impairment: Discrete movements are more affected than rhythmic ones, which are more affected if they are visually guided. These results are a new illustration that discrete and rhythmic movements are two fundamental primitives in upper-limb movements. Moreover, this hierarchy of impairment opens new post-stroke rehabilitation perspectives.

Using Movement and Intentions to Understand Human Activity

ERIC Educational Resources Information Center

Zacks, Jeffrey M.; Kumar, Shawn; Abrams, Richard A.; Mehta, Ritesh

2009-01-01

During perception, people segment continuous activity into discrete events. They do so in part by monitoring changes in features of an ongoing activity. Characterizing these features is important for theories of event perception and may be helpful for designing information systems. The three experiments reported here asked whether the body…

Benign Occipital Epilepsies of Childhood: Clinical Features and Genetics

ERIC Educational Resources Information Center

Taylor, Isabella; Berkovic, Samuel F.; Kivity, Sara; Scheffer, Ingrid E.

2008-01-01

The early and late benign occipital epilepsies of childhood (BOEC) are described as two discrete electro-clinical syndromes, eponymously known as Panayiotopoulos and Gastaut syndromes. Our aim was to explore the clinical features, classification and clinical genetics of these syndromes using twin and multiplex family studies to determine whether…

GENET note no. 1

NASA Technical Reports Server (NTRS)

Yeh, J. W.

1971-01-01

The general features of the GENET system for simulating networks are described. A set of features is presented which are desirable for network simulations and which are expected to be achieved by this system. Among these features are: (1) two level network modeling; and (2) problem oriented operations. Several typical network systems are modeled in GENET framework to illustrate various of the features and to show its applicability.

Discreteness-induced concentration inversion in mesoscopic chemical systems.

PubMed

Ramaswamy, Rajesh; González-Segredo, Nélido; Sbalzarini, Ivo F; Grima, Ramon

2012-04-10

Molecular discreteness is apparent in small-volume chemical systems, such as biological cells, leading to stochastic kinetics. Here we present a theoretical framework to understand the effects of discreteness on the steady state of a monostable chemical reaction network. We consider independent realizations of the same chemical system in compartments of different volumes. Rate equations ignore molecular discreteness and predict the same average steady-state concentrations in all compartments. However, our theory predicts that the average steady state of the system varies with volume: if a species is more abundant than another for large volumes, then the reverse occurs for volumes below a critical value, leading to a concentration inversion effect. The addition of extrinsic noise increases the size of the critical volume. We theoretically predict the critical volumes and verify, by exact stochastic simulations, that rate equations are qualitatively incorrect in sub-critical volumes.

Global robust stability of bidirectional associative memory neural networks with multiple time delays.

PubMed

Senan, Sibel; Arik, Sabri

2007-10-01

This correspondence presents a sufficient condition for the existence, uniqueness, and global robust asymptotic stability of the equilibrium point for bidirectional associative memory neural networks with discrete time delays. The results impose constraint conditions on the network parameters of the neural system independently of the delay parameter, and they are applicable to all bounded continuous nonmonotonic neuron activation functions. Some numerical examples are given to compare our results with the previous robust stability results derived in the literature.

International Neural Network Society Annual Meeting (1994) Held in San Diego, California on 5-9 June 1994. Volume 1

DTIC Science & Technology

1994-06-09

Ethics and the Soul 1-221 P. Werbos A Net Program for Natural Language Comprehension 1-863 J. Weiss Applications Oral ANN Design of Image Processing...Controlling Nonlinear Dynamic Systems Using Neuro-Fuzzy Networks 1-787 E. Teixera, G. Laforga, H. Azevedo Neural Fuzzy Logics as a Tool for Design Ecological ...Discrete Neural Network 11-466 Z. Cheng-fu Representation of Number A Theory of Mathematical Modeling 11-479 J. Cristofano An Ecological Approach to

Random Boolean networks for autoassociative memory: Optimization and sequential learning

NASA Astrophysics Data System (ADS)

Sherrington, D.; Wong, K. Y. M.

Conventional neural networks are based on synaptic storage of information, even when the neural states are discrete and bounded. In general, the set of potential local operations is much greater. Here we discuss some aspects of the properties of networks of binary neurons with more general Boolean functions controlling the local dynamics. Two specific aspects are emphasised; (i) optimization in the presence of noise and (ii) a simple model for short-term memory exhibiting primacy and recency in the recall of sequentially taught patterns.

Impact of hyperbolicity on chimera states in ensembles of nonlocally coupled chaotic oscillators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Semenova, N.; Anishchenko, V.; Zakharova, A.

2016-06-08

In this work we analyse nonlocally coupled networks of identical chaotic oscillators. We study both time-discrete and time-continuous systems (Henon map, Lozi map, Lorenz system). We hypothesize that chimera states, in which spatial domains of coherent (synchronous) and incoherent (desynchronized) dynamics coexist, can be obtained only in networks of chaotic non-hyperbolic systems and cannot be found in networks of hyperbolic systems. This hypothesis is supported by numerical simulations for hyperbolic and non-hyperbolic cases.

