New Method For Modeling Research Articles

Probabilistic passage models for semantic search of genomics literature

AbstractWe explore unsupervised learning techniques for extracting semantic information about biomedical concepts and topics, and introduce a passage retrieval model for using these semantics in context to improve genomics literature search. Our contributions include a new passage retrieval model based on an undirected graphical model (Markov Random Fields), and new methods for modeling passage‐concepts, document‐topics, and passage‐terms as potential functions within the model. Each potential function includes distributional evidence to disambiguate topics, concepts, and terms in context. The joint distribution across potential functions in the graph represents the probability of a passage being relevant to a biologist's information need. Relevance ranking within each potential function simplifies normalization across potential functions and eliminates the need for tuning of passage retrieval model parameters. Our dimensional indexing model facilitates efficient aggregation of topic, concept, and term distributions. The proposed passage‐retrieval model improves search results in the presence of varying levels of semantic evidence, outperforming models of query terms, concepts, or document topics alone. Our results exceed the state‐of‐the‐art for automatic document retrieval by 14.46% (0.3554 vs. 0.3105) and passage retrieval by 15.57% (0.1128 vs. 0.0976) as assessed by the TREC 2007 Genomics Track, and automatic document retrieval by 18.56% (0.3424 vs. 0.2888) as assessed by the TREC 2005 Genomics Track. Automatic document retrieval results for TREC 2007 and TREC 2005 are statistically significant at the 95% confidence level (p = .0359 and .0253, respectively). Passage retrieval is significant at the 90% confidence level (p = 0.0893).

혼잡한 환경에서 적응적 가우시안 혼합 모델을 이용한 계층적 객체 검출

For the detection of moving objects, background subtraction methods are widely used. In case the background has variation, we need to update the background in real-time for the reliable detection of foreground objects. Gaussian mixture model (GMM) combined with probabilistic learning is one of the most popular methods for the real-time update of the background. However, it does not work well in the complex and dynamic backgrounds with high traffic regions. In this paper, we propose a new method for modelling and updating more reliably the complex and dynamic backgrounds based on the probabilistic learning and the layered processing.

Discrete Event Modeling and Simulation of Wireless Sensor Network Performance

The wireless distributed microsensor networks have profited from recent technological advances and it seems essential to precisely understand these systems. Modeling and simulation appear to be an essential aspect of predicting the Wireless Sensor Network (WSN) specific behavior under different conditions. We want to provide a new method of modeling, simulation and visualization of WSN using a discrete-event approach. Described by Zeigler in the 1970's, the Discrete Event System Specification is ideal for describing the asynchronous nature of the events occuring in WSN. We have provided a basic model for the analysis of WSN performance, including routing management, energy consumption and relative CPU activity. Our approach uses a detailed definition of node-oriented components and aims to introduce methods of visualizing the network at a different level of abstraction.

Subsurface Texture Mapping

Designing and rendering translucent material requires a faithful description of its aspects as well as a realistic simulation of its interaction with light. A new method for modeling and rendering complex organic materials made up of multiple layers of variable thickness uses simple texture-mapping principles and a single-scattering computation.

Control and modeling of temperature distribution in a tubular polymerization process

Temperature distribution along tubular reactors reveals the process of a polymerization reaction, so that it can be used as an indicator to monitor polymer molecular weight distribution. In this study, model of temperature distribution in a tubular polymerization reaction was developed using a B-spline neural network in conjunction with a linear recurrent neural network for the control purpose. This provides a new method for modeling distributed parameter system. Both dynamic and static neural network were applied to resolve the modeling of distribution function from a high dimensional data set. The dynamic neural network describes the time relationship of distribution function and the manipulated variables, whereas the static neural network describes the algebraic relationship of temperature distribution and position in tubular reactor. Using the error set of the expected and the measured temperature distribution as control indexes, optimal control sequence based on the distribution model can be derived. An adaptive control strategy was investigated under conditions with un-measurable noises and disturbances. An extended integral square error (EISE) control index was proposed, which introduces the real-time model error into the control strategy. This provides a feedback channel for the control, and therefore largely enhances the robustness and anti-disturbance performance of the control method. Simulation results demonstrate the effectiveness of the proposed method.

