Mutually Correlated Research Articles

MULTI-CRITERIA EVALUATION OF THE MULTIFACTOR STOCHASTIC SYSTEMS EFFECTIVENESS

Subject of the research in the article is the evaluation of complex multifactor stochastic systems functioning effectiveness according to a variety of correlated criteria. The problem actuality is determined by the fact that an independent evaluation of system effectiveness for each of the mutually correlated criteria for the system under consideration is not informative. In well-known works in the direction of multiple correlation, a relatively simple problem of estimating the correlation between one resulting parameter and a set of influencing factors is considered, which is not enough for the analysis and management of multicriteria systems. In addition, the known results do not take into account possible significant differences in influencing factors mutual correlation values. Purpose of Work is to develop a methodology for a comprehensive assessment of system effectiveness according to a variety of interrelated criteria. Tasks to be Solved: splitting the set of system parameters into two subsets (parameters determining the effectiveness of system and parameters affecting it), forming additive convolutions of parameters included in subsets, developing a methodology for calculating the multiple correlation coefficient between the components of the selected subsets, developing a method for differentiating a scalar function from a vector argument by this argument. Applied Methods: nonlinear programming, multidimensional correlation analysis, method of differentiation of scalar functions by vector argument. These methods are used for forming and calculating a multiple correlation coefficient between the set of system effectiveness complex criterion components values and its control parameters set values. Results Obtained: proposed methodology provides the possibility of solving the problems of system management, taking into account the revealed relationship between the multi-criteria evaluation of system effectiveness and values of its controlled parameters. At the same time, an important advantage of the obtained result lies in the possibility that arises when using it to take into account the joint (group) influence of control variables on the complex criterion of system efficiency. The developed technology of scalar functions differentiation by vector argument has great practical importance which expands the arsenal of computational mathematics.

Methodology for estimating reception efficiency of mutually correlated or in- phase signals at diversity transmission

The article describes and theoretically substantiates the potential technical capabilities of spatially separated earth stations (ES). When several ESs emit in-phase or mutually pairwise correlated signals in the direction of one received antenna of the object, the total level of the sum of emissions at its output (at the input of the receiving path) may be several times higher than the sum of the powers of these signals. In this regard, the article investigates the influence of the phase difference of the transmitted signals on the value of their total average power at the input of the receiving path. In the case of addition of common-mode signals, a formula is used to calculate in which the power of harmonic radiation is proportional to the square of the sum of the amplitude of the common-mode signals. This paradox is also valid for pairwise cross-correlated signals. The presented technique for evaluatingтthe effectivenessтallows one to establish not only the dependence of the energy ratios on the nonтenergy parameter, but also to determine the number of low-power ESs required to ensureтthe required signal level at the input of the receiving device. The use of the presented technique makes it possible to evaluate the efficiency of receiving a combined high-power signal for a differentтnumber of emitters that form in-phase or pairwise mutual correlation of signals at the receiving point.

Improving the objectivity of sustainability indices by a novel approach for combining contrasting effects: Happy Planet Index revisited

Measuring complex and rather intuitive qualities such as sustainability requires combining many different measures together. These measures often quantify contrasting effects. The resulting composite index then also depends not only on the component indices but also on the way that these have been combined together. An example of such a measure is the Happy Planet Index (HPI) that aggregates information on positive qualities like life-expectancy and human well-being with negative ones like ecological footprint to rank countries according to their sustainability. However, since component indices are often mutually correlated and feature quite different distributions of entities ranked, elaborate rules are used in the process of combination. As a result, the resulting composite index may look somewhat contrived and its rankings may depend heavily on subjective parameters in the combination process. We propose a geometrically motivated parameter-free method for combining indices with contrasting effects together. The method is independent of the number of contrasting indices to be combined and eliminates mutual correlation between component indices by using Singular Value Decomposition (SVD) analysis. As an example of its use, we revisit the Happy Planet Index and demonstrate the impact of adding new component indices to HPI on ranking nations by their sustainability.