A new estimation of equivalent matrix block sizes in fractured media with two-phase flow applications in dual porosity models

NASA Astrophysics Data System (ADS)

Jerbi, Chahir; Fourno, André; Noetinger, Benoit; Delay, Frederick

2017-05-01

Single and multiphase flows in fractured porous media at the scale of natural reservoirs are often handled by resorting to homogenized models that avoid the heavy computations associated with a complete discretization of both fractures and matrix blocks. For example, the two overlapping continua (fractures and matrix) of a dual porosity system are coupled by way of fluid flux exchanges that deeply condition flow at the large scale. This characteristic is a key to realistic flow simulations, especially for multiphase flow as capillary forces and contrasts of fluid mobility compete in the extraction of a fluid from a capacitive matrix then conveyed through the fractures. The exchange rate between fractures and matrix is conditioned by the so-called mean matrix block size which can be viewed as the size of a single matrix block neighboring a single fracture within a mesh of a dual porosity model. We propose a new evaluation of this matrix block size based on the analysis of discrete fracture networks. The fundaments rely upon establishing at the scale of a fractured block the equivalence between the actual fracture network and a Warren and Root network only made of three regularly spaced fracture families parallel to the facets of the fractured block. The resulting matrix block sizes are then compared via geometrical considerations and two-phase flow simulations to the few other available methods. It is shown that the new method is stable in the sense it provides accurate sizes irrespective of the type of fracture network investigated. The method also results in two-phase flow simulations from dual porosity models very close to that from references calculated in finely discretized networks. Finally, calculations of matrix block sizes by this new technique reveal very rapid, which opens the way to cumbersome applications such as preconditioning a dual porosity approach applied to regional fractured reservoirs.

Hybrid ICA-Bayesian network approach reveals distinct effective connectivity differences in schizophrenia.

PubMed

Kim, D; Burge, J; Lane, T; Pearlson, G D; Kiehl, K A; Calhoun, V D

2008-10-01

We utilized a discrete dynamic Bayesian network (dDBN) approach (Burge, J., Lane, T., Link, H., Qiu, S., Clark, V.P., 2007. Discrete dynamic Bayesian network analysis of fMRI data. Hum Brain Mapp.) to determine differences in brain regions between patients with schizophrenia and healthy controls on a measure of effective connectivity, termed the approximate conditional likelihood score (ACL) (Burge, J., Lane, T., 2005. Learning Class-Discriminative Dynamic Bayesian Networks. Proceedings of the International Conference on Machine Learning, Bonn, Germany, pp. 97-104.). The ACL score represents a class-discriminative measure of effective connectivity by measuring the relative likelihood of the correlation between brain regions in one group versus another. The algorithm is capable of finding non-linear relationships between brain regions because it uses discrete rather than continuous values and attempts to model temporal relationships with a first-order Markov and stationary assumption constraint (Papoulis, A., 1991. Probability, random variables, and stochastic processes. McGraw-Hill, New York.). Since Bayesian networks are overly sensitive to noisy data, we introduced an independent component analysis (ICA) filtering approach that attempted to reduce the noise found in fMRI data by unmixing the raw datasets into a set of independent spatial component maps. Components that represented noise were removed and the remaining components reconstructed into the dimensions of the original fMRI datasets. We applied the dDBN algorithm to a group of 35 patients with schizophrenia and 35 matched healthy controls using an ICA filtered and unfiltered approach. We determined that filtering the data significantly improved the magnitude of the ACL score. Patients showed the greatest ACL scores in several regions, most markedly the cerebellar vermis and hemispheres. Our findings suggest that schizophrenia patients exhibit weaker connectivity than healthy controls in multiple regions, including bilateral temporal, frontal, and cerebellar regions during an auditory paradigm.

Modelling road accident blackspots data with the discrete generalized Pareto distribution.

PubMed

Prieto, Faustino; Gómez-Déniz, Emilio; Sarabia, José María

2014-10-01

This study shows how road traffic networks events, in particular road accidents on blackspots, can be modelled with simple probabilistic distributions. We considered the number of crashes and the number of fatalities on Spanish blackspots in the period 2003-2007, from Spanish General Directorate of Traffic (DGT). We modelled those datasets, respectively, with the discrete generalized Pareto distribution (a discrete parametric model with three parameters) and with the discrete Lomax distribution (a discrete parametric model with two parameters, and particular case of the previous model). For that, we analyzed the basic properties of both parametric models: cumulative distribution, survival, probability mass, quantile and hazard functions, genesis and rth-order moments; applied two estimation methods of their parameters: the μ and (μ+1) frequency method and the maximum likelihood method; used two goodness-of-fit tests: Chi-square test and discrete Kolmogorov-Smirnov test based on bootstrap resampling; and compared them with the classical negative binomial distribution in terms of absolute probabilities and in models including covariates. We found that those probabilistic models can be useful to describe the road accident blackspots datasets analyzed. Copyright © 2014 Elsevier Ltd. All rights reserved.

Nakhla: a Martian Meteorite with Indigenous Organic Carbonaceous Features

NASA Technical Reports Server (NTRS)

McKay, D. S.; Gibson, E. K.; Thomas-Keprta, K. L.; Clemett, S. J.; Le, L.; Rahman, Z.; Wentworth, S. J.

