Classical Deterministic Methods Research Articles

White Matter Fiber Tracking Method with Adaptive Correction of Tracking Direction.

The deterministic fiber tracking method has the advantage of high computational efficiency and good repeatability, making it suitable for the noninvasive estimation of brain structural connectivity in clinical fields. To address the issue of the current classical deterministic method tending to deviate in the tracking direction in the region of crossing fiber region, in this paper, we propose an adaptive correction-based deterministic white matter fiber tracking method, named FTACTD. The proposed FTACTD method can accurately track white matter fibers by adaptively adjusting the deflection direction strategy based on the tensor matrix and the input fiber direction of adjacent voxels. The degree of correction direction changes adaptively according to the shape of the diffusion tensor, mimicking the actual tracking deflection angle and direction. Furthermore, both forward and reverse tracking techniques are employed to track the entire fiber. The effectiveness of the proposed method is validated and quantified using both simulated and real brain datasets. Various indicators such as invalid bundles (IB), valid bundles (VB), invalid connections (IC), no connections (NC), and valid connections (VC) are utilized to assess the performance of the proposed method on simulated data and real diffusion-weighted imaging (DWI) data. The experimental results of the simulated data show that the FTACTD method tracks outperform existing methods, achieving the highest number of VB with a total of 13 bundles. Additionally, it identifies the least number of incorrect fiber bundles, with only 32 bundles identified as wrong. Compared to the FACT method, the FTACTD method reduces the number of NC by 36.38%. In terms of VC, the FTACTD method surpasses even the best performing SD_Stream method among deterministic methods by 1.64%. Extensive in vivo experiments demonstrate the superiority of the proposed method in terms of tracking more accurate and complete fiber paths, resulting in improved continuity. The FTACTD method proposed in this study indicates superior tracking results and provides a methodological basis for the investigating, diagnosis, and treatment of brain disorders associated with white matter fiber deficits and abnormalities.

Analysis of infoware and software for human affective states recognition

The article presents an analytical review of research in the affective computing field. This research direction is a component of artificial intelligence, and it studies methods, algorithms and systems for analyzing human affective states during interactions with other people, computer systems or robots. In the field of data mining, the definition of affect means the manifestation of psychological reactions to an exciting event, which can occur both in the short and long term, and also have different intensity. The affects in this field are divided into 4 types: affective emotions, basic emotions, sentiment and affective disorders. The manifestation of affective states is reflected in verbal data and non-verbal characteristics of behavior: acoustic and linguistic characteristics of speech, facial expressions, gestures and postures of a person. The review provides a comparative analysis of the existing infoware for automatic recognition of a person’s affective states on the example of emotions, sentiment, aggression and depression. The few Russian-language, affective databases are still significantly inferior in volume and quality compared to electronic resources in other world languages. Thus, there is a need to consider a wide range of additional approaches, methods and algorithms used in a limited amount of training and testing data, and set the task of developing new approaches to data augmentation, transferring model learning and adapting foreign-language resources. The article describes the methods of analyzing unimodal visual, acoustic and linguistic information, as well as multimodal approaches for the affective states recognition. A multimodal approach to the automatic affective states analysis makes it possible to increase the accuracy of recognition of the phenomena compared to single-modal solutions. The review notes the trend of modern research that neural network methods are gradually replacing classical deterministic methods through better quality of state recognition and fast processing of large amount of data. The article discusses the methods for affective states analysis. The advantage of multitasking hierarchical approaches is the ability to extract new types of knowledge, including the influence, correlation and interaction of several affective states on each other, which potentially leads to improved recognition quality. The potential requirements for the developed systems for affective states analysis and the main directions of further research are given.

