Parts Of Variables Research Articles

Efficient InSAR phase noise reduction via total variation regularization

We consider the phase noise filtering problem for Interferometric Synthetic Aperture Radar (InSAR) using a total variation regularized complex linear least squares formulation. Although the original formulation is convex, solving it directly with the standard CVX package is time consuming due to the large problem size. In this paper, we introduce the effective and efficient alternating direction method of multipliers (ADMM) to solve the equivalent well-defined complex formulation for the real and imaginary parts of the optimization variables. Both the iteration complexity and the computational complexity of the ADMM are established in the forms of theorems for our InSAR phase noise problem. Simulation results based on simulated and measured data show that this new InSAR phase noise reduction method not only is 3 orders of magnitude faster than the standard CVX solver, but also has a much better performance than the several existing phase filtering methods.

Prediction of Depression through Early Maladaptive Schemas

The aim of the present project was assessing the correlation between Young's early maladaptive schemas model and depression symptoms between non-hospitalized depressed patients and healthy group. Early maladaptive schemas (EMSs) are stable and trait-like underlying beliefs that considered as the infrastructure of recurrent and chronic psychological disorders. In this study, the correlation between these schemas and chronic depression disorders are evaluated. In this project, 47 non-hospitalized depressed patients who referred to the health care and clinical centers of Kermanshah city, during a year, were detected by using structured interview and Beck Depression Inventory (BDI-II). Also, 49 cases referred to clinics for mild problems were considered as the control group (=N96). Fifteen EMSs were measured through Young Schemas Questionnaire-Short Form Schemas (YSQ-SF). The data was analyzed using correlation and multiple regression method. The highest meaningful correlation was ascribed to the schemas of disease and vulnerability to harm (r=0.64) and the lowest one was achieved by schemas of constraints hampered (r=0.45). By using stepwise multiple regression model, with combination of four schemas of disease and vulnerability to harm, social isolation, emotional inhibition, and continence/insufficient discipline about 61% (r2=0.61) of chronic depression changes could be explained. The four schemas of EMS, vulnerability, social isolation, emotional inhibition, and continence/insufficient discipline enable to present the largest amount of variance of the dependent variable or so called chronic depression. However, schemas like failure to achieve, abandonment, mistrust, emotional deprivation, defectiveness / shame, dependency, subjugation, self-sacrifice, unrelenting standards, entitlement, and self-control cannot significantly predict the major parts of dependent variables. The findings showed that EMSs have a positive correlation with the severity of depression' and chronic depression can be predicted with a high accuracy through these schemas. The accuracy of this prediction in the rejection cut is higher than other fields. DOI: 10.5901/mjss.2015.v6n1s1p602

Observations and models of highly intermittent phytoplankton distributions.

The measurement of phytoplankton distributions in ocean ecosystems provides the basis for elucidating the influences of physical processes on plankton dynamics. Technological advances allow for measurement of phytoplankton data to greater resolution, displaying high spatial variability. In conventional mathematical models, the mean value of the measured variable is approximated to compare with the model output, which may misinterpret the reality of planktonic ecosystems, especially at the microscale level. To consider intermittency of variables, in this work, a new modelling approach to the planktonic ecosystem is applied, called the closure approach. Using this approach for a simple nutrient-phytoplankton model, we have shown how consideration of the fluctuating parts of model variables can affect system dynamics. Also, we have found a critical value of variance of overall fluctuating terms below which the conventional non-closure model and the mean value from the closure model exhibit the same result. This analysis gives an idea about the importance of the fluctuating parts of model variables and about when to use the closure approach. Comparisons of plot of mean versus standard deviation of phytoplankton at different depths, obtained using this new approach with real observations, give this approach good conformity.

Improved Rao-Blackwellized Particle Filter by Particle Swarm Optimization

The Rao-Blackwellized particle filter (RBPF) algorithm usually has better performance than the traditional particle filter (PF) by utilizing conditional dependency relationships between parts of the state variables to estimate. By doing so, RBPF could not only improve the estimation precision but also reduce the overall computational complexity. However, the computational burden is still too high for many real-time applications. To improve the efficiency of RBPF, the particle swarm optimization (PSO) is applied to drive all the particles to the regions where their likelihoods are high in the nonlinear area. So only a small number of particles are needed to participate in the required computation. The experimental results demonstrate that this novel algorithm is more efficient than the standard RBPF.