2011-01-01

The Nakhla meteorite possesses discrete, well defined, structurally coherent morphologies of carbonaceous phases present within iddingsite alteration zones. Based upon both isotopic measurements and analysis of organic phases the presence of pre-terrestrial organics is now recognized. Within the microcrystalline layers of Nakhla s iddingsite, discrete clusters of salt crystals are present. These salts are predominantly halite (NaCl) with minor MgCl2 crystals. Some CaSO4, likely gypsum, appears to be partially intergrown with some of the halite. EDX mapping shows discrete C-rich features are interspersed among these crystals. A hollow semi-spherical bowl structure ( 3 m ) has been identified and analyzed after using a focused ion beam (FIB) to cut a transverse TEM thin section of the feature and the underlying iddingsite. TEM/EDX analysis reveals that the feature is primarily carbonaceous containing C with lesser amounts of Si, S, Ca, Cl, F, Na, and minor Mn and Fe; additionally a small peak consistent with N, which has been previously seen in Nakhla carbonaceous matter, is also present. Selected area electron diffraction (SAED) shows that this C-rich material is amorphous (lacking any long-range crystallographic order) and is not graphite or carbonate. Micro-Raman spectra acquired from the same surface from which the FIB section was extracted demonstrate a typical kerogen-like D and G band structure with a weak absorption peak at 1350 and a stronger peak at 1600/cm. The C-rich feature is intimately associated with both the surrounding halite and underlying iddingsite matrix. Both iddingsite and salts are interpreted as having formed as evaporate assemblages from progressive evaporation of water bodies on Mars. This assemblage, sans the carbonaceous moieties, closely resembles iddingsite alteration features previously described which were interpreted as indigenous Martian assemblages. These distinctive macromolecular carbonaceous structures in Nakhla may represent one of the sources of the high molecular weight organic material previously identified in Nakhla. While we do not speculate on the origin of these unique carbonaceous structures, we note that the significance of such observations is that it may allow us to construct a C-cycle for Mars based on the C chemistry of the Martian meteorites with obvious implications for astrobiology and the prebiotic evolution of Mars. In any case, our observations strongly suggest that organic C exists as micrometersize, discrete structures on Mars.

Two papers on feed-forward networks

NASA Technical Reports Server (NTRS)

Buntine, Wray L.; Weigend, Andreas S.

1991-01-01

Connectionist feed-forward networks, trained with back-propagation, can be used both for nonlinear regression and for (discrete one-of-C) classification, depending on the form of training. This report contains two papers on feed-forward networks. The papers can be read independently. They are intended for the theoretically-aware practitioner or algorithm-designer; however, they also contain a review and comparison of several learning theories so they provide a perspective for the theoretician. The first paper works through Bayesian methods to complement back-propagation in the training of feed-forward networks. The second paper addresses a problem raised by the first: how to efficiently calculate second derivatives on feed-forward networks.

Discrete-continuous variable structural synthesis using dual methods

NASA Technical Reports Server (NTRS)

Schmit, L. A.; Fleury, C.

1980-01-01

Approximation concepts and dual methods are extended to solve structural synthesis problems involving a mix of discrete and continuous sizing type of design variables. Pure discrete and pure continuous variable problems can be handled as special cases. The basic mathematical programming statement of the structural synthesis problem is converted into a sequence of explicit approximate primal problems of separable form. These problems are solved by constructing continuous explicit dual functions, which are maximized subject to simple nonnegativity constraints on the dual variables. A newly devised gradient projection type of algorithm called DUAL 1, which includes special features for handling dual function gradient discontinuities that arise from the discrete primal variables, is used to find the solution of each dual problem. Computational implementation is accomplished by incorporating the DUAL 1 algorithm into the ACCESS 3 program as a new optimizer option. The power of the method set forth is demonstrated by presenting numerical results for several example problems, including a pure discrete variable treatment of a metallic swept wing and a mixed discrete-continuous variable solution for a thin delta wing with fiber composite skins.

Estimation of the hydraulic conductivity of a two-dimensional fracture network using effective medium theory and power-law averaging

NASA Astrophysics Data System (ADS)

Zimmerman, R. W.; Leung, C. T.

2009-12-01

Most oil and gas reservoirs, as well as most potential sites for nuclear waste disposal, are naturally fractured. In these sites, the network of fractures will provide the main path for fluid to flow through the rock mass. In many cases, the fracture density is so high as to make it impractical to model it with a discrete fracture network (DFN) approach. For such rock masses, it would be useful to have recourse to analytical, or semi-analytical, methods to estimate the macroscopic hydraulic conductivity of the fracture network. We have investigated single-phase fluid flow through generated stochastically two-dimensional fracture networks. The centers and orientations of the fractures are uniformly distributed, whereas their lengths follow a lognormal distribution. The aperture of each fracture is correlated with its length, either through direct proportionality, or through a nonlinear relationship. The discrete fracture network flow and transport simulator NAPSAC, developed by Serco (Didcot, UK), is used to establish the “true” macroscopic hydraulic conductivity of the network. We then attempt to match this value by starting with the individual fracture conductances, and using various upscaling methods. Kirkpatrick’s effective medium approximation, which works well for pore networks on a core scale, generally underestimates the conductivity of the fracture networks. We attribute this to the fact that the conductances of individual fracture segments (between adjacent intersections with other fractures) are correlated with each other, whereas Kirkpatrick’s approximation assumes no correlation. The power-law averaging approach proposed by Desbarats for porous media is able to match the numerical value, using power-law exponents that generally lie between 0 (geometric mean) and 1 (harmonic mean). The appropriate exponent can be correlated with statistical parameters that characterize the fracture density.

Multiscale fracture network characterization and impact on flow: A case study on the Latemar carbonate platform

NASA Astrophysics Data System (ADS)

Hardebol, N. J.; Maier, C.; Nick, H.; Geiger, S.; Bertotti, G.; Boro, H.