Cyber surgery: Parameterized mesh for multi-modal surgery simulation

This paper presents a new method for modeling of virtual organs for surgery simulation. The organ models have a parameterized representation that supports real-time interactive deformation. To accomplish real-time interaction with deformable organs, instead of computing the deformation on the 3D organ models in 3D space we use a novel yet simple and fast free-form deformation on the 2D parameterized representation itself. With the parameterized mesh, we also demonstrate that realistic visual and haptic rendering can be provided for interactive surgery simulation.

Modelling of organic liquid-phase decomposition reactions through gas-phase product analysis: Model systems and peracetic acid

A new method for modelling of liquid-phase decomposition reactions was developed. The method is based on rapid on-line mass spectrometric analysis of liberated products in gas phase. The experimental set-up and the generalized modelling principles were introduced. The approach was demonstrated with first and zero order as well as for complex decomposition kinetics (decomposition of peracetic acid). The interaction of decomposition kinetics and mass-transfer effects was illustrated with theoretical and real reaction conditions. The approach is generally applicable to any liquid-phase decomposition, provided that gaseous products can be quantitatively analyzed.

A method for dynamic crack and shear band propagation with phantom nodes

AbstractA new method for modelling of arbitrary dynamic crack and shear band propagation is presented. We show that by a rearrangement of the extended finite element basis and the nodal degrees of freedom, the discontinuity can be described by superposed elements and phantom nodes. Cracks are treated by adding phantom nodes and superposing elements on the original mesh. Shear bands are treated by adding phantom degrees of freedom. The proposed method simplifies the treatment of element‐by‐element crack and shear band propagation in explicit methods. A quadrature method for 4‐node quadrilaterals is proposed based on a single quadrature point and hourglass control. The proposed method provides consistent history variables because it does not use a subdomain integration scheme for the discontinuous integrand. Numerical examples for dynamic crack and shear band propagation are provided to demonstrate the effectiveness and robustness of the proposed method. Copyright © 2006 John Wiley & Sons, Ltd.

Modeling surface roughness in the stone polishing process

In this paper, a new method for modeling and predicting the surface roughness of the workpiece in the stone polishing process is developed. This method is based on the random distribution of the stone grain protrusion heights and the force balance by contact grains. To do so, first, the topography of a polishing stone is generated based on a Gaussian distribution with the mean value and standard deviation determined from a given stone grit number. Second, the plasticity theory is applied to determine the micro depth of cut of a single grain for a given workpiece hardness (Brinell number). Third, a search method is developed to determine the number of the contact grains and the micro depth of cut, based on the force balance principle between the force applied on the stone and the forces transmitted to the grains that are in contact with the workpiece. Fourth, a method is presented for predicting the surface roughness based on the micro depth of cut and contact grains. A good agreement of the prediction results with the experimental data proves the effectiveness of the proposed method.

On the equivalence between stratified media and oscillators

SUMMARY A new method of modelling scattered seismic pulses is developed, based on the deformation that suffers an initial pulse due to scattering and on a reinterpretation of the product of convolution of a Green function with a source time function. In this new interpretation, the convolution product is equivalent to the solution of an ordinary differential equation, in which the source term corresponds to the solution of the homogeneous differential equation. The medium response (identified with the Green function) corresponds to the inhomogeneous term of the differential equation. Numerical solutions show the equivalence between the proposed method and the classical one based on convolution. This method can easily be generalized to any kind of complex medium and source time function.

New method for modelling and design of multiconductor airborne antennas

A new approach is proposed, based on numerical (FDTD) and analytical (multiconductor transmission line MTL) tools, for the modelling of HF loop antennas mounted on aircraft. This method can be used to calculate the significant mutual coupling and the interaction with the body of the aircraft and also to perform fast optimisation of antenna size and position.

