Scalar Estimates Research Articles

Effect of number of diffusion encoding directions in neonatal diffusion tensor imaging using Tract-Based Spatial Statistical analysis.

Diffusion tensor imaging (DTI) has been used to study the developing brain in early childhood, infants and in utero studies. In infants, number of used diffusion encoding directions has traditionally been smaller in earlier studies down to the minimum of 6 orthogonal directions. Whereas the more recent studies often involve more directions, number of used directions remain an issue when acquisition time is optimized without compromising on data quality and in retrospective studies. Variability in the number of used directions may introduce bias and uncertainties to the DTI scalar estimates that affect cross-sectional and longitudinal study of the brain. We analysed DTI images of 133 neonates, each data having 54 directions after quality control, to evaluate the effect of number of diffusion weighting directions from 6 to 54 with interval of 6 to the DTI scalars with Tract-Based Spatial Statistics (TBSS) analysis. The TBSS analysis was applied to DTI scalar maps, and the mean region of interest (ROI) values were extracted using JHU atlas. We found significant bias in ROI mean values when only 6 directions were used (positive in fractional anisotropy [FA] and negative in fractional anisotropy [MD], axial diffusivity [AD] and fractional anisotropy [RD]), while when using 24 directions and above, the difference to scalar values calculated from 54 direction DTI was negligible. In repeated measures voxel-wise analysis, notable differences to 54 direction DTI were observed with 6, 12 and 18 directions. DTI measurements from data with at least 24 directions may be used in comparisons with DTI measurements from data with higher numbers of directions.

Message Passing-Aided Joint Data Detection and Estimation of Nonlinear Satellite Channels

Satellite communication is capable of supporting seamless global coverage. However, owing to the reliance on limited-duration solar power, the high power amplifier (HPA) is often driven close to its saturation point, which leads to severe nonlinear distortion in satellite channels. Thus, mitigating the effect of the nonlinear distortion becomes essential for reliable communications. In this article, we propose an efficient joint channel estimation and data detection method based on message passing within the associated factor graph modelling the HPA employed in nonlinear satellite channels. Then, we develop a combined belief propagation and mean field (BP-MF) method to cope with the hard constraints and dense short loops on the factor graph. In particular, the parametric message updating expressions relying on the canonical parameters are derived in the symbol detection part. To alleviate the impact of dense loops, we reformulate the system model into a compact form within the channel estimation part and then reconstruct a loop-free subgraph associated with vector-valued nodes to guarantee convergence. Furthermore, the proposed BP-MF method is also extended to the realistic scenario of having unknown noise variance. To further reduce the computational complexity of the large-scale matrix inversion of channel estimation, the generalized approximate message passing (GAMP) algorithm is employed to decouple the vector of channel coefficient estimation into a series of scalar estimations. Simulation results show that the proposed methods outperform the state-of-the-art benchmarks both in terms of bit error rate performance and channel estimation accuracy.

Применение третичной структуры алгебраической байесовской сети в задаче апостериорного вывода

In the theory of algebraic Bayesian networks, there are algorithms that allow to conduct a global posterior inference using secondary structures. At the same time, building secondary structures implies the use of tertiary structure. Consequently, the question about the separate application of the tertiary structure in the problem of a posterior inference arises. This issue has been considered earlier, but only a general description of the algorithm has been given, and only models with scalar estimates of the probability of truth have been taken into account. In this paper, we present an algorithm that extends the aforementioned algorithm to the possibility of using it in the case of interval estimates. In addition, an important property of an algebraic Bayesian network is acyclicality, and the correctness of the above-mentioned algorithms is ensured only for acyclic networks. Therefore, it is also necessary to be able to check the acyclicity of an algebraic Bayesian network using a tertiary structure. The description of this algorithm is also presented in this paper, it is based on the previously proved theorem that relates the number of knowledge pattern models in the network to the number of non-empty separators and the number of strong restriction connectivity components in acyclic algebraic Bayesian network, as well as the theorem proved in this paper that two knowledge pattern models belong to the same strong restriction connectivity component. For all the developed algorithms, the correctness of their performance is proved, and their time complexity estimation is calculated.

