New Method For Modeling Research Articles

Dynalets: A new method for modelling and compressing biological signals. Applications to physiological and molecular signals

The biological information coming from electrophysiologic sensors like ECG, pulse sensor or from molecular signal devices like NMR spectrometry has to be visualized and manipulated in a compressed way for an efficient medical use by clinicians, if stored in scientific data bases or in personalized patient records repositories. Here, we define a new transform called Dynalet based on Liénard ordinary differential equations susceptible to model the mechanism at the source of the studied signal, and we propose to apply this new technique first to the modelling and compression of real biological periodic signals like ECG and pulse rhythm. We consider that the cardiovascular activity results from the summation of cellular oscillators located in the cardiac sinus node and we show that, as a result, the van der Pol oscillator (a particular Liénard system) fits well the ECG signal and the pulse signal. The reconstruction of the original signal (pulse or ECG) using Dynalet transform is then compared with that of Fourier, counting the number of parameters to be set for obtaining an expected signal-to-noise ratio. Then, we apply the Dynalet transform to the modelling and compression of molecular spectra obtained by protein NMR spectroscopy. The reconstruction of the original signal (peak) using Dynalet transform is again compared with that of Fourier. After reconstructing visually the peak, we propose to periodize the signal and give it to hear, the whole process being called the protein “stethoscope”.

The Key Parameter Modeling of SiC MOSFET

This thesis mainly analysed the internal relations between parameters of SiC MOSFET and its dynamic and static characteristics respectively. And furthermore, a new method for modeling of SiC MOSFET based on PSpice was proposed in this paper. By introducing voltage’s controlling for voltage source E, we can compensate and correct the difference between PSpice default value of gate threshold voltage and actual value. At the same time, we can adopt two different junction capacitance models to describe the changes of junction capacitance which were brought about by the difference of the terminal voltage. In the meanwhile, there are newly increased source-drain resistance of MOSFET and gate resistance with the temperature change of variable temperature sub models. New models are able to fully and accurately reflect SiC MOSFET’s static and dynamic characteristics. Moreover, it provides an important foundation for switch process analysis of SC MOSFET, loss calculation and main circuit design. The fact that the experimental testing result is highly coincident with the simulation results verifies the correctness and accuracy of the models.

Forecasting Real-Time Tourist Arrivals using Hierarchical Cluster and Gaussian Fitting Algorithm: A Case Study of Jiuzhai Valley

In the process of studying the number of tourist arrivals of each moment on every day, one of the key findings is a very good statistical regularity about tourist total arrivals. There exists high s imilarity among different scales of tourist arrivals, including the nearly same change trend. And on this basis this paper puts forward a new method of modeling and forecasting real-time tourist arrivals of each moment on every day. The forecasting process of the new proposed model is innovative. We consider the scales of tourist arrivals mainly, and model using hierarchical cluster and Gaus sian fitting algorithm according to the different scales of tourist arrivals, thus predict real-time arrivals of the future a scale (the over all size of the known) through the existing scales. Finally take Jiuzhai Valley as an example to analysis, and experimental results show that the forecast method is effective.

A method superior to adding percentiles when only limited anthropometric data such as percentile tables are available for design models

PurposeDesigners and ergonomists may occasionally be limited to using tables of percentiles of anthropometric data to model users. Design models that add or subtract percentiles produce unreliable estimates of the proportion of users accommodated, in part because they assume a perfect correlation between variables. Percentile data do not allow the use of more reliable modeling methods such as Principle Component Analysis. A better method is needed. ResultsA new method for modeling with limited data is described. It uses measures of central tendency (median or mean) of the range of possible correlation values to estimate the combined variance is shown to reduce error compared to combining percentiles. Second, use of the Chebyshev inequality allows the designer to more reliably estimate the percent accommodation when the distributions of the underlying anthropometric data are unknown than does combining percentiles. ConclusionThis paper describes a modeling method that is more accurate than combining percentiles when only limited data are available.