Full and Hybrid Multiscale Lubrication Modeling

The numerical solution for the lubrication of parallel rough surfaces cannot be obtained using the well-known flow factors of Patir and Cheng. Nor can it be determined using homogenization techniques. Is there an alternative, besides a purely long-term deterministic way of solving the problem? The present paper aims at proposing a multiscale approach in order to reduce the computing time, specific to deterministic resolutions, while maintaining good accuracy. The configuration is a parallel rough surface slider, with imposed hydrodynamic operating conditions. The domain consists of independent macro-elements, on which the Reynolds equation is solved. Then, the macro-element boundaries are adjusted to ensure global mass conservation. In its hybrid version, the algorithm replaces some well-chosen macro-elements by simple linear finite elements. The results clearly show the potential of our method. Because the lubrication of each macro-element can be processed independently, the multicore architecture of the processor is exploited. Even if the performance depends on the ratio roughness/height, the computing time is half than for the classical deterministic method, with a few percent errors. The work concludes with some recommendations on the configurations for which the multiscale method is best suited, such as surfaces with short correlation lengths.

On the occurrence of linearly dependent eigenvectors in nonclassical transport calculations

Described here is the occurrence of linearly dependent eigenvectors in the analytical solution of the spectral approximation of the nonclassical transport equation in the discrete ordinates (SN) formulation. To our knowledge, this characteristic does not arise in the analytical solution of the classical SN transport equations. Therefore, classical deterministic analytically-based methods need to be modified when applied to nonclassical transport calculations in order to address this issue.

NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals.

Achieving global goals for sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require instantaneous access to information on food-source quality at key points of agri-food systems. Although laboratory analysis and benchtop NIR spectrometers are regularly used to quantify grain quality, these do not suit all end users, for example, stakeholders in decentralized agri-food chains that are typical in emerging economies. Therefore, we explored benchtop and portable NIR instruments, and the methods that might aid these particular end uses. For this purpose, we generated NIR spectra for 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, and sorghum) with a standard benchtop NIR spectrometer (DS2500, FOSS) and a novel portable NIR-based instrument (HL-EVT5, Hone). We explored classical deterministic methods (via winISI, FOSS), novel machine learning (ML)-driven methods (via Hone Create, Hone), and a convolutional neural network (CNN)-based method for building the calibrations to predict grain protein out of the NIR spectra. All of the tested methods enabled us to build relevant calibrations out of both types of spectra (i.e., R2 ≥ 0.90, RMSE ≤ 0.91, RPD ≥ 3.08). Generally, the calibration methods integrating the ML techniques tended to enhance the prediction capacity of the model. We also documented that the prediction of grain protein content based on the NIR spectra generated using the novel portable instrument (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the presented findings lay the foundations for the expanded use of NIR spectroscopy in agricultural research, development, and trade.

Evaluation of the classical statistical, deterministic and geostatistical interpolation methods for estimating the groundwater level

Proper management of groundwater resources depends on identifying, modeling and predicting the level of groundwater in the plains for long-term planning and optimal use of the potential of water. To achieve continuous and integrated maps and predict unknown values, interpolation methods can be used. In this research, the classic statistical interpolation methods (including nearest neighbor, natural neighbor, moving average, and triangulation with linear interpolation), deterministic interpolation methods (including inverse distance to a power, radial basis functions and local polynomial) and geostatistical interpolation method (kriging) were used to estimate the groundwater level of Naqadeh plain in the northwest of Iran in the period of 2001–2016. The cross-validation technique was applied to evaluate the accuracy of the various methods, and three indices—the determination coefficient (R2), the root mean squared error and mean absolute error—were used to compare the interpolation methods. The results of geostatistical analysis indicated that the groundwater level is regionalized variable and there is a high spatial structure ratio between groundwater level data. The best-experimented variogram is a Gaussian model with a correlation coefficient of 0.981. According to the results of cross-validation method, the geostatistical interpolation method with the highest accuracy and the minimum error and the classic statistical interpolation methods with the least accuracy and maximum error were introduced as the optimal and inappropriate methods, respectively.

Exploring Hilbert space on a budget: Novel benchmark set and performance metric for testing electronic structure methods in the regime of strong correlation.