Neural Network-Based Confinement Mode Prediction for Real-Time Disruption Avoidance

Reliable disruption avoidance techniques are critical for the development of safe and economically viable fusion reactors. This incipient need has driven the fusion community to spend substantial effort to develop machine learning (ML) models that aim to assist in this area. One area of concern is a sudden loss in global confinement associated with an unintended back-transition from H-mode confinement to L-mode confinement (“H–L back-transition”), which can sometimes lead to a disruption and increased risk of damage to a reactor-grade fusion device. A recent experiment on DIII-D demonstrated a real-time control method able to steer away from such unwanted back transitions. The present confinement mode of the plasma was inferred in real-time using a temporal convolutional neural network that outputs a continuous scalar estimate suitable for input to a closed-loop controller. This scalar estimate was then regulated to a value consistent with H-mode by adjusting power associated with the ratio of the plasma pressure to magnetic pressure ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta _{N}$ </tex-math></inline-formula> ). The model was trained on hundreds of thousands of discrete time steps of expert-labeled data of periods of H-mode and L-mode confinement, taken from hundreds of DIII-D tokamak discharges. This work presents these experimental results, including the method of collecting expert-labeled H- and L-mode confinement data, and the ML model performance and efficacy on the mode prediction task. In addition, the first application of this tool for H–L back-transition prevention is demonstrated in this experiment. Details are presented on the training of the neural network model, including concerns such as hyperparameter tuning and the network architecture. In addition, the methodology for embedding the neural network into the control system for real-time inference and closed-loop control is discussed.

INFORMATION-ANALYTICAL SYSTEM FOR EVALUATING THE SCIENTIFIC PERFORMANCE OF STRUCTURAL UNITS OF UNIVERSITIES AND RESEARCH INSTITUTES BASED ON THE APPROACH OF CONSTRUCTING COMPLEX INTEGRAL EVALUATION

The article discusses the creation of an information-analytical system for evaluating the scientific performance of structural units of universities and research institutes based on the approach of constructing complex integral evaluation. A model of information technology for evaluating the results of scientific activity is proposed, consisting of four modules: an information collection module, an information storage module, an analytical module, and a module for user interaction and data visualization. The modular structure of the technology will allow expanding and modifying the capabilities of each of the modules independently of the others, as well as increasing the stability and flexibility of the technology. The implementation of this system is performed using microservices technology. A conceptual model of the information system and a structural model of the functioning of the information collection module, as well as a structural model of the information system database, are proposed.&#x0D; It is shown that most of the well-known indices for evaluating the performance of subjects of scientific activity, for example, h-index, g-index, e-index, I-10 index, etc., do not fully take into account information about citation. Therefore, the method for calculating the evaluation of scientific research activities of scientists was proposed, which does not lose information about any citation of the author and publication. This method determines the scalar evaluation of the results of scientific activity, and it is based on determining a few coefficients. The coefficients define one scientist's citation in the publications of other scientists. As a result, assessment is obtained by solving a system of linear algebraic equations that are constructed based on calculated coefficients. Most of the known evaluation approaches have their own calculation features and disadvantages, which are associated with the loss of some information. Therefore, it is not recommended to give preference to one of them. For the purposes of a comprehensive assessment of the productivity of research activities of scientists, the authors proposed a method of vector evaluation of the results and the construction of the integral assessment. This method is based on the construction of vectors and scalar estimates for each scientist in a multidimensional metric space. The dimensionality of the space is determined by the number of calculated scalar estimates. The method is also based on the construction of an ideal point, which consists of scalar estimates that are the best in terms of achieving maximum performance. The assessment of each subject of scientific activity is calculated as the metric distance from the ideal point to the vector of scalar estimates of this subject of scientific activity.