Mean-Variance Portfolio Selection with Margin Requirements

We study the continuous-time mean-variance portfolio selection problem in the situation when investors must pay margin for short selling. The problem is essentially a nonlinear stochastic optimal control problem because the coefficients of positive and negative parts of control variables are different. We can not apply the results of stochastic linearquadratic (LQ) problem. Also the solution of corresponding Hamilton-Jacobi-Bellman (HJB) equation is not smooth. Li et al. (2002) studied the case when short selling is prohibited; therefore they only need to consider the positive part of control variables, whereas we need to handle both the positive part and the negative part of control variables. The main difficulty is that the positive part and the negative part are not independent. The previous results are not directly applicable. By decomposing the problem into several subproblems we figure out the solutions of HJB equation in two disjoint regions and then prove it is the viscosity solution of HJB equation. Finally we formulate solution of optimal portfolio and the efficient frontier. We also present two examples showing how different margin rates affect the optimal solutions and the efficient frontier.

Stochastic synchronization of nonlinear energy resource system via partial feedback control

This paper addresses stochastic synchronization issue of the four-dimensional energy resource system with stochastic noises based on partial states. By using the adaptive control approach, in the case of only parts of states are available, several sufficient conditions are derived to ensure synchronization of the nonlinear energy resource system under noise perturbation. Furthermore, robust synchronization in the presence of parameter mismatches is also considered. To be pointed out that, the important feature of the proposed method is that synchronization could be achieved through the control of only parts of available variables even with stochastic noises. Finally, a numerical example with simulation results is provided to illustrate and verify the effectiveness of the obtained results.

EXPORT AND INVESTMENT IN FISHERIES SECTOR IN MALUKU PROVINCE

Export and investment are considered important parts of the macro variables that affect economicgrowth. The purpose of this study in long-term perspective is to analyze the dynamicbehavior of export and investment and the movement direction of causality between exportsand investment in the province of Maluku. Data used in this research is secondary data, theperiod of 1990-2009. Estimation methods used in this study is Granger Causality Test, VectorAutoregressive (VAR). Results show that based on Granger causality test there is one-waydirection from investment variable to the export of fisheries at lag 1-4, and at lag 5 occurringin two-way relationship between two variables in the model. This proves that investment is avery good instrument to stimulate the expansion of fisheries exports sector towards economicopenness. Dynamic behavior of the movement of exports and investment shows that there arereciprocal relationships between export and investment the results. It provides evidence thatIRF shocks/innovations of the investment variables have a direct impact on exports and viceversa. This means that if export variable decreases, the investment variable also decreases.On the contrary, when export variable increases, the investment variable also increases. Yet,the Cholesky decomposition results show that shocks on fishery exports can be explained eitherby itself or investment. It indicates that variable of investment give better dynamic responsesand more dominant compared to export variable.

Weak convergence of sequences from fractional parts of random variables and applications

We prove results concerning the weak convergence to the uniform distribution on $[0,1]$ of sequences $(Z_n)_{n \geq 1}$ of the form $Z_n = Y_n \pmod 1= \{Y_n \}$, where $(Y_n)_{n \geq 1}$ is a general sequence of real random variables. Applications are given: (i) to the case of partial sums of (i.i.d.) random variables having a distribution belonging to the domain of attraction of a stable law; (ii) to the case of sample maxima of i.i.d. random variables.

Distributionally Robust Optimization and Its Tractable Approximations

In this paper we focus on a linear optimization problem with uncertainties, having expectations in the objective and in the set of constraints. We present a modular framework to obtain an approximate solution to the problem that is distributionally robust and more flexible than the standard technique of using linear rules. Our framework begins by first affinely extending the set of primitive uncertainties to generate new linear decision rules of larger dimensions and is therefore more flexible. Next, we develop new piecewise-linear decision rules that allow a more flexible reformulation of the original problem. The reformulated problem will generally contain terms with expectations on the positive parts of the recourse variables. Finally, we convert the uncertain linear program into a deterministic convex program by constructing distributionally robust bounds on these expectations. These bounds are constructed by first using different pieces of information on the distribution of the underlying uncertainties to develop separate bounds and next integrating them into a combined bound that is better than each of the individual bounds.