This work explores the ability of classical electronic structure methods to efficiently represent (compress) the information content of full configuration interaction (FCI) wave functions. We introduce a benchmark set of four hydrogen model systems of different dimensionalities and distinctive electronic structures: a 1D chain, a 1D ring, a 2D triangular lattice, and a 3D close-packed pyramid. To assess the ability of a computational method to produce accurate and compact wave functions, we introduce the accuracy volume, a metric that measures the number of variational parameters necessary to achieve a target energy error. Using this metric and the hydrogen models, we examine the performance of three classical deterministic methods: (i) selected configuration interaction (sCI) realized both via an a posteriori (ap-sCI) and variational selection of the most important determinants, (ii) an a posteriori singular value decomposition (SVD) of the FCI tensor (SVD-FCI), and (iii) the matrix product state representation obtained via the density matrix renormalization group (DMRG). We find that the DMRG generally gives the most efficient wave function representation for all systems, particularly in the 1D chain with a localized basis. For the 2D and 3D systems, all methods (except DMRG) perform best with a delocalized basis, and the efficiency of sCI and SVD-FCI is closer to that of DMRG. For larger analogs of the models, the DMRG consistently requires the fewest parameters but still scales exponentially in 2D and 3D systems, and the performance of SVD-FCI is essentially equivalent to that of ap-sCI.

Performance Analysis of APSO and Firefly Algorithm for Short Term Optimal Scheduling of Multi-Generation Hybrid Energy System

This paper presents a modified and novel form of the conventional short-term hydrothermal scheduling problem by incorporating the effects of adding the photovoltaic energy source to the conventional grid. A photovoltaic energy source is intermittent in nature, therefore, to determine the optimal power contribution of the photovoltaic source towards the economic dispatch problem, a detailed strategy is presented in this paper. The proposed design method includes the forecasting of the photovoltaic system’s parameters using the Auto-Regressive Integrated Moving Average (ARIMA) model. The analytical model is developed based on the fractional integral polynomials for studying the characteristics of the single photovoltaic module. The optimization of power allocation in the system consisting of conventional and non-conventional energy sources is highly non-linear and classical deterministic methods can not be guaranteed to determine the optimal solution for economic power dispatch. The global optima of such non-linear and non-convex problems can be determined using swarm-based intelligence techniques. This paper presents accelerated particle swarm optimization and the firefly algorithm to determine a solution for short-term non-linear scheduling problems. The multiple test cases have been presented to demonstrate the effectiveness of the proposed solution over classical methods. The overall generation cost of the selected hybrid system is reduced using the proposed methods while meeting the generation constraints of each energy source. Moreover, due to the stochastic nature of the meta-heuristic techniques, a comprehensive statistical comparison, based on the independent T-test results, is also presented to highlight the algorithm which performs better for selected scheduling problems. It has been demonstrated that accelerated particle swarm optimization gives lower mean generation cost of the system whereas the execution time of the firefly algorithm is better compared to its counterpart.

Nonlinear dynamic analysis method for large-scale single-layer lattice domes with uncertain-but-bounded parameters

Currently, nonlinear dynamic analysis for large-scale single-layer domes is commonly performed using deterministic numerical methods. However, in practical engineering cases, complex large-scale single-layer domes have many uncertain parameters that cannot be considered using deterministic methods. Therefore, there is a growing awareness that classical deterministic methods need to be extended towards the introduction of the uncertain aspects in dynamic analysis, and a non-deterministic analysis method for large-scale spatial structures is required. In this paper, a new method is presented by introducing uncertainties into the nonlinear dynamic analysis for large-scale single-layer lattice domes. The method accounts for uncertainties in material properties, structural imperfections, loads, and damping with bounds. The focus is on the treatment of uncertainty in damping and the adopted geometric shape, which is different from that of conventional approaches. Finite element dynamic analyses for sample structures with multiple sources of uncertainty subjected to dynamic loads are performed. Results show that the variability of the variables with an associated uncertainty imposes significant negative effects on the dynamic properties, dynamic demands, and safety of a dome. Uncertain damping in a structure plays the most important role in determining structural performance. The numerical results reveal the differences between conventional analysis methods with deterministic parameters used in previous practical applications and the uncertain analysis method. Finally, a parametric study is performed, and the impacts of sample size on statistical dynamic demands, single uncertain source on structural failure, and single uncertain source on damping coefficients are discussed.