FORMALIZATION OF THE PROBLEM OF TRANSPORT LOGISTICS OPTIMIZATION NETWORKS AT THE STAGE OF REENGINEERING

The subject of research in the article is the process of supporting decision-making in the tasks of optimizing closed logistics networks at the stage of reengineering. The goal of the work is to improve the efficiency of technologies for the automated design of closed logistics networks due to the improvement of mathematical models of multi-criteria problems of reengineering their topological structures. The following tasks are solved in the article: review and analysis of the current state of the problem of supporting decision-making in the tasks of optimizing logistics networks at the stage of their reengineering; decomposition of the problem of optimization of logistics networks at the main stages of their life cycles; selection of a logical scheme of the reengineering process of the logistics network as a territorially distributed object; development of a mathematical model of the general problem of multi-criteria optimization of logistics networks according to indicators of economy, efficiency, reliability and survivability; selection of models for scalar multi-criteria evaluation of reengineering options, taking into account factors that are difficult to formalize, knowledge and experience of the decision-maker. The following methods are used: system approach, theories of systems, theories of usefulness, theories of decision-making, system design, optimization and operations research. Results. Decomposition of the reengineering problem was carried out on the following tasks: determination of the purpose of reengineering and the principles of network reconstruction; network structure optimization; optimization of the topology of network elements; selection of functioning technology; determination of parameters of elements and vehicles; assessment and selection of the best network construction option. The general mathematical model of the multi-criteria task of reengineering the topological structures of centralized three-level logistics networks based on the indicators of costs, cargo delivery time, reliability and survivability has been improved. Universal functions of general utility and utility of local criteria are proposed to obtain scalar estimates for multiple indicators. Exclusion of part of local criteria and restrictions from the general model allows obtaining models of practically all interesting problems of optimization of logistics networks. Conclusions. The developed complex of mathematical models expands the methodological principles of automating the processes of designing logistics networks, allows for the correct reduction of a set of effective options for their construction for the final choice, taking into account factors that are difficult to formalize, the knowledge and experience of designers. The practical use of the proposed complex of mathematical models will reduce the time and capacity complexity of project decision-making support technologies, and due to the use of the proposed options selection procedures, increase their quality based on a number of functional and cost indicators.

Variational Bayesian and Generalized Approximate Message Passing-Based Sparse Bayesian Learning Model for Image Reconstruction

In this paper, we present a novel sparse Bayesian learning (SBL) framework for large-scale image recovery. We formulate variational Bayesian (VB) and generalized approximate message passing (GAMP) into the SBL model (called VGAMP-SBL) to speed up image reconstruction. GAMP can be argued a scalar estimation function described by a set of simple state evolution (SE) equations. From the SE equations, one can accurately predict the values of SBL Params, while it can obtain better reconstruction results without matrix inversion. Moreover, the interaction between data fluctuations and parameter fluctuations is negligible in VB structure, so the maximum marginal likelihood function can be easily obtained, This improves the computation efficiency of our algorithm greatly. Experimental results corroborate these claims.

Robust adaptive learning for attitude control of rigid bodies with initial alignment errors

In the attitude tracking control tasks of rigid-body systems, initial alignment errors are recognized as one of the main factors that hinder the effective controller design and thus the control accuracy improvement. To overcome this difficulty, we develop a novel adaptive iterative learning control (ILC) scheme to achieve the attitude tracking control tasks with high precision for rigid bodies. Owing to the newly employed deadzone mechanism in the parametric updating law, the proposed adaptive ILC scheme is able to deal with initial alignment errors properly without imposing extra requirements to the system dynamics. Moreover, by utilizing the quaternion-based description in the system dynamics, various uncertainties, including external iteration-varying disturbances and unknown system parameters, can be addressed together by a single scalar estimation scheme. The convergence of the proposed adaptive ILC scheme is analyzed rigorously with a Lyapunov-like theory by virtue of newly introducing a modified composite energy function. In addition, to demonstrate its effectiveness, the proposed adaptive ILC method is applied to the attitude tracking of a spacecraft, which shows excellent control performances despite various uncertainties.

The Stability of Linear Diffusion Acceleration Relative to CMFD

Coarse Mesh Finite Difference (CMFD) is a widely-used iterative acceleration method for neutron transport problems in which nonlinear terms are introduced in the derivation of the low-order CMFD diffusion equation. These terms, including the homogenized diffusion coefficient, the current coupling coefficients, and the multiplicative prolongation constant, are subject to numerical instability when a scalar flux estimate becomes sufficiently small or negative. In this paper, we use a suite of contrived problems to demonstrate the susceptibility of CMFD to failure for each of the vulnerable quantities of interest. Our results show that if a scalar flux estimate becomes negative in any portion of phase space, for any iterate, numerical instability can occur. Specifically, the number of outer iterations required for convergence of the CMFD-accelerated transport problem can increase dramatically, or worse, the iteration scheme can diverge. An alternative Linear Diffusion Acceleration (LDA) scheme addresses these issues by explicitly avoiding local nonlinearities. Our numerical results show that the rapid convergence of LDA is unaffected by the very small or negative scalar flux estimates that can adversely affect the performance of CMFD. Therefore, our results demonstrate that LDA is a robust alternative to CMFD for certain sensitive problems in which CMFD can exhibit reduced effectiveness or failure.