A New Method for Modelling of Large Scale Interval Systems

In recent years, simulation, design and control of systems with parametric uncertainties for the entire range of operation gained much importance. This paper presents a new method for reducing order of Large Scale Interval systems. The proposed method involves simple computations, and is efficient as it needs the formulation of only one Routh type tabulation, avoiding the necessity of formulating two tables viz., γ and δ tables, and avoids the computation of time moments of the original high order Interval system beforehand, unlike other available methods [1,2]. The proposed Simplified Routh Approximation method (SRAM) always generates stable reduced order Interval systems, retaining the initial time moments of the original high order system. The proposed simplified method is extended for the reduction of high order Multivariable and Discrete Interval systems to overcome the limitations and drawbacks of some of the existing methods [1,2] in literature. Typical numerical examples are considered to illustrate the flexibility and effectiveness of the proposed method.

Hierarchical facial data modeling for visual expression synthesis

This paper presents a new method for modeling of a personalized 3D face model that has a hierarchical structure of the skin, muscle, and skull for realistic expression synthesis. We start from an accurate facial mesh reconstructed from the individual face measurements. The deformable skin model has a multi-layer structure to approximate different types of soft tissue and takes into account the nonlinear stress-strain relationship and incompressibility of the real skin. The 3D face model incorporates the skull structure which extends the scope of facial motion and facilitates interactive facial muscle insertion. Under the muscular force, the deformation of the facial skin is evaluated by the numerical integration of the governing Lagrangian dynamics. The resulting animation system enables realistic facial expressions to be dynamically synthesized.

Constraints-driven scheduling and resource assignment

This paper describes a new method for modeling and solving different scheduling and resource assignment problems that are common in high-level synthesis (HLS) and system-level synthesis. It addresses assignment of resources for operations and tasks as well as their static, off-line scheduling. Different heterogeneous constraints are considered for these problems. These constraints can be grouped into two classes: problem-specific constraints and design-oriented constraints. They are uniformly modeled, in our approach, by finite domain (FD) constraints and solved using related constrained programming (CP) techniques. This provides a way to improve quality of final solutions. We have developed in Java a constraint solver engine, JaCoP ( Ja va Co nstraint P rogramming), to evaluate this approach. This solver and a related framework make it possible to model different resource assignment and scheduling problems, and handle them uniformly. The JaCoP prototype system has been extensively evaluated on a number of HLS and system-level synthesis benchmarks. We have been able to obtain optimal results together with related proofs of optimality for all HLS scheduling benchmarks and for all explored design styles (except one functional pipeline design). Many system-level benchmarks can also be solved optimally. For large randomly generated task graphs, we have used heuristic search methods and obtained results that are 1--3% worse than lower bounds or optimal results. These experiments have proved the feasibility of the presented approach.

Theory and experiment of Fourier–Bessel field calculation and tuning of a pulsed wave annular array

A one-dimensional (1D) Fourier-Bessel series method for computing and tuning (beamforming) the linear lossless field of flat pulsed wave annular arrays is developed and supported with both numerical simulation and experimental verification. The technique represents a new method for modeling and tuning the propagated field by linking the quantized surface pressure profile to a known set of limited diffraction Bessel beams propagating into the medium. This enables derivation of an analytic expression for the field at any point in space and time in terms of the transducer surface pressure profile. Tuning of the field then also follows by formulating a least-squares design for the transducer surface pressure with respect to a given desired field in space and time. Simulated and experimental results for both field computation and tuning are presented in the context of a 10-ring annular array operating at a central frequency of 2.5 MHz in water.