Fuzzy approach in modeling static and fatigue strength of composite materials and structures

For composite materials and structures static strength, fatigue damage and durability demonstrate a scatter factor of results larger than for isotropic materials. To characterize it the fuzzy set approach is proposed. Two different mechanical descriptions of fatigue life are used in order to describe the uncertainty and randomness of parameters characterizing the fatigue damage and finally the fatigue durability. The theoretical predictions representing the lower and upper bounds of a fatigue life are compared with experimental data. In general, the present analysis shows that the fuzzy set description allows us to take into account much more parameters than classical deterministic or statistical methods. The scatter of the results is significantly dependent on the form of the used approximations of fatigue lives.

An Overview on Stochastic Global Optimization and its Multi-Domain Applications

Optimization is of great interest and it has widespread applications in engineering and science. It has become a major technology contributor to the growth of industry. It is extensively used in solving a wide variety of problems in design, operation, and analysis of engineering and technological processes. Optimization of large-scale problems pose difficulties concerning to dimensionality, differentiability, multimodality and nonlinearity in objective functions and constraints. In order to overcome such difficulties, there has been a rapidly growing interest in advanced optimization algorithms. Stochastic and evolutionary optimization algorithms are increasingly used to solve challenging optimization problems. These algorithms include genetic algorithm, simulated annealing, differential evolution, ant colony optimization, tabu search, particle swarm optimization, artificial bee colony algorithm, and cuckoo search algorithm. These algorithms are typically inspired by some phenomena from nature and they are robust. These algorithms do not require any gradient information and are even suitable to solve discrete optimization problems. These methods are extensively used to solve the optimization problems concerning to systems that are highly nonlinear, high dimensional, and noisy or for solving problems that are not easily solved by classical deterministic methods of optimization.

Entropy As an Assessment Factor of the Current State of Vessel When Approaching an Object of Maneuver

The paper describes a method for assessing the current state of a vessel as an element of a complex dynamic system when approaching a mobile object. As an evaluation criterion, it is proposed to use the maximum value of the entropy of the “vessel – the object of maneuver” system with the equality of probabilities of possible states of this system. The relevance of the study is due to the need to improve the classical methods of analysis and solving the problems of the vessel’s approach to the object of maneuver. Classical deterministic methods often prove ineffective if the object of maneuver performs unpredictable random actions in relation to the maneuvering vessel. Such objects include, for example, mobile shoals of fish or other vessels operated by inexperienced navigators. The purpose of the study is to justify the method of assessing the current state of the vessel when approaching the object of maneuver on the calculated deterministic trajectory. The solution of the control example confirmed the possibility of using the entropy of the “vessel – the object of maneuver” system as a universal estimation parameter of vessel’s current state when approaching an object of maneuver. The materials of the paper can be of practical and theoretical interest for specialists in the field of automation of ship movement control, navigators of merchant and fishing ships.

Forward and inverse nonlinear heat transfer analysis for optimization of a constructal T-shape fin under dry and wet conditions