Devising a method for identifying the model of multi-criteria expert estimation of alternatives

An approach to constructing mathematical models of individual multicriterial estimation was proposed based on information about the ordering relations established by the expert for a set of alternatives. Structural identification of the estimation model using the additive utility function of alternatives was performed within axiomatics of the multi-attribute utility theory (MAUT). A method of parametric identification of the model based on the ideas of the theory of comparative identification has been developed. To determine the model parameters, it was proposed to use the midpoint method that has resulted in the possibility of obtaining a uniform stable solution of the problem. It was shown that in this case, the problem of parametric identification of the estimation model can be reduced to a standard linear programming problem. The scalar multicriterial estimates of alternatives obtained on the basis of the synthesized mathematical model make it possible to compare them among themselves according to the degree of efficiency and, thus, choose "the best" or rank them. A significant advantage of the proposed approach is the ability to use only non-numerical information about the decisions already made by experts to solve the problem of identifying the model parameters. This enables partial reduction of the degree of expert’s subjective influence on the outcome of decision-making and reduces the cost of the expert estimation process. A method of verification of the estimation model based on the principles of cross-validation has been developed. The results of computer modeling were presented. They confirmed the effectiveness of using the proposed method of parametric model identification to solve problems related to automation of the process of intelligent decision making.

Nonparaxial scalar diffraction theory modifications for improved efficiency estimation

Using the nonparaxial scalar diffraction equations to estimate the diffraction efficiency of multiple orders of a sinusoidal reflection grating, we compare the results of transverse electric (TE) and transverse magnetic (TM) vector wave simulations for a reflective surface on glass. Modifications are presented that enable the nonparaxial scalar solution to approximate simulation results in each order to within 1% to 2% across a 0 to 90 deg range of incidence angles when the diffracted power is predominantly carried in the first several orders, up to the fourth order. The accuracy of simulations in this regime is particularly important for surface scattering where individual spatial frequencies typically have amplitudes much less than a hundred nanometers. A substantial reduction in the error between vector simulations and scalar estimation of diffraction efficiency is demonstrated with the following modifications: (1) an obliquity factor correction for the specular order based on a first-order power conservation criterion, (2) a “cutoff” factor modifying the obliquity factor as the diffracted angle approaches 90 deg for each order, and (3) nonuniform unallocated power distribution near the low-order cutoffs. The second and third modifications are applied to higher diffraction orders for extending the range of applicable grating heights up to 200 nm (peak-to-valley), periods down to twice the wavelength, and slopes up to 0.2 (root mean square).

A COMPARISON OF STOCHASTIC MESH CELL VOLUME COMPUTATION STRATEGIES FOR THE RANDOM RAY METHOD OF NEUTRAL PARTICLE TRANSPORT

The random ray method is a recently developed neutron transport method that can be used to perform efficient full-core, general-purpose, high-fidelity 3D simulations of nuclear reactors. While Tramm et al. have so far documented the new random ray algorithm in several publications, one critical detail has not yet been published: how to best determine the volume of each source region (or cell) of the simulation. As the “true” analytical constructive solid geometry cell volumes are typically not known a priori they must be computed by the application at runtime, which is not straightforward in TRRM as different rays are used each power iteration such that the sampled volume of each cell also changes between iterations. In the present study, we analyze two different on-the-fly stochastic methods for computing the cell volumes and quantify their impacts on the accuracy of scalar flux estimates. We find that the “na¨ıve” stochastic volume estimator (which arises naturally from the derivation of the Method of Characteristics), is highly biased and can result in over 1,000 pcm error in eigenvalue. Conversely, we find that the “simulation averaged” estimator is unbiased and is therefore equivalent to the use of analytical cell volumes even when using a coarse ray density. Thus, the new simulation averaged method is a critical (and as yet undocumented) component of the TRRM algorithm, and is therefore vital information for those in the reactor physics community working to implement random ray solvers of their own.

Back-to-back regression: Disentangling the influence of correlated factors from multivariate observations

Identifying causes solely from observations can be particularly challenging when i) the factors under investigation are difficult to manipulate independently from one-another and ii) observations are high-dimensional. To address this issue, we introduce ‘‘Back-to-Back’’ regression (B2B), a linear method designed to efficiently estimate, from a set of correlated factors, those that most plausibly account for multidimensional observations. First, we prove the consistency of B2B, its links to other linear approaches, and show how it can provide a robust, unbiased and interpretable scalar estimate for each factor. Second, we use a variety of simulated data to show that B2B can outperform forward modeling (“encoding”), backward modeling (“decoding”) as well as cross-decomposition modeling (i.e. canonical correlation analysis and partial least squares) on causal identification when the factors and the observations are not orthogonal. Finally, we apply B2B to a hundred magneto-encephalography recordings and to a hundred functional Magnetic Resonance Imaging recordings acquired while subjects performed a 1 ​h reading task. B2B successfully disentangles the respective contribution of collinear factors such as word length, word frequency in the early visual and late associative cortical responses respectively. B2B compared favorably to other standard techniques on this disentanglement. We discuss how the speed and the generality of B2B sets promising foundations to help identify the causal contributions of covarying factors from high-dimensional observations.