Design and Fabrication of Photonic Crystal Nano-Beam Resonator: Transmission Line Model

We present a new method for modeling and design of photonic crystal nano-beam resonators (PCNBRs) based on cascaded transmission lines. The proposed model provides an accurate estimate of the PCNBRs properties such as resonance wavelength and quality factor (Q) with much smaller computation cost as compared to the brute-force numerical methods. Furthermore, we have developed a straightforward technique for the design of high-Q PCNBRs based on resonance modes with Gaussian electromagnetic field profiles. The results obtained by using the proposed transmission line model are compared against numerical and experimental results and the accuracy of the model is verified. The proposed model provides an insight to silicon cavity design and significantly reduces computational burden.

Modeling ink diffusion within paper to achieve a raggedness ruler for print quality control

Raggedness is one of the most important print quality factors. It is defined as the appearance of geometric distortion of an edge from its ideal position that should be absolutely straight along the length of the line. Raggedness is caused by several phenomena. The non-uniformity absorption of ink, which may be because of the paper roughness or ink quality, may cause various degree of raggedness. Print quality assessments are usually based on comparing a test sample with a reference as a ruler of different quality samples. Therefore, it is important to prepare appropriate rulers for different quality metrics. The present study introduces a new method for modeling of raggedness by simulating ink diffusion within paper. It is assumed that different parts of a paper have different diffusion coefficients, so the ink can randomly diffuse through different paths. These paths can be changed via paper properties and drying time. Using this method and controlling the effective variables it is expected to generate different levels of raggedness. The experimental results show that with the proposed method it is possible to achieve different levels of raggedness, which can be used to generate test targets and raggedness rulers. Also, it can standardize objective and subjective test methods for measuring raggedness.

Modeling and design of networked control systems using a stochastic switching systems approach

AbstractA networked control system (NCS) is a control system in which plants, sensors, controllers, and actuators are connected through communication networks. In this paper, we consider NCSs modeled by stochastic switching systems, and propose a new method for modeling and optimal control. First, a recursive representation of the expected value of the state is proposed. Next, after an over‐approximation of this recursive representation is derived, the optimal control problem is reduced to a linear programming problem. Finally, the effectiveness of the proposed method is shown by a numerical example. The proposed method provides us an easy‐to‐use control method for NCSs. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Water Purification from Heavy Metal Ions in a Packed Column

The process of adsorption from water solutions containing ternary system of Cu(II), Zn(II) and Ni(II) ions in time-variable conditions in a packed column with lyophilized chitosan beads is considered. The system of partial differential equations describing the adsorption column, due to the assumption of a properly defined variable is transformed into a system of ordinary nonlinear equations which enables the identification of object parameters in simple experiments. Analytical solution of that system was possible due to the assumptions made regarding the equation describing sorption kinetics. Using experimental data, the effective diffusion coefficient Deff and the sorption kinetic constants K in the real environment were identified. The sorption capacity q* was obtained in separate sorption equilibrium experiments. Using a mathematical model the influence of initial concentration and solution flow rate on the breakthrough curve was determined. The approach which is presented provides a new method for modeling of sorption in a packed column.

A performance evaluation method for radio frequency identification–based tracking network of job-shop-type work-in-process material flows

With the rapid development of the modern manufacture, a radio frequency identification–based tracking network has been gradually established to monitor and track the material flows in a job-shop floor. Based on an event-driven graphical schema, this article puts forward a new method for modeling and analyzing the job-shop-type work-in-process material flows tracking network. The schema elements are mapped into the network nodes, and the association relationships among the elements are mapped into the network edges. Furthermore, corresponding measures and metrics are presented to evaluate the work-in-process material flows tracking network performance. Finally, a simple example is given to verify the feasibility of the proposed method. It is expected that this study will provide a systemic evaluation method for guiding and optimizing the radio frequency identification–based tracking configuration solution for job-shop-type material flows.

Joint operation of wind farms and pump-storage units in the electricity markets: Modeling, simulation and evaluation

Renewable power plants generation scheduling and unit commitment construct new problems of the power systems. In this paper, optimal scheduling of the joint operation (JO) and uncoordinated operation (UO) of wind farms and pump-storage plants in the energy and ancillary service markets are studied. For this purpose, a new method for modeling, simulation and evaluation of these units is presented. Since there are uncertainties in wind power generation (WPG) and the market prices, the scheduling problem is modeled by a stochastic optimization problem. Optimal bidding strategy of units is determined by solving this stochastic optimization problem. For this purpose, uncertainties are modeled by a scenario tree method. In order to evaluate the performance of the results of JO and UO of the plants, the value at risk (VaR) and the profit of the plants are compared. With JO of pump-storage plants and wind units, the profit of these plants in comparison with their UO will be increased. The results for pump-storage and wind farms of IEEE 118-bus standard system, verify that the JO of these units, improves the profit and VaR of the system.