This work deals with direct and inverse analysis of T-shaped dry and wet fins. Direct analysis is done to study heat transfer performance, whereas inverse analysis is performed to simultaneously estimate five optimum geometric parameters satisfying a prescribed fin volume. The modified differential evolution (MDE) search algorithm is used to explore the required geometrical parameters pertaining to the stem and the flange parts of the fin. In the present MDE, the mutant is generated using five distinct vectors instead of three as conventionally practiced. Due to the existence of multiple solutions, the selection criterion is based upon the fulfilment of different performance parameters. These involve individual maximization of heat transfer rate, fin efficiency and fin effectiveness. Since the application of the differential transformation method (DTM) is not yet demonstrated for nonlinear heat transfer analysis of T-shaped wet fins, thus, for generating the heat transfer parameters using the inversely estimated geometric parameters, a forward approach based on the DTM is used here. Parametric variations along with necessary validations of the direct method are presented. Furthermore, a comparison of the present MDE search-based inverse algorithm is done with a classical gradient-based optimization technique. It can be highlighted from the present study that only when at-least three geometrical parameters are known, then the classical method successfully yields heat transfer performance parameters comparable with the MDE algorithm. From the optimization study, it is found that a particular value of fin performance (heat transfer rate, efficiency, effectiveness) can be acquired with various values of surface area and even at a given surface area, different fin performances can be obtained. However, a single and distinct operating point is revealed where the performance index of the fin is maximized. For ensuring maximum possible performance from constructal T-shape wet fins, it is recommended that for the present type of problem, the present stochastic optimization method such as MDE must be used where the classical deterministic methods suffer from inherent limitations on multi-variables optimization.

Usage of Optimization Techniques in Civil Engineering During the Last Two Decades

In the real world, there are many problems in which it is desirable to optimize one or more objective functions at the same time. These are known as single and multi-objective optimization problems respectively and continuous research is being conducted in this field and nature inspired heuristic optimization methods (also called advanced optimization algorithms) are proving to be better than the classical deterministic methods and thus are widely used.

Моделювання технічних резервів страхової компанії

In the modern rapidly evolving society, the science and the business are facing new needs and challenges constantly. The insurance industry and its mathematical foundation, the actuarial science, are not exceptions. Currently, the greatest challenge that the insurance system has to cope with is the issue of the new international financial standard that affects the calculation of reserves among other things. So far, insurers have mainly used common classical deterministic methods. However, the new standard emphasizes the necessity of the realistic prognosis that is best achieved with stochastic modelling tools since deterministic models do not represent the uncertainty and the random nature of future possible losses. This article considers the advantage of using stochastic modelling for reserve calculation in comparison to the deterministic approach. The article consists of five sections. In the first section, we briefly present the technique that lies in the basis of technical reserves calculation. The second section is devoted to such deterministic methods of reserve calculation as the Bornhuetter-Ferguson method and the chain-ladder method. In the third section, we consider modifications of two stochastic models – the Mack method and the bootstrapping technique. The fourth section considers the adjustment of reserves for the time value of money and inflation. In the fifth section, the results of modelling in the programming language R are presented.

Bundle-specific tractography with incorporated anatomical and orientational priors

Anatomical white matter bundles vary in shape, size, length, and complexity, making diffusion MRI tractography reconstruction of some bundles more difficult than others. As a result, bundles reconstruction often suffers from a poor spatial extent recovery. To fill-up the white matter volume as much and as best as possible, millions of streamlines can be generated and filtering techniques applied to address this issue. However, well-known problems and biases are introduced such as the creation of a large number of false positives and over-representation of easy-to-track parts of bundles and under-representation of hard-to-track.To address these challenges, we developed a Bundle-Specific Tractography (BST) algorithm. It incorporates anatomical and orientational prior knowledge during the process of streamline tracing to increase reproducibility, sensitivity, specificity and efficiency when reconstructing certain bundles of interest. BST outperforms classical deterministic, probabilistic, and global tractography methods. The increase in anatomically plausible streamlines, with larger spatial coverage, helps to accurately represent the full shape of bundles, which could greatly enhance and robustify tract-based and connectivity-based neuroimaging studies.