2015-12-01

A fracture network arrangement is quantified across an isolated carbonate platform from outcrop and aerial imagery to address its impact on fluid flow. The network is described in terms of fracture density, orientation, and length distribution parameters. Of particular interest is the role of fracture cross connections and abutments on the effective permeability. Hence, the flow simulations explicitly account for network topology by adopting Discrete-Fracture-and-Matrix description. The interior of the Latemar carbonate platform (Dolomites, Italy) is taken as outcrop analogue for subsurface reservoirs of isolated carbonate build-ups that exhibit a fracture-dominated permeability. New is our dual strategy to describe the fracture network both as deterministic- and stochastic-based inputs for flow simulations. The fracture geometries are captured explicitly and form a multiscale data set by integration of interpretations from outcrops, airborne imagery, and lidar. The deterministic network descriptions form the basis for descriptive rules that are diagnostic of the complex natural fracture arrangement. The fracture networks exhibit a variable degree of multitier hierarchies with smaller-sized fractures abutting against larger fractures under both right and oblique angles. The influence of network topology on connectivity is quantified using Discrete-Fracture-Single phase fluid flow simulations. The simulation results show that the effective permeability for the fracture and matrix ensemble can be 50 to 400 times higher than the matrix permeability of 1.0 · 10-14 m2. The permeability enhancement is strongly controlled by the connectivity of the fracture network. Therefore, the degree of intersecting and abutting fractures should be captured from outcrops with accuracy to be of value as analogue.

Morphological self-organizing feature map neural network with applications to automatic target recognition

NASA Astrophysics Data System (ADS)

Zhang, Shijun; Jing, Zhongliang; Li, Jianxun

2005-01-01

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

Asynchronous State Estimation for Discrete-Time Switched Complex Networks With Communication Constraints.

PubMed

Zhang, Dan; Wang, Qing-Guo; Srinivasan, Dipti; Li, Hongyi; Yu, Li

2018-05-01

This paper is concerned with the asynchronous state estimation for a class of discrete-time switched complex networks with communication constraints. An asynchronous estimator is designed to overcome the difficulty that each node cannot access to the topology/coupling information. Also, the event-based communication, signal quantization, and the random packet dropout problems are studied due to the limited communication resource. With the help of switched system theory and by resorting to some stochastic system analysis method, a sufficient condition is proposed to guarantee the exponential stability of estimation error system in the mean-square sense and a prescribed performance level is also ensured. The characterization of the desired estimator gains is derived in terms of the solution to a convex optimization problem. Finally, the effectiveness of the proposed design approach is demonstrated by a simulation example.

Effective degree Markov-chain approach for discrete-time epidemic processes on uncorrelated networks.

PubMed

Cai, Chao-Ran; Wu, Zhi-Xi; Guan, Jian-Yue

2014-11-01

Recently, Gómez et al. proposed a microscopic Markov-chain approach (MMCA) [S. Gómez, J. Gómez-Gardeñes, Y. Moreno, and A. Arenas, Phys. Rev. E 84, 036105 (2011)PLEEE81539-375510.1103/PhysRevE.84.036105] to the discrete-time susceptible-infected-susceptible (SIS) epidemic process and found that the epidemic prevalence obtained by this approach agrees well with that by simulations. However, we found that the approach cannot be straightforwardly extended to a susceptible-infected-recovered (SIR) epidemic process (due to its irreversible property), and the epidemic prevalences obtained by MMCA and Monte Carlo simulations do not match well when the infection probability is just slightly above the epidemic threshold. In this contribution we extend the effective degree Markov-chain approach, proposed for analyzing continuous-time epidemic processes [J. Lindquist, J. Ma, P. Driessche, and F. Willeboordse, J. Math. Biol. 62, 143 (2011)JMBLAJ0303-681210.1007/s00285-010-0331-2], to address discrete-time binary-state (SIS) or three-state (SIR) epidemic processes on uncorrelated complex networks. It is shown that the final epidemic size as well as the time series of infected individuals obtained from this approach agree very well with those by Monte Carlo simulations. Our results are robust to the change of different parameters, including the total population size, the infection probability, the recovery probability, the average degree, and the degree distribution of the underlying networks.

Neural-network-based state feedback control of a nonlinear discrete-time system in nonstrict feedback form.

PubMed

Jagannathan, Sarangapani; He, Pingan

2008-12-01

In this paper, a suite of adaptive neural network (NN) controllers is designed to deliver a desired tracking performance for the control of an unknown, second-order, nonlinear discrete-time system expressed in nonstrict feedback form. In the first approach, two feedforward NNs are employed in the controller with tracking error as the feedback variable whereas in the adaptive critic NN architecture, three feedforward NNs are used. In the adaptive critic architecture, two action NNs produce virtual and actual control inputs, respectively, whereas the third critic NN approximates certain strategic utility function and its output is employed for tuning action NN weights in order to attain the near-optimal control action. Both the NN control methods present a well-defined controller design and the noncausal problem in discrete-time backstepping design is avoided via NN approximation. A comparison between the controller methodologies is highlighted. The stability analysis of the closed-loop control schemes is demonstrated. The NN controller schemes do not require an offline learning phase and the NN weights can be initialized at zero or random. Results show that the performance of the proposed controller schemes is highly satisfactory while meeting the closed-loop stability.

Discrete event simulation for exploring strategies: an urban water management case.