Realistic Modeling of Far-End Crosstalk in Metallic Cables

Crosstalk and especially far-end crosstalk (FEXT) represents the most serious source of disturbance in today's digital transmission systems. It mostly limits the information capacity of current xDSL digital subscriber lines and local data networks with unshielded or shielded twisted pairs. The elimination of FEXT will require implementation of advanced methods for its modeling to obtain required predictions of its behavior. The standard FEXT model and ITU-T model gives only average results based on the sum of crosstalk in the entire cable, which is usually sufficient in case of summarizing FEXT contributions. However, the implementation of VDMT modulation would require individual modeling of FEXT crosstalk between all subscriber lines in a cable. That is why a new method for modeling of FEXT was developed and is presented here. The results of the model are also compared with measured characteristics for a typical UTP cable and several other cables. The proposed advanced FEXT model with minor modifications is applicable for any metallic cable to provide realistic FEXT characteristics.

Correlation-based analytical modeling of MIMO systems with hybrid-diversity antenna

By considering the correlations between cross-polarized sub-channels, the traditional multi-input multi-output (MIMO) channel models are proved to be unable to reflect the effect of antenna polarization on correlation coefficients. To solve the problem, a new modeling theory is proposed to amend the traditional model. Furthermore, the method of calculating correlation coefficients of the MIMO system with hybrid-diversity antennas in traditional channel models is proved to be an inaccurate approximation, and a new method of modeling with statistical data of MIMO systems with hybrid-diversity antennas is presented. The new model is able to replicate the correlation coefficients accurately and meet the high-precision requirements.

Virtual Endoscopy in Neurosurgery

Virtual endoscopy is the computerized creation of images depicting the inside of patient anatomy reconstructed in a virtual reality environment. It permits interactive, noninvasive, 3-dimensional visual inspection of anatomical cavities or vessels. This can aid in diagnostics, potentially replacing an actual endoscopic procedure, and help in the preparation of a surgical intervention by bridging the gap between plain 2-dimensional radiologic images and the 3-dimensional depiction of anatomy during actual endoscopy. If not only the endoscopic vision but also endoscopic handling, including realistic haptic feedback, is simulated, virtual endoscopy can be an effective training tool for novice surgeons. In neurosurgery, the main fields of the application of virtual endoscopy are third ventriculostomy, endonasal surgery, and the evaluation of pathologies in cerebral blood vessels. Progress in this very active field of research is achieved through cooperation between the technical and the medical communities. While the technology advances and new methods for modeling, reconstruction, and simulation are being developed, clinicians evaluate existing simulators, steer the development of new ones, and explore new fields of application. This review introduces some of the most interesting virtual reality systems for endoscopic neurosurgery developed in recent years and presents clinical studies conducted either on areas of application or specific systems. In addition, benefits and limitations of single products and simulated neuroendoscopy in general are pointed out.

A Genetic Algorithm Approach for Prediction of Linear Dynamical Systems

Modelling of linear dynamical systems is very important issue in science and engineering. The modelling process might be achieved by either the application of the governing laws describing the process or by using the input-output data sequence of the process. Most of the modelling algorithms reported in the literature focus on either determining the order or estimating the model parameters. In this paper, the authors present a new method for modelling. Given the input-output data sequence of the model in the absence of any information about the order, the correct order of the model as well as the correct parameters is determined simultaneously using genetic algorithm. The algorithm used in this paper has several advantages; first, it does not use complex mathematical procedures in detecting the order and the parameters; second, it can be used for low as well as high order systems; third, it can be applied to any linear dynamical system including the autoregressive, moving-average, and autoregressive moving-average models; fourth, it determines the order and the parameters in a simultaneous manner with a very high accuracy. Results presented in this paper show the potentiality, the generality, and the superiority of our method as compared with other well-known methods.