Observation of state variables in resonant DC-DC converter using the high gain design approach

We are considering the problem of state observation for in series resonant converters (SRC). This is a crucial issue in SRC output voltage control because the control model is nonlinear and involves nonphysical state variables, namely real and imaginary parts of complex electrical variables. A high gain observer is designed that ensures global exponential convergence of state estimates to their true values. The observer design involves no resolution of complex dynamical or algebraic equation. The global and exponential features of state estimate convergence make the observer utilizable in control.

Interconnected state observers for series resonant converters

We are considering the problem of state observation in series resonant converters (SRC). This is a crucial issue in SRC output voltage control as the control model is nonlinear and involves nonphysical state variables, namely real and imaginary parts of complex electrical variables. An interconnected high gain observer is designed to get online estimates of these states. The observer is shown to be globally asymptotically stable. Simulation results estimation, the global stability of the observer is analytically treated using the lyapunov theory; finally we present numerical simulation to illustrate the performance of the suggested approach.

SU‐FF‐T‐256: IMRT Inverse Planning with Voxel‐Based Penalty Scheme

Purpose: A tacit assumption made in current inverse planning algorithms is that all points within a structure (target or sensitive organ) are equivalent, as reflected by the fact that the dose prescription, weighting factors and dose‐volume constraints are generally imposed a priori on a structure‐specific basis. This seriously limits the IMRT solution space and compromises the achievable dose distributions. The purpose of this study is to develop a novel IMRT inverse planning framework with a more general voxel‐specific penalty. Methods: Different from conventional inverse planning in which the same penalty (objective) function is used for all voxels, here we assign each voxel a penalty proportional to where Dc and D0 are computed and prescribed doses, respectively, and ni is a voxel‐specific exponent. In this way, we “break” the implicit constraint that a structure is a mathematically uniform entity with all voxels having the same “identity”. The seemingly insolvable task of determining a large number of voxel‐specific {ni} is dealt by heuristically relating them to the doses they receive. The optimization starts from a uniform value of and, during the iterative optimization process, the ni's are treated as parts of the system variables and are updated together with the beamlet weights. If a voxel is overdosed, for example, it means that the voxel is not penalized sufficiently and the algorithm will automatically increase the value of ni, and vice versa. Results: A voxel‐dependent penalty scheme is established for IMRT inverse planning. The formalism properly models the intra‐organ tradeoff and allows the optimizer to access solutions that would otherwise be inaccessible. Our phantom and clinical head‐and‐neck case studies show that much superior treatment plans can be obtained as compared with the conventional approaches. Conclusion: Voxel‐based penalty scheme leads to substantially improved IMRT dose distributions and allows us to maximally utilize the IMRT technology.

On probabilities of moderate deviations of sums for independent random variables

We investigate asymptotics of probabilities of moderate deviations and their logarithms for an array of row-wise independent random variables with finite variations and finite one-sided moments of order p > 2. The range of the zone of normal convergence is calculated in terms of Lyapunov ratios constructed from the positive parts of the random variables. Bounds for probabilities of moderate deviations are also derived in the case where the normal convergence fails. Bibliography: 16 titles.

A method for structured linear total least norm on blind deconvolution problem

The regularized structured total least norm (RSTLN) method finds an approximate solutionx and error matrixE to the overdetermined linear system (H+E)x≈b, preserving structure ofH. A new separation scheme by parts of variables for the regularized structured total least norm on blind deconvolution problem is suggested. A method combining the regularized structured total least norm method with a separation by parts of variables can be obtain a better approximated solution and a smaller residual. Computational results for the practical problem with Block Toeplitz with Toeplitz Block structure show the new method ensures more efficiency on image restoration.

Improving the efficiency of time-delayed feedback control of chaos through linear invertible transform

A scheme,which can be used to effectively improve the quality of time-delayed feedback control of chaos,was proposed based on linear invertible transform.Through transforming parts of state variables of the system in the corresponding sub-space,we can successfully control chaos by a single time-delayed feedback signal instead of multi-signals in the original system before the transformation.Therefore,the conditions for stabilizing the system can be found theoretically.The numerical simulation results show that this chaos control method is commendably effective and feasible.