PubMed

Huang, Dong-Bin; Scholz, Roland W; Gujer, Willi; Chitwood, Derek E; Loukopoulos, Peter; Schertenleib, Roland; Siegrist, Hansruedi

2007-02-01

This paper presents a model structure aimed at offering an overview of the various elements of a strategy and exploring their multidimensional effects through time in an efficient way. It treats a strategy as a set of discrete events planned to achieve a certain strategic goal and develops a new form of causal networks as an interfacing component between decision makers and environment models, e.g., life cycle inventory and material flow models. The causal network receives a strategic plan as input in a discrete manner and then outputs the updated parameter sets to the subsequent environmental models. Accordingly, the potential dynamic evolution of environmental systems caused by various strategies can be stepwise simulated. It enables a way to incorporate discontinuous change in models for environmental strategy analysis, and enhances the interpretability and extendibility of a complex model by its cellular constructs. It is exemplified using an urban water management case in Kunming, a major city in Southwest China. By utilizing the presented method, the case study modeled the cross-scale interdependencies of the urban drainage system and regional water balance systems, and evaluated the effectiveness of various strategies for improving the situation of Dianchi Lake.

Hydro-mechanical model for wetting/drying and fracture development in geomaterials

DOE PAGES

Asahina, D.; Houseworth, J. E.; Birkholzer, J. T.; ...

2013-12-28

This study presents a modeling approach for studying hydro-mechanical coupled processes, including fracture development, within geological formations. This is accomplished through the novel linking of two codes: TOUGH2, which is a widely used simulator of subsurface multiphase flow based on the finite volume method; and an implementation of the Rigid-Body-Spring Network (RBSN) method, which provides a discrete (lattice) representation of material elasticity and fracture development. The modeling approach is facilitated by a Voronoi-based discretization technique, capable of representing discrete fracture networks. The TOUGH–RBSN simulator is intended to predict fracture evolution, as well as mass transport through permeable media, under dynamicallymore » changing hydrologic and mechanical conditions. Numerical results are compared with those of two independent studies involving hydro-mechanical coupling: (1) numerical modeling of swelling stress development in bentonite; and (2) experimental study of desiccation cracking in a mining waste. The comparisons show good agreement with respect to moisture content, stress development with changes in pore pressure, and time to crack initiation. Finally, the observed relationship between material thickness and crack patterns (e.g., mean spacing of cracks) is captured by the proposed modeling approach.« less

Interplay of node connectivity and epidemic rates in the dynamics of epidemic networks

DOE PAGES

Kostova, Tanya

2010-07-09

We present and analyze a discrete-time susceptible-infected epidemic network model which represents each host as a separate entity and allows heterogeneous hosts and contacts. We establish a necessary and sufficient condition for global stability of the disease-free equilibrium of the system (defined as epidemic controllability) which defines the epidemic reproduction number of the network. When this condition is not fulfilled, we show that the system has a unique, locally stable equilibrium. As a result, we further derive sufficient conditions for epidemic controllability in terms of the epidemic rates and the network topology.

Implementing the sine transform of fermionic modes as a tensor network

NASA Astrophysics Data System (ADS)

Epple, Hannes; Fries, Pascal; Hinrichsen, Haye

2017-09-01

Based on the algebraic theory of signal processing, we recursively decompose the discrete sine transform of the first kind (DST-I) into small orthogonal block operations. Using a diagrammatic language, we then second-quantize this decomposition to construct a tensor network implementing the DST-I for fermionic modes on a lattice. The complexity of the resulting network is shown to scale as 5/4 n logn (not considering swap gates), where n is the number of lattice sites. Our method provides a systematic approach of generalizing Ferris' spectral tensor network for nontrivial boundary conditions.

Mathematical Model Taking into Account Nonlocal Effects of Plasmonic Structures on the Basis of the Discrete Source Method

NASA Astrophysics Data System (ADS)

Eremin, Yu. A.; Sveshnikov, A. G.

2018-04-01

The discrete source method is used to develop and implement a mathematical model for solving the problem of scattering electromagnetic waves by a three-dimensional plasmonic scatterer with nonlocal effects taken into account. Numerical results are presented whereby the features of the scattering properties of plasmonic particles with allowance for nonlocal effects are demonstrated depending on the direction and polarization of the incident wave.

High-resolution maps of Jupiter at five microns.

NASA Technical Reports Server (NTRS)

Keay, C. S. L.; Low, F. J.; Rieke, G. H.; Minton, R. B.

1973-01-01

The distribution of 5-micron radiation, emitted from a large number of discrete sources from Jupiter, was observed during the 1972 apparition. These sources are less bright than those observed by Westphal (1969). At least 50 discrete sources having brightness temperatures exceeding 227 K were revealed which were mainly located within three narrow-latitude bands. Strong correlation exists between the 5-micron brightness temperatures of Jovian features and their colors as recorded photographically.

Categorical Structure among Shared Features in Networks of Early-Learned Nouns

ERIC Educational Resources Information Center

Hills, Thomas T.; Maouene, Mounir; Maouene, Josita; Sheya, Adam; Smith, Linda

2009-01-01

The shared features that characterize the noun categories that young children learn first are a formative basis of the human category system. To investigate the potential categorical information contained in the features of early-learned nouns, we examine the graph-theoretic properties of noun-feature networks. The networks are built from the…

Documentation of a Conduit Flow Process (CFP) for MODFLOW-2005

USGS Publications Warehouse

Shoemaker, W. Barclay; Kuniansky, Eve L.; Birk, Steffen; Bauer, Sebastian; Swain, Eric D.