Analysis of complex-valued functional magnetic resonance imaging data: are we just going through a “phase”?

Abstract Functional magnetic resonance imaging (fMRI) data are acquired as a natively complex data set, however for various reasons the phase data is typically discarded. Over the past few years, interest in incorporating the phase information into the analyses has been growing and new methods for modeling and processing the data have been developed. In this paper, we provide an overview of approaches to understand the complex nature of fMRI data and to work with the utilizing the full information, both the magnitude and the phase. We discuss the challenges inherent in trying to utilize the phase data, and provide a selective review with emphasis on work in our group for developing biophysical models, preprocessing methods, and statistical analysis of the fully-complex data. Of special emphasis are the use of data-driven approaches, which are particularly useful as they enable us to identify interesting patterns in the complex-valued data without making strong assumptions about how these changes evolve over time, something which is challenging for magnitude data and even more so for the complex data. Finally, we provide our view of the current state of the art in this area and make suggestions for what is needed to make efficient use of the fully-complex fMRI data.

Modeling of the optical system illuminated by quasi-monochromatic spatially incoherent light: new numerical approach

This Letter presents a new method for modeling of complex optical setups illuminated by quasi monochromatic spatially incoherent light. The algorithm provides better performance and quality than other modeling methods both for isoplanatic and nonisoplanatic systems. The algorithm maintains energy relations, image orientation, and magnification of the system. Computer modeling and experimental results are presented.

Parametric Modeling of Whole Tower Crane Based on ANSYS

The conventional finite element modeling of the tower in the ANSYS GUI, in particular, the modeling of the whole tower crane which is large and complex, always gives rise to low efficiency and less authenticity. A new method of modeling of whole tower crane is presented here. This paper applies the APDL which stands for ANSYS Parametric Design Language to separately build crane’s components :the mast, the tower head, the counter-jib ,the jib and the simplified slewing platform. And then accomplishes the parametric finite element model of the whole tower by using the assembly and connection technology provided by ANSYS to assemble and connect them. Further, based on these methods and Visual Basic Programming Software, the parametric design software on tower crane has been developed. At the end of the paper, the paper takes the H25/23 tower crane as an analysis example to demonstrate that such a method is not only technically feasible, but also cost-effective. The details of the parameterized modeling and related technologies are given in the paper.

Local Gaussian correlation: A new measure of dependence

It is a common view among finance analysts and econometricians that the correlation between financial objects becomes stronger as the market is going down, and that it approaches one when the market crashes, having the effect of destroying the benefit of diversification. The purpose of this paper is to introduce a local dependence measure that gives a precise mathematical description and interpretation of such phenomena. We propose a new local dependence measure, a local correlation function, based on approximating a bivariate density locally by a family of bivariate Gaussian densities using local likelihood. At each point the correlation coefficient of the approximating Gaussian distribution is taken as the local correlation. Existence, uniqueness and limit results are established. A number of properties of the local Gaussian correlation and its estimate are given, along with examples from both simulated and real data. This new method of modelling carries with it the prospect of being able to do locally for a general density what can be done globally for the Gaussian density. In a sense it extends Gaussian analysis from a linear to a non-linear environment.

Description of the plasma delay effect in silicon detectors

A new method of modeling of the current signal induced by charged particle in silicon detectors is presented. The approach is based on the Ramo–Shockley theorem for which the charge carrier velocities are determined by taking into account not only the external electric field generated by the electrodes but also the Coulomb interaction between the electron and hole clouds as well as their diffusion.

Asymptotic modelling and design of some microlayered functionally graded heat conductors

AbstractThe paper presents a new method of modelling for certain microlayered heat conductors. So far the main attention related to the modelling of composite materials with deterministic structure was mainly restricted to the periodic composites (Bensoussan, Lions, and Papanicolaou [1]). The main future of the presented approach is that it can be also applied to the class with a non periodic structure. These structures can by referred to as functionally graded in the direction normal to the interfaces between layers. At the same time the non‐homogenous material structure of the composite is slowly varying in this direction. That is why the obtained model equations have functionally but slowly varying coefficients. Moreover, this equations can by applied for designing functionally graded heat conductors with given a priori class of temperature fields.