Exploiting Joint Sparsity for Pansharpening: The J-SparseFI Algorithm

Recently, sparse signal representation of image patches has been explored to solve the pansharpening problem. Although these proposed sparse-reconstruction-based methods lead to promising results, three issues remained unsolved: 1) high computational cost; 2) no consideration given to the possibility of mutually correlated information in different multispectral channels; and 3) requirement that the spectral responses of the panchromatic (Pan) image and the multispectral image cover the same wavelength range, which is not necessarily valid for most sensors. In this paper, we propose a sophisticated sparse image fusion algorithm, which is named “jointly sparse fusion of images” (J-SparseFI). It is based on the earlier proposed sparse fusion of images (SparseFI) algorithm and overcomes the aforementioned three drawbacks of the existing sparse image fusion algorithms. The computational problem is handled by reducing the problem size and by proposing a fully parallelizable scheme. Moreover, J-SparseFI exploits the possible signal structure correlations between multispectral channels by introducing the joint sparsity model (JSM) and sharpening the highly correlated adjacent multispectral channels together. This is done by exploiting the distributed compressive sensing theory that restricts the solution of an underdetermined system by considering an ensemble of signals being jointly sparse. J-SparseFI also offers a practical solution to overcome spectral range mismatch between the Pan and multispectral images. By means of sensor spectral response and channel mutual correlation analysis, the multispectral channels are assigned to primary groups of joint channels, secondary groups of joint channels, and individual channels. Primary groups of joint channels, individual channels, and secondary groups of joint channels are then reconstructed sequentially, by the JSM or by modified SparseFI, using a dictionary trained from the Pan image or previously reconstructed high-resolution multispectral channels. A recipe of how to choose appropriate algorithm parameters, including the most crucial regularization parameter, is provided. The algorithm is evaluated and validated using WorldView-2-like images that are simulated using very high resolution airborne HySpex hyperspectral imagery and further practically demonstrated using real WorldView-2 images. The algorithm's performance is compared with other state-of-the-art methods. Visual and quantitative analyses demonstrate the high quality of the proposed method. In particular, the analysis of the difference images suggests that J-SparseFI is superior in image resolution recovery.

Analysis of complex system reliability with correlated random vectors

The analysis of reliability of complex engineering systems remains a challenge in the field of reliability. It will be even more difficult if correlated random vectors are involved, which is generally the case as practical engineering systems invariably contain parameters that are mutually correlated. A new method for transforming correlated distributions, involving the Nataf transformation, is proposed that avoids the solution of integral equations; the method is based on the Taylor series expansion of the probability density function (PDF) of a bivariate normal distribution resulting in an explicit polynomial equation of the equivalent correlation coefficient. The required numerical results can be obtained efficiently and accurately.The proposed method for transformation of correlated random vectors is useful for developing a method for system reliability including complex systems with correlated random vectors. Based on the complete system failure process (originally defined as the development process of nonlinearity) and the fourth-moment method, the analysis of system reliability for elastic-plastic material avoids the identification of the potential failure modes of the system and their mutual correlations which are required in the traditional methods. Finally, four examples are presented – two examples to illustrate the potential of the new method for transformation of correlated random vectors, and two examples to illustrate the application of the proposed more effective method for system reliability.