2007-01-01

This report documents the Conduit Flow Process (CFP) for the modular finite-difference ground-water flow model, MODFLOW-2005. The CFP has the ability to simulate turbulent ground-water flow conditions by: (1) coupling the traditional ground-water flow equation with formulations for a discrete network of cylindrical pipes (Mode 1), (2) inserting a high-conductivity flow layer that can switch between laminar and turbulent flow (Mode 2), or (3) simultaneously coupling a discrete pipe network while inserting a high-conductivity flow layer that can switch between laminar and turbulent flow (Mode 3). Conduit flow pipes (Mode 1) may represent dissolution or biological burrowing features in carbonate aquifers, voids in fractured rock, and (or) lava tubes in basaltic aquifers and can be fully or partially saturated under laminar or turbulent flow conditions. Preferential flow layers (Mode 2) may represent: (1) a porous media where turbulent flow is suspected to occur under the observed hydraulic gradients; (2) a single secondary porosity subsurface feature, such as a well-defined laterally extensive underground cave; or (3) a horizontal preferential flow layer consisting of many interconnected voids. In this second case, the input data are effective parameters, such as a very high hydraulic conductivity, representing multiple features. Data preparation is more complex for CFP Mode 1 (CFPM1) than for CFP Mode 2 (CFPM2). Specifically for CFPM1, conduit pipe locations, lengths, diameters, tortuosity, internal roughness, critical Reynolds numbers (NRe), and exchange conductances are required. CFPM1, however, solves the pipe network equations in a matrix that is independent of the porous media equation matrix, which may mitigate numerical instability associated with solution of dual flow components within the same matrix. CFPM2 requires less hydraulic information and knowledge about the specific location and hydraulic properties of conduits, and turbulent flow is approximated by modifying horizontal conductances assembled by the Block-Centered Flow (BCF), Layer-Property Flow (LPF), or Hydrogeologic-Unit Flow Packages (HUF) of MODFLOW-2005. For both conduit flow pipes (CFPM1) and preferential flow layers (CFPM2), critical Reynolds numbers are used to determine if flow is laminar or turbulent. Due to conservation of momentum, flow in a laminar state tends to remain laminar and flow in a turbulent state tends to remain turbulent. This delayed transition between laminar and turbulent flow is introduced in the CFP, which provides an additional benefit of facilitating convergence of the computer algorithm during iterations of transient simulations. Specifically, the user can specify a higher critical Reynolds number to determine when laminar flow within a pipe converts to turbulent flow, and a lower critical Reynolds number for determining when a pipe with turbulent flow switches to laminar flow. With CFPM1, the Hagen-Poiseuille equation is used for laminar flow conditions and the Darcy-Weisbach equation is applied to turbulent flow conditions. With CFPM2, turbulent flow is approximated by reducing the laminar hydraulic conductivity by a nonlinear function of the Reynolds number, once the critical head difference is exceeded. This adjustment approximates the reductions in mean velocity under turbulent ground-water flow conditions.

A novel encoding scheme for effective biometric discretization: Linearly Separable Subcode.

PubMed

Lim, Meng-Hui; Teoh, Andrew Beng Jin

2013-02-01

Separability in a code is crucial in guaranteeing a decent Hamming-distance separation among the codewords. In multibit biometric discretization where a code is used for quantization-intervals labeling, separability is necessary for preserving distance dissimilarity when feature components are mapped from a discrete space to a Hamming space. In this paper, we examine separability of Binary Reflected Gray Code (BRGC) encoding and reveal its inadequacy in tackling interclass variation during the discrete-to-binary mapping, leading to a tradeoff between classification performance and entropy of binary output. To overcome this drawback, we put forward two encoding schemes exhibiting full-ideal and near-ideal separability capabilities, known as Linearly Separable Subcode (LSSC) and Partially Linearly Separable Subcode (PLSSC), respectively. These encoding schemes convert the conventional entropy-performance tradeoff into an entropy-redundancy tradeoff in the increase of code length. Extensive experimental results vindicate the superiority of our schemes over the existing encoding schemes in discretization performance. This opens up possibilities of achieving much greater classification performance with high output entropy.

Hamiltonian dynamics of a quantum of space: hidden symmetries and spectrum of the volume operator, and discrete orthogonal polynomials

NASA Astrophysics Data System (ADS)

Aquilanti, Vincenzo; Marinelli, Dimitri; Marzuoli, Annalisa

2013-05-01

The action of the quantum mechanical volume operator, introduced in connection with a symmetric representation of the three-body problem and recently recognized to play a fundamental role in discretized quantum gravity models, can be given as a second-order difference equation which, by a complex phase change, we turn into a discrete Schrödinger-like equation. The introduction of discrete potential-like functions reveals the surprising crucial role here of hidden symmetries, first discovered by Regge for the quantum mechanical 6j symbols; insight is provided into the underlying geometric features. The spectrum and wavefunctions of the volume operator are discussed from the viewpoint of the Hamiltonian evolution of an elementary ‘quantum of space’, and a transparent asymptotic picture of the semiclassical and classical regimes emerges. The definition of coordinates adapted to the Regge symmetry is exploited for the construction of a novel set of discrete orthogonal polynomials, characterizing the oscillatory components of torsion-like modes.

GEMPIC: geometric electromagnetic particle-in-cell methods

NASA Astrophysics Data System (ADS)

Kraus, Michael; Kormann, Katharina; Morrison, Philip J.; Sonnendrücker, Eric

2017-08-01

We present a novel framework for finite element particle-in-cell methods based on the discretization of the underlying Hamiltonian structure of the Vlasov-Maxwell system. We derive a semi-discrete Poisson bracket, which retains the defining properties of a bracket, anti-symmetry and the Jacobi identity, as well as conservation of its Casimir invariants, implying that the semi-discrete system is still a Hamiltonian system. In order to obtain a fully discrete Poisson integrator, the semi-discrete bracket is used in conjunction with Hamiltonian splitting methods for integration in time. Techniques from finite element exterior calculus ensure conservation of the divergence of the magnetic field and Gauss' law as well as stability of the field solver. The resulting methods are gauge invariant, feature exact charge conservation and show excellent long-time energy and momentum behaviour. Due to the generality of our framework, these conservation properties are guaranteed independently of a particular choice of the finite element basis, as long as the corresponding finite element spaces satisfy certain compatibility conditions.