A novel pipeline leveraging surface-based features of small subcortical structures to classify individuals with autism spectrum disorder

Autism spectrum disorder (ASD) is accompanied with widespread impairment in social-emotional functioning. Classification of ASD using sensitive morphological features derived from structural magnetic resonance imaging (MRI) of the brain may help us to better understand ASD-related mechanisms and improve related automatic diagnosis. Previous studies using T1 MRI scans in large heterogeneous ABIDE dataset with typical development (TD) controls reported poor classification accuracies (around 60%). This may because they only considered surface-based morphometry (SBM) as scalar estimates (such as cortical thickness and surface area) and ignored the neighboring intrinsic geometry information among features. In recent years, the shape-related SBM achieves great success in discovering the disease burden and progression of other brain diseases. However, when focusing on local geometry information, its high dimensionality requires careful treatment in its application to machine learning. To address the above challenges, we propose a novel pipeline for ASD classification, which mainly includes the generation of surface-based features, patch-based surface sparse coding and dictionary learning, Max-pooling and ensemble classifiers based on adaptive optimizers. The proposed pipeline may leverage the sensitivity of brain surface morphometry statistics and the efficiency of sparse coding and Max-pooling. By introducing only the surface features of bilateral hippocampus that derived from 364 male subjects with ASD and 381 age-matched TD males, this pipeline outperformed five recent MRI-based ASD classification studies with >80% accuracy in discriminating individuals with ASD from TD controls. Our results suggest shape-related SBM features may further boost the classification performance of MRI between ASD and TD.

Researching of the low-precision SINS research for an unmanned vehicle

A small-sized inertial navigation system (SINS) Gyrolab GL VG 109 is researched. It is shown that this system has low accuracy; therefore it cannot be used to determine the parameters of an unmanned vehicle in an autonomous mode. Correction of the system from the satellite navigation system significantly increases the accuracy of determining the parameters of an unmanned vehicle, but only under conditions of stable signals from the satellite navigation system (SNS). The algorithmic support for the correction facility of the navigation system based on the scalar adaptive estimation algorithm and identification procedure is formed. The use of algorithmic correction of SINS from SNS using an estimation algorithm allows achieving an accuracy that corresponds to systems of the third accuracy class. Keywords inertial navigation system without platform; unmanned vehicle; correction; satellite navigation system; scalar estimation algorithm; scalar identification; analysis of accuracy

Time-Evolving, Spatially Explicit Forecasts of the Northern Gulf of Mexico Hypoxic Zone.

The Mississippi-Atchafalaya River Basin delivers large amounts of freshwater and nutrients to the northern Gulf of Mexico promoting the development of a large hypoxic zone every summer. Statistical and semiempirical models have long been used to provide seasonal forecasts of the mid-summer hypoxic extent using historic time series of spring nutrient load and mid-summer hypoxic extent. These forecasts consist of a scalar estimate of the hypoxic area with uncertainty but do not include spatial distributions or temporal evolution of hypoxic conditions. Three-dimensional (3D) circulation-biogeochemical models of the coastal ocean simulate the temporal evolution of hypoxia in a spatially explicit manner but have not yet been used for seasonal hypoxia forecasting. Here, we present a hybrid method for seasonal, spatially explicit, time-evolving forecasts of the hypoxic zone that combines statistical forecasting with information from a 3D biogeochemical model. The hybrid method uses spring nitrate load and a multiyear (1985-2018) 3D hindcast simulation to produce a seasonal forecast. Validation shows that the method explains up to 76% of the observed year-to-year variability in the hypoxic area. The forecasts suggest that the maximum seasonal extent of hypoxia is reached, on average, on August 13, 2 weeks after the completion of the annual cruise. An analysis of month-to-month variations in hypoxia forecasts due to variability in wind speed and freshwater discharge allows estimates of weather-related uncertainties in the forecast.