KORELASI ANTARA INTENSI TURNOVER, KEPUASAN KERJA DAN STRES KERJA PADA KARYAWAN PT. X JAMBI

This study was conducted to determine: (1) the correlation between job satisfaction and turnover intention of employees of PT. X Jambi, (2) the correlation between job stress and turnover intention of employees of PT. X Jambi and (3) the correlation between job satisfaction and job stress on employees of PT. X Jambi. The results of the study are (1) between job satisfaction and turnover intention has a negative correlation was weak and not significant, it is shown by the value of the correlation coefficient of -0.295 and opportunities are not mutually correlated 0.065 greater than the maximum terms are correlated by 0.025. It can be concluded between job satisfaction and turnover intention has not had a meaningful relationship, (2) the correlation between job satisfaction and job stress also has a weak positive correlation was not significant, it is shown by the value of the correlation coefficient of 0.024 and opportunities are not mutually correlation of 0.882 is greater than the maximum terms are correlated 0.025, (3) the correlation between job stress and turnover intention has a significant positive relationship, it is shown by the value of the correlation coefficient of 0.381 and opportunities are not mutually correlated by 0,015 less than the requirement maximum mutual correlation of 0.025. It can be concluded between job stress and turnover intention is to have a meaningful relationship.Kata kunci : korelasi, intensi turnover, kepuasan kerja, stres kerja

Motor cortical prediction of EMG: evidence that a kinetic brain-machine interface may be robust across altered movement dynamics

During typical movements, signals related to both the kinematics and kinetics of movement are mutually correlated, and each is correlated to some extent with the discharge of neurons in the primary motor cortex (M1). However, it is well known, if not always appreciated, that causality cannot be inferred from correlations. Although these mutual correlations persist, their nature changes with changing postural or dynamical conditions. Under changing conditions, only signals directly controlled by M1 can be expected to maintain a stable relationship with its discharge. If one were to rely on noncausal correlations for a brain-machine interface, its generalization across conditions would likely suffer. We examined this effect, using multielectrode recordings in M1 as input to linear decoders of both end point kinematics (position and velocity) and proximal limb myoelectric signals (EMG) during reaching. We tested these decoders across tasks that altered either the posture of the limb or the end point forces encountered during movement. Within any given task, the accuracy of the kinematic predictions tended to be somewhat better than the EMG predictions. However, when we used the decoders developed under one task condition to predict the signals recorded under different postural or dynamical conditions, only the EMG decoders consistently generalized well. Our results support the view that M1 discharge is more closely related to kinetic variables like EMG than it is to limb kinematics. These results suggest that brain-machine interface applications using M1 to control kinetic variables may prove to be more successful than the more standard kinematic approach.

Closed-Form Evaluation of the Second-Order Statistical Distribution of the Interferometric Phases in Dual-Baseline SAR Systems

Multichannel inteferometric synthetic aperture radar (InSAR) systems allow to improve the accuracy of the estimation of the height profiles of the observed scenes. Multichannel images can be acquired by using different sensors operating at different frequencies (multifrequency InSAR) or acquiring multiple images with slightly different view angles (multibaseline InSAR). The enhanced accuracy height estimation is obtained exploiting the multichannel interferometric phases and an a priori inaccurate ground profile and requires the knowledge of the joint probability distribution of the multichannel interferometric phases, whose evaluation in a closed form is very complicated and is not found in literature. In this paper, we evaluate the analytical form of the second-order probability density function (pdf) of dual-baseline InSAR phase interferograms, obtained from three mutually correlated interferometric images. The evaluation exploits the Gaussian model for the complex SAR images, takes into account the mutual correlation of all the images, and does not use any approximation. The closed form of the second-order joint pdf can be usefully adopted in statistical digital elevation model (DEM) estimation methods using dual-baseline SAR systems, which commonly use an approximate expression of the joint pdf of measured interferometric phases, given by the product of the marginal pdfs of each phase interferogram, obtained in the assumption of independent interferograms. The effect of this approximation is evaluated by computing the Cramer-Rao lower bounds and the mean square estimation errors obtained using the exact and the approximate model. Presented results represent the basis for the generalization to the case of more than two interferograms.

Mutual correlation of excited potentials of the brainwith the level of special physical training of football-players

Decreased amplitude and increased latency of visual and cognitive excited potentials were revealed in football-players in the stage of prima ry special sports training in the first year of their training process. In the second training year, this amplitude is increased and latency is decreased. Forming special physical preparing of the football-players in the stage of primary sports specialization is mutually correlated with functional changes in the central nervous system which manifested in decreased latent period and increased amplitude of excited potentials of the brain.