Black holes in loop quantum gravity.

PubMed

Perez, Alejandro

2017-12-01

This is a review of results on black hole physics in the context of loop quantum gravity. The key feature underlying these results is the discreteness of geometric quantities at the Planck scale predicted by this approach to quantum gravity. Quantum discreteness follows directly from the canonical quantization prescription when applied to the action of general relativity that is suitable for the coupling of gravity with gauge fields, and especially with fermions. Planckian discreteness and causal considerations provide the basic structure for the understanding of the thermal properties of black holes close to equilibrium. Discreteness also provides a fresh new look at more (at the moment) speculative issues, such as those concerning the fate of information in black hole evaporation. The hypothesis of discreteness leads, also, to interesting phenomenology with possible observational consequences. The theory of loop quantum gravity is a developing program; this review reports its achievements and open questions in a pedagogical manner, with an emphasis on quantum aspects of black hole physics.

An Optimal Mobile Service for Telecare Data Synchronization using a Role-based Access Control Model and Mobile Peer-to-Peer Technology.

PubMed

Ke, Chih-Kun; Lin, Zheng-Hua

2015-09-01

The progress of information and communication technologies (ICT) has promoted the development of healthcare which has enabled the exchange of resources and services between organizations. Organizations want to integrate mobile devices into their hospital information systems (HIS) due to the convenience to employees who are then able to perform specific healthcare processes from any location. The collection and merage of healthcare data from discrete mobile devices are worth exploring possible ways for further use, especially in remote districts without public data network (PDN) to connect the HIS. In this study, we propose an optimal mobile service which automatically synchronizes the telecare file resources among discrete mobile devices. The proposed service enforces some technical methods. The role-based access control model defines the telecare file resources accessing mechanism; the symmetric data encryption method protects telecare file resources transmitted over a mobile peer-to-peer network. The multi-criteria decision analysis method, ELECTRE (Elimination Et Choice Translating Reality), evaluates multiple criteria of the candidates' mobile devices to determine a ranking order. This optimizes the synchronization of telecare file resources among discrete mobile devices. A prototype system is implemented to examine the proposed mobile service. The results of the experiment show that the proposed mobile service can automatically and effectively synchronize telecare file resources among discrete mobile devices. The contribution of this experiment is to provide an optimal mobile service that enhances the security of telecare file resource synchronization and strengthens an organization's mobility.

Visual word ambiguity.

PubMed

van Gemert, Jan C; Veenman, Cor J; Smeulders, Arnold W M; Geusebroek, Jan-Mark

2010-07-01

This paper studies automatic image classification by modeling soft assignment in the popular codebook model. The codebook model describes an image as a bag of discrete visual words selected from a vocabulary, where the frequency distributions of visual words in an image allow classification. One inherent component of the codebook model is the assignment of discrete visual words to continuous image features. Despite the clear mismatch of this hard assignment with the nature of continuous features, the approach has been successfully applied for some years. In this paper, we investigate four types of soft assignment of visual words to image features. We demonstrate that explicitly modeling visual word assignment ambiguity improves classification performance compared to the hard assignment of the traditional codebook model. The traditional codebook model is compared against our method for five well-known data sets: 15 natural scenes, Caltech-101, Caltech-256, and Pascal VOC 2007/2008. We demonstrate that large codebook vocabulary sizes completely deteriorate the performance of the traditional model, whereas the proposed model performs consistently. Moreover, we show that our method profits in high-dimensional feature spaces and reaps higher benefits when increasing the number of image categories.

Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG.

PubMed

Bai, Ou; Lin, Peter; Vorbach, Sherry; Li, Jiang; Furlani, Steve; Hallett, Mark

2007-12-01

To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG). Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis (ICA), common spatial patterns analysis (CSP), and surface Laplacian derivation (SLD); temporal filtering using power spectral density estimation (PSD) and discrete wavelet transform (DWT); pattern classification using linear Mahalanobis distance classifier (LMD), quadratic Mahalanobis distance classifier (QMD), Bayesian classifier (BSC), multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), and support vector machine (SVM). A robust multivariate feature selection strategy using a genetic algorithm was employed. The combinations of spatial filtering using ICA and SLD, temporal filtering using PSD and DWT, and classification methods using LMD, QMD, BSC and SVM provided higher performance than those of other combinations. Utilizing one of the better combinations of ICA, PSD and SVM, the discrimination accuracy was as high as 75%. Further feature analysis showed that beta band EEG activity of the channels over right sensorimotor cortex was most appropriate for discrimination of right and left hand movement intention. Effective combinations of computational methods provide possible classification of human movement intention from single trial EEG. Such a method could be the basis for a potential brain-computer interface based on human natural movement, which might reduce the requirement of long-term training. Effective combinations of computational methods can classify human movement intention from single trial EEG with reasonable accuracy.

Adaptive Observatories for Observing Moving Marine Organisms (Invited)

NASA Astrophysics Data System (ADS)

Bellingham, J. G.; Scholin, C.; Zhang, Y.; Godin, M. A.; Hobson, B.; Frolov, S.