Distributed Estimation of Gaussian Correlations

We study a distributed estimation problem in which two remotely located parties, Alice and Bob, observe an unlimited number of i.i.d. samples corresponding to two different parts of a random vector. Alice can send k bits on average to Bob, who in turn wants to estimate the cross-correlation matrix between the two parts of the vector. In the case where the parties observe jointly Gaussian scalar random variables with an unknown correlation ρ, we obtain two constructive and simple unbiased estimators attaining a variance of (1 - ρ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> )/(2k ln 2), which coincides with a known but non-constructive random coding result of Zhang and Berger. We extend our approach to the vector Gaussian case, which has not been treated before, and construct an estimator that is uniformly better than the scalar estimator applied separately to each of the correlations. We then show that the Gaussian performance can essentially be attained even when the distribution is completely unknown. This in particular implies that in the general problem of distributed correlation estimation, the variance can decay at least as O(1/k) with the number of transmitted bits. This behavior, however, is not tight: we give an example of a rich family of distributions for which local samples reveal essentially nothing about the correlations, and where a slightly modified estimator attains a variance of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-Ω(k)</sup> .

ПРОЦЕДУРЫ МНОГОКРИТЕРИАЛЬНОГО ВЫБОРА В УПРАВЛЕНИИ ПЕРСОНАЛОМ

The paper considers the methodology for multi-criteria selection in personnel management and, in particular, in the process of evaluating alternatives in the search and selection of high-quality staff. As well, the systems interaction of criteria in assessment of choosing alternatives noted by M. Sugeno is taken into account. Due to the fact that multiple criteria naturally come as a result of analysis of the operation purpose, in practice not all criteria of an optimization problem equally interact. The purpose of the operation is decomposed into a number of subgoals, where each subgoal induces its own system of criteria, evaluating the degree of its achievement for each alternative choice. Since the criteria serve to assess the degree of achievement of a common subgoal, than, on the one hand, it is natural to expect their interaction. On the other hand, the absence of interaction with the criteria of other subsets can be assumed. In this regard, an individual fuzzy measure of criteria values was introduced to display the interaction of criteria. A general fuzzy measure is constructed as an additive union of private measures. A scalarization indicator structure was chosen to use the constructed fuzzy measure for a reasonable assessment and ranking of alternatives, taking into account the system interaction of the criteria. M. Sugeno proposed a fuzzy integral as a scalarized construction, which allows us to obtain the desired scalar estimate. The disadvantage of this formula, in addition to the rather cumbersome calculation algorithm, is that it directly applies only to normalized criteria. An alternative structure of a scalarizing indicator is introduced to extend the applicability of the method and in cases where “natural” criteria are used. The considered methodology can be recommended as a means of decision support in personnel management.

A reduced muscle model and planar musculoskeletal model fit for the simulation of whole-body movements

Musculoskeletal models are made to reflect the capacities of the human body in general, and often a specific subject in particular. It remains challenging to both model the musculoskeletal system and then fit the modelled muscles to a specific human subject. We present a reduced muscle model, a planar musculoskeletal model, and a fitting method that can be used to find a feasible set of active and passive muscle parameters for a specific subject. At a minimum, the fitting method requires inverse dynamics data of the subject, a scalar estimate of the peak activation reached during the movement, and a plausible initial estimate for the strength and flexibility of that subject. While additional data can be used to result in a more accurate fit, this data is not required for the method solve for a feasible fit. The minimal input requirements of the proposed fitting method make it well suited for subjects who cannot undergo a maximum voluntary contraction trial, or for whom recording electromyographic data is not possible. To evaluate the model and fitting method we adjust the musculoskeletal model so that it can perform an experimentally recorded stoop-lift of a 15 kg box.

О семантометрических свойствах периметра и замыкающей квадратического полигона при оценке точности геодезических засечек

The methods of constructing a quadratic polygon and a circle of standards with internal eccentricity are considered for assessing the accuracy of geodetic notches with any combination of angular and linear equal or unequal measurements. Formulae based on the values of the perimeter and closing of such a polygon are derived. Using these formulas, it is possible to determine the mean square errors of a point simultaneously along any two mutually normal directions and the correlation coefficient between them, the eigenvalues of the covariance matrices, the number of conditionality of the matrix, its trace, determinant and cofactor. Based on the values of the perimeter and the closing quadratic polygon only, you can find almost any scalar accuracy estimates in the form of radii- Vectors, areas and other characteristics of various geometric shapes, such as a circle, ellipse, saddle, and to formulate different conditions for the A-, D-, E-, and I-optimization of serifs in a new way.