Fourier-Bessel Field Calculation and Tuning of a CW Annular Array

A 1-D Fourier-Bessel series method for computing and tuning the linear lossless field of flat continuous wave (CW) annular arrays is given and discussed with both numerical simulation and experimental verification. The technique provides a new method for modelling and manipulating the propagated field by linking the quantized surface pressure profile to a set of limited diffraction Bessel beams propagating into the medium. In the limit, these become a known set of nondiffracting Bessel beams satisfying the lossless linear wave equation, which allow us to derive a linear matrix formulation for the field in terms of the ring pressures on the transducer surface. Tuning (beamforming) of the field then follows by formulating a least squares design with respect to the transducer ring pressures. Results are presented in the context of a 10-ring annular array operating at 2.5 MHz in water.

Union: A new approach for combining sub-band observations for noisy speech recognition

Recent studies have shown that the sub-band based speech recognition approach has the potential of improving upon the conventional, full-band based model against frequency-selective noise. A critical issue towards exploiting this potential is the choice of the method for combining the sub-band observations. This paper introduces a new method, namely, the probabilistic-union model, for this combination. The new model is based on the probability theory for the union of random events, and represents a new method for modeling partially corrupted observations given little knowledge about the corruption. The new model has been incorporated into a hidden Markov model (HMM) and tested for recognizing a speaker-independent E-set, corrupted by various types of additive noise. The results show that the new model offers robustness to partial frequency corruption, requiring little or no knowledge about the noise statistics.

An efficient technique for modeling and synthesis of automotive engine sounds

This paper presents a new method for modeling and synthesis of automotive engine sounds using a deterministic-stochastic signal decomposition approach. First, the deterministic component is extracted using a synchronous discrete Fourier transform method and this is subtracted out from the original signal. Next, the (residual) stochastic component is modeled (and synthesized) using a new multipulse excited time-series modeling technique. The effectiveness of the proposed methodology is demonstrated using recorded data sets of actual engine sounds. The results of both numerical and subjective assessment tests are presented.

Virtual-crystal approximation that works: Locating a compositional phase boundary inPb(Zr1−xTix)O3

We present a new method for modeling disordered solid solutions, based on the virtual crystal approximation (VCA). The VCA is a tractable way of studying configurationally disordered systems; traditionally, the potentials which represent atoms of two or more elements are averaged into a composite atomic potential. We have overcome significant shortcomings of the standard VCA by developing a potential which yields averaged atomic properties. We perform the VCA on a ferroelectric oxide, determining the energy differences between the high-temperature rhombohedral, low-temperature rhombohedral and tetragonal phases of Pb(Zr_{1-x}Ti_x)O_3 at x=0.5 and comparing these results to superlattice calculations and experiment. We then use our new method to determine the preferred structural phase at x=0.4. We find that the low-temperature rhombohedral phase becomes the ground state at x=0.4, in agreement with experimental findings.

A comparison of different methods for the estimation of regression models with correlated binary responses

In the present paper results of a simulation study designed to evaluate the small sample properties of three different estimators for regression models with correlated binary responses are presented. Throughout, binary panel probit models are considered, where an underlying latent continuous random variable crosses a threshold. The estimation procedures considered are (1) marginal maximum likelihood (ML) estimation using Gauss–Hermite quadrature, (2) generalized estimating equations (GEE) techniques and, (3) the MECOSA (‘mean and covariance structure analysis’ (1991)) approach proposed by Schepers et al. (1991) (The analysis of non-metric endogenous variables in latent variable models: the MECOSA approach. In: Gruber, J. (Ed.), Econometric Decision Models: New Methods of Modelling and Applications, Lecture Notes in Economics and Mathematical Systems, Vol. 366. Springer, Berlin, pp. 459–472.) for hierarchical mean and covariance structure models. The results show that for small and moderate sample sizes calculation of the MECOSA estimators is problematic because of convergence problems and a tendency to underestimate the root mean squared errors. On the other hand, the portion of data sets for which the ML and GEE estimation procedures failed to converge was low. In general, the MECOSA estimators are not as efficient as ML or GEE estimators. For small ‘true’ correlations, differences between ML and GEE estimators with respect to efficiency are negligible. However, for larger correlations, the ML estimator is more efficient than the GEE estimator.