Chaos and Residual Correlations in Pinned Disordered Systems

We study, using functional renormalization, two copies of an elastic system pinned by mutually correlated random potentials. Short scale decorrelation depends on a nontrivial boundary layer regime with (possibly multiple) chaos exponents. Large scale mutual displacement correlations behave as [x - x'](2zeta-mu), mu proportional to the difference between Flory (or mean field) and exact roughness exponents zeta. For short range disorder mu>0 and small; e.g., for random bond interfaces mu=5zeta-epsilon, epsilon=4-d, and mu=epsilon{[(2pi)(2)/36]-1} for the one component Bragg glass. Random field (i.e., long range) disorder exhibits finite residual correlations (no chaos mu=0) described by new functional renormalization fixed points. Temperature and dynamic chaos (depinning) are discussed.

Statistical properties of resonance radiation selectively reflected from a cold semi-infinite medium

The problem of the quantum-statistical properties of resonance radiation selectively reflected from unexcited media in the case where the photons are mutually correlated in the incident radiation flux is posed and solved. Allowance for mutual photon correlation precludes, in principle, solving the problem by perturbation methods. A quantum analog of the optical theorem of absorption, which causes an infinite subsequence of Feynman diagrams to vanish, is pointed out. The frequency-angle distribution of the photons in the reflected flux is predicted. The Fresnel formulas are reconstructed for the averaged reflection characteristics. The limits for their applicability in describing the reflection of mutually correlated photons are given. A suppression effect is predicted for the reflection of radiation from a laser source.

Hardware fading simulator for a number of narrowband channels with controllable mutual correlation

A multipath fading simulator with a number of narrowband channels with controllable mutual correlation is described. With this simulator it is possible to test the influence of joint fading statistics of mutually correlated channels on a mobile data communication link with frequency diversity.

A Comparison of Low-Frequency Current Observations off British Columbia with Coastal-Trapped Wave Theory

Abstract Six current-meter mooring were deployed in a line approximately 600 km in length along the continental shelf of British Columbia. Analysis of the low frequency (periods exceeding a day) fluctuations in current for the winter 1981–82 period is discussed. Alongshore currents off Vancouver Island are mutually correlated with time lag less than a day. The region of mutual correlation does not extend north of Vancouver Island, across Queen Charlotte Sound. Coherence is observed between currents south and north of Queen Charlotte Sound only in a frequency band where there is mutual coherence with local wind. A comparison is made between observation and free coastal-trapped wave theory. Off northern Vancouver Island, where the shelf is narrower than off southern Vancouver Island, there is increased vertical shear, a feature of the second coastal-trapped wave mode. A consistency test is applied using the cross spectral matrix of alongshore components of velocity. In the dominant energy-containing frequen...

Adaptive Estimation with Mutually Correlated Training Sequences

The linear least-mean-square error (LMS) estimate of a scalar random variable given an observation of a vector-valued random variable (data) is well know. Computation of the estimate requires knowledge of the data correlation matrix. Algorithms have been proposed by Griffiths [9] and by Widrow [7] for iterative determination of the estimate of each element from a sequence of scalar random variables given an observation of the corresponding element from a sequence of date vectors when the data correlation matrix is not known. These algorithms are easy to implement, require little storage, and are suitable for real-time processing. Past convergence studies of these algorithms have assumed that the data vectors were mutually independent. In this study some asymptotic properties of these and other related algorithms are derived for a sequence of mutually correlated data vectors. A generalized algorithm is defined for analytic purposes. It is demonstrated for this generalized algorithm that excess mean-square error (as defined by Widrow) can be made arbitrarily small for large values of time in the correlated case. The analysis can be applied to a particular estimation scheme of 1) the particular algorithm can be placed in the generalized form, and 2) the given assumptions are satisfied. The analysis of the generalized algorithm requires that the data vectors possess only a few properties; foremost among these are ergodicity and a form of asymptotic independence. This analysis does not assume any particular probability distribution function nor any particular form of mutual correlation for the data vectors.