Eigenoscillations of the differentially rotating Sun

The general partial differential equation governing linear adiabatic nonradial oscillations in a spherical, differentially and slowly rotating non-magnetic star is derived. This equation describes mainly low-frequency and high-degree g-modes, convective g-modes, rotational Rossby-like vorticity modes, and their mutual interaction for arbitrarily given radial and latitudinal gradients of the rotation rate. Applying to this equation the “traditional approximation” of geophysics results in a separation into radial- and angular-dependent parts of the physical variables, each of which is described by an ordinary differential equation. The angular parts of the eigenfunctions are described by the Laplace tidal equation generalized here to take into account differential rotation. It is shown that there appears a critical latitude in the sphere where the frequencies of eigenmodes coincide with the frequencies of inertial modes. The resonant transformation of the modes into the inertial waves acts as a resonant damping mechanism of the modes. Physically this mechanism is akin to the Alfvén resonance damping mechanism for MHD waves. It applies even if the rotation is rigid. The exact solutions of the Laplace equation for low frequencies and rigid rotation are obtained. The eigenfunctions are expressed by Jacobi polynomials which are polynomials of higher order than the Legendre polynomials for spherical harmonics. In this ideal case there exists only a retrograde wave spectrum. The modes are subdivided into two branches: fast and slow modes. The long fast waves carry energy opposite to the rotation direction, while the shorter slow-mode group velocity is in the azimuthal plane along the direction of rotation. It is shown that the slow modes are concentrated around the equator, while the fast modes are concentrated around the poles. The band of latitude where the mode energy is concentrated is narrow, and the spatial location of these band depends on the wave numbers ().

Multivariate Exponential and Geometric Distributions with Limited Memory

Many connections between geometric and exponential distributions are known. Characterizations and derivations of these distributions often run parallel. Moreover, one kind of distribution can be derived from the other: exponential distributions are limits of sequences of rescaled geometric distributions, and the integral parts of exponential random variables have geometric distributions. The lack of memory property is expressed by functional equations of the form F ( x + u) = F ( x) F ( u) for all ( x, u) ∈ S , where F ( x) = P{ X 1 > x 1, ..., X n > x n }. With S = R 2 n +, the equation expresses a complete lack of memory that is possessed only by distributions with independent exponential marginals. But when S is a proper subset of R 2 n +, the functional equation expresses a partial lack of memory property that in some cases is possessed by a more interesting family of multivariate exponential distributions, as for example, those with exponential minima. In this paper appropriate choices for S and the resulting families of solutions are investigated, together with the associated families of multivariate geometric distributions.

Immitance-Type Three-Term Schur and Levinson Recursions for Quasi-Toeplitz Complex Hermitian Matrices

A comprehensive analysis is made of Schur- and Levinson-type algorithms for Toeplitz and quasi-Toeplitz matrices that have half the number of multiplications and the same number of additions as the classical algorithms. Several results of this type have appeared in the literature under the label “split algorithms.” In this approach the reduction in computation is obtained by a two-step procedure: (i) the first step is a variable (or “recursion-type” ) transformation from the classical (i.e., “scattering” ) variables to a new (so-called, “immitance”) set of variables, which by itself reduces the number of multiplications at the cost of increasing the number of additions; (ii) the second step achieves control of the number of additions by converting the two-term recursions into the lesser known (for discrete orthogonal polynomials) three-term recursions. In the Toeplitz case the new variables turn out to be the odd and even parts of the classical variables, leading to the terminology of split algorithms, bu...

When do singular inputs of an LTI system not cause discontinuities in state variables?

Considers the following question: when do the regular parts of state variables of an LTI system remain continuous functions, regardless of the presence of a singular part in the inputs. A simple test, which gives an answer to the above question, before solving this system, is presented.

Suboptimal control of output‐regulator roblems with incomplete output feedback via moving model

Abstract Output‐regulator problems of a nonlinear multivariable system are considered. The control system is represented by a moving model which is obtained via block pulse functions. Only parts of the output variables are used for feedback control. Since the moving model is set up to approximate any system at every moving step, and the interactions of the output variables exist, incomplete output feedback via the moving model gives satisfactory results. An example of nonisothermal continuous stirred tank reactors is shown to exhibit considerable promise for solving this nonlinear feedback control problem.