Balancing principle in supervised learning for a general regularization scheme

We discuss the problem of parameter choice in learning algorithms generated by a general regularization scheme. Such a scheme covers well-known algorithms as regularized least squares and gradient descent learning. It is known that in contrast to classical deterministic regularization methods, the performance of regularized learning algorithms is influenced not only by the smoothness of a target function, but also by the capacity of a space, where regularization is performed. In the infinite dimensional case the latter one is usually measured in terms of the effective dimension. In the context of supervised learning both the smoothness and effective dimension are intrinsically unknown a priori . Therefore we are interested in a posteriori regularization parameter choice, and we propose a new form of the balancing principle. An advantage of this strategy over the known rules such as cross-validation based adaptation is that it does not require any data splitting and allows the use of all available labeled data in the construction of regularized approximants. We provide the analysis of the proposed rule and demonstrate its advantage in simulations. • For smooth kernels, a power decay of effective dimensions may not be appropriate. • Learning rates under general smoothness assumptions on target functions. • Optimal learning rates in all involved norms based on a novel bound. • An adaptive parameter choice which uses the whole dataset without data splitting.

A scheduling methodology for dealing with uncertainty in construction projects

PurposeThe purpose of this paper is to address the problem of scheduling under uncertainty in construction projects. The existing methods for determining a project schedule are based on assumption of complete knowledge of project parameters; but in reality there is uncertainty in construction projects, deriving from a multitude of context‐dependent sources and often provided as outcome of a risk analysis process. Thus, classical deterministic analysis might provide a schedule which is not sufficiently protected against possible disruptions.Design/methodology/approachA quantitative methodology is developed for planning construction projects under uncertainty aimed at determining a reliable resource feasible project schedule by taking into account the available probabilistic information to produce solutions that are less sensitive to perturbations that occur on line. The methodology relies on a computer‐supported system that allows to identify, analyze and quantify the schedule reliability and the impact of possible disruptions on the duration of the project.FindingsIt is found that the proposed methodology can exploit more information about the uncertain parameters than the commonly‐used deterministic method, and it provides an improved understanding of the schedule reliability in presence of uncertainty. The schedule generated with a classical deterministic method sets a completely unrealistic planned project delivery date of about 1,250 days, with a probability around 50 per cent to be exceeded. This behavior can be very unsatisfactory for construction projects for which high penalties are usually associated to heavy due date violations.Originality/valueThis paper presents an approach for robust scheduling of construction project problem under uncertainty. We provide a tool able to support managers in developing a workable and realistic project schedule to be used as a guideline for project control and monitoring.

An augmented Kalman filter for force identification in structural dynamics

An augmented Kalman filter for force identification in structural dynamics is developed, in which the unknown forces are included in the state vector and estimated in conjunction with the states. Noise is modeled as a stochastic process and is assumed to be present not only on the measurements, but also on the state variables, thus accounting to some extent for modeling errors. This distinguishes the proposed technique from purely deterministic methods for force identification in which no errors are assumed on the states. To analyze the effect hereof on the quality of the identification, the results obtained with a commonly used recursive least-squares method, the Dynamic Programming algorithm, are compared to those obtained using the augmented filter in a laboratory experiment on an instrumented steel beam. It is shown how, in the collocated case, more accurate results can be obtained with the augmented filter due to its incorporation of modeling errors. In the non-collocated case, however, better solutions are produced by classical deterministic methods as Dynamic Programming in which only the forces are estimated, and not the states as well.

Monte Carlo estimation of thermal radiation from wildland fires

As an alternative to the classical deterministic methods, a Monte Carlo method is proposed for the estimation of the radiant heat flux from wildland fires, and thus of fire-safe distances. The solid flame model is used, in which the flame is regarded as a uniformly-radiating surface with a simple geometry. Case studies are presented, ranging from the relatively simple case of planar or cylindrical flames to the case of the more complex geometries of irregular flame fronts where the strategy consists in approximating the front as a collection of individual solid flames. Model results are in good agreement with available data. An attempt is made to estimate the radiant heat flux ahead of a fire front spreading on a heterogeneous landscape. Finally the model is applied to determine the defensible space around a structure in which combustible vegetation has been totally cleared in the aim of reducing the wildfire threat to homes.