2010-12-01

The ability to characterize the response of small marine organisms to each other, and to their environment, is a demanding observational challenge. Small organisms live in a water reference frame, while existing cable or mooring-based observatories operate in an Earth reference frame. Thus repeated observations from a fixed system observe different populations as currents sweep organisms by the sensors. In contrast, mobile systems are typically optimized for spatial coverage rather than repeated observations of the same water volume. Lagrangian drifters track water mass, but are unable to find or reposition themselves relative to ocean features. We are developing a system capable of finding, following and observing discrete populations of marine organisms over time, leveraging a decade and a half investment in the Autonomous Ocean Sampling Network (AOSN) program. AOSN undertook the development of platforms to enable multi-platform coordinated measurement of ocean properties in the late 1990s, leading to the development of a variety of autonomous underwater vehicles (AUVs) and associated technologies, notably several glider systems, now in common use. Efforts by a number of research groups have focused on methods to employ these networked systems to observe and predict dynamic physical ocean phenomena. For example, periodic large scale field programs in Monterey Bay have progressively integrated these systems with data systems, predictive models, and web-based collaborative portals. We are adapting these approaches to follow and observe the dynamics of marine organisms. Compared to physical processes, the temporal and spatial variability of small marine organisms, particularly micro-organisms, is typical greater. Consequently, while multi-platform observations of physical processes can be coordinated via intermittent communications links from shore, biological observations require a higher degree of adaptability of the observation system in situ. This talk will describe the platform capabilities developed for such observations, the onboard intelligence for finding and observing discrete populations, and the cyberinfrastructure employed to understand and coordinate observations from shore.

Path statistics, memory, and coarse-graining of continuous-time random walks on networks

PubMed Central

Kion-Crosby, Willow; Morozov, Alexandre V.

2015-01-01

Continuous-time random walks (CTRWs) on discrete state spaces, ranging from regular lattices to complex networks, are ubiquitous across physics, chemistry, and biology. Models with coarse-grained states (for example, those employed in studies of molecular kinetics) or spatial disorder can give rise to memory and non-exponential distributions of waiting times and first-passage statistics. However, existing methods for analyzing CTRWs on complex energy landscapes do not address these effects. Here we use statistical mechanics of the nonequilibrium path ensemble to characterize first-passage CTRWs on networks with arbitrary connectivity, energy landscape, and waiting time distributions. Our approach can be applied to calculating higher moments (beyond the mean) of path length, time, and action, as well as statistics of any conservative or non-conservative force along a path. For homogeneous networks, we derive exact relations between length and time moments, quantifying the validity of approximating a continuous-time process with its discrete-time projection. For more general models, we obtain recursion relations, reminiscent of transfer matrix and exact enumeration techniques, to efficiently calculate path statistics numerically. We have implemented our algorithm in PathMAN (Path Matrix Algorithm for Networks), a Python script that users can apply to their model of choice. We demonstrate the algorithm on a few representative examples which underscore the importance of non-exponential distributions, memory, and coarse-graining in CTRWs. PMID:26646868

Investigation of the Effect of Cemented Fractures on Fracturing Network Propagation in Model Block with Discrete Orthogonal Fractures

NASA Astrophysics Data System (ADS)

Wang, Y.; Li, C. H.

2017-07-01

Researchers have recently realized that the natural fractures in shale reservoirs are often cemented or sealed with various minerals. However, the influence of cement characteristics of natural fracture on fracturing network propagation is still not well understood. In this work, laboratory-scaled experiments are proposed to prepare model blocks with discrete orthogonal fractures network with different strength of natural fracture, in order to reveal the influence of cemented natural fractures on the interactions between hydraulic fractures and natural fractures. A series of true triaxial hydraulic fracturing experiments were conducted to investigate the mechanism of hydraulic fracture initiation and propagation in model blocks with natural fractures of different cement strength. The results present different responses of interactions between hydraulic and natural fractures, which can be reflected on the pump pressure profiles and block failure morphology. For model blocks with fluctuated pump pressure curves, the communication degree of hydraulic and natural fractures is good, which is confirmed by a proposed new index of "P-SRV." The most significant finding is that too high and too low strength properties of cemented natural fracture are adverse to generate complex fracturing network. This work can help us better understand how cemented natural fractures affect the fracturing network propagation subsurface and give us reference to develop more accurate hydraulic fracturing models.

On the performance of voltage stepping for the simulation of adaptive, nonlinear integrate-and-fire neuronal networks.

PubMed

Kaabi, Mohamed Ghaith; Tonnelier, Arnaud; Martinez, Dominique

2011-05-01

In traditional event-driven strategies, spike timings are analytically given or calculated with arbitrary precision (up to machine precision). Exact computation is possible only for simplified neuron models, mainly the leaky integrate-and-fire model. In a recent paper, Zheng, Tonnelier, and Martinez (2009) introduced an approximate event-driven strategy, named voltage stepping, that allows the generic simulation of nonlinear spiking neurons. Promising results were achieved in the simulation of single quadratic integrate-and-fire neurons. Here, we assess the performance of voltage stepping in network simulations by considering more complex neurons (quadratic integrate-and-fire neurons with adaptation) coupled with multiple synapses. To handle the discrete nature of synaptic interactions, we recast voltage stepping in a general framework, the discrete event system specification. The efficiency of the method is assessed through simulations and comparisons with a modified time-stepping scheme of the Runge-Kutta type. We demonstrated numerically that the original order of voltage stepping is preserved when simulating connected spiking neurons, independent of the network activity and connectivity.