Gotowa bibliografia na temat „Generalized Vector Explicit Guidance”

Wave gliders as innovative autonomous ocean-going vehicles harvest the abundant marine natural energy for persistent ocean environment monitoring. This paper analyzes wave glider’s operating mechanism and builds the dynamic model of wave glider. By simplifying the model into 3 DOS in longitudinal plane and selecting three generalized velocity, the kinematic equations and the generalized force can be confirmed. Then, based on the equation of Kane vector operation modeling method, the explicit kinetic model of wave glider is presented. According to simulation study of the kinetic model, the relationship between the motion state of wave glider and the wave motion are indicated. The study provides theoretical guidance and reference for wave glider design.

This paper presents an approach to compute the optimal time-to-go and final velocity magnitude in the Generalized Explicit (GENEX) guidance. Time-to-go and final velocity magnitude are two critical input parameters in GENEX guidance implementation. Optimal time-to-go selects that optimal solution which yields less cost compared to the costs yielded by other optimal solutions. In addition to it, the input of realistic final velocity lowers the cost further. These developments relax the existing limitations of GENEX, thereby making this optimal guidance law more optimal, effective and generic.

In information retrieval, it is common to model index terms and documents as vectors in a suitably defined vector space. The main difficulty with this approach is that the explicit representation of term vectors is not known a priori. For this reason, the vector space model adopted by Salton for the SMART system treats the terms as a set of orthogonal vectors. In such a model it is often necessary to adopt a separate, corrective procedure to take into account the correlations between terms. In this paper, we propose a systematic method (the generalized vector space model) to compute term correlations directly from automatic indexing scheme. We also demonstrate how such correlations can be included with minimal modification in the existing vector based information retrieval systems.

A new generalised Taylor-like explicit method for stiff ordinary differential equations (ODEs) is proposed. The algorithm is presented in its component and vector forms. The error and stability analysis of the method are developed showing that it has an arbitrary high order of convergence and the L-stability property. Moreover, it is verified that several integration schemes are special cases of the new general form. The method is applied on stiff problems and the numerical solutions are compared with those of the classical Taylor-like integration schemes. The results show that the proposed method is accurate and overcomes the shortcoming of the classical Taylor-like schemes in their component and vector forms.

In this paper, we study the variational problem associated to support vector regression in Banach function spaces. Using the Fenchel–Rockafellar duality theory, we give an explicit formulation of the dual problem as well as of the related optimality conditions. Moreover, we provide a new computational framework for solving the problem which relies on a tensor-kernel representation. This analysis overcomes the typical difficulties connected to learning in Banach spaces. We finally present a large class of tensor-kernels to which our theory fully applies: power series tensor kernels. This type of kernels describes Banach spaces of analytic functions and includes generalizations of the exponential and polynomial kernels as well as, in the complex case, generalizations of the Szegö and Bergman kernels.

We obtain the (N+1)-th flow of the generalized (N–1)-KdV hierarchy from self-dual Yang-Mills equations with gauge group SL(N) and space-time signature (2, 2). The dimensional reduction is performed by using a pair of orthogonal Killing vector fields (one time-like and one null) and we generalize previous results by Mason and Sparling to N≥2. We illustrate our method with explicit examples and determine the form of the self-dual solutions for N=2, 3, 4. Applications of this formalism and its possible generalizations are also discussed briefly.

The two-component vector breather solution of the modified Benjamin–Bona–Mahony equation is considered. By means of the generalized perturbation reduction method, the equation is reduced to the coupled nonlinear Schrodinger equations for auxiliary functions. Explicit analytical expressions for the profile and parameters of the two-component vector breather, the components of which oscillating with the sum and difference of the frequencies and wave numbers are obtained.

In this paper, we study generalized quasi-Einstein manifolds ( M n, g, V, λ) satisfying certain geometric conditions on its potential vector field V whenever it is harmonic, conformal, and parallel. First, we construct some integral formulas and obtain some triviality results. Then, we find some necessary conditions to construct a quasi-Einstein structure on ( M n, g, V, λ). Moreover, we prove that for any generalized Ricci soliton [Formula: see text], where [Formula: see text] is a generalized Robertson–Walker spacetime metric and the potential field [Formula: see text] is conformal, [Formula: see text] can be considered as the model of perfect fluids in general relativity. Moreover, the fiber ( M, g) also satisfies the quasi-Einstein metric condition. Therefore, the state equation of [Formula: see text] is presented. We also construct some explicit examples of generalized quasi-Einstein metrics by using a four-dimensional Walker metric.

In this thesis, optimal control theory has been applied to design constrained terminal guidance algorithms for interceptor to destroy incoming high speed ballistic targets. There exist several optimal guidance algorithms which are developed to achieve different mission objectives. Two types of terminal angle-constrained optimal guidance laws are studied here. The first one has a closed form expression which is obtained by minimizing a weighted control energy considering linear state dynamics with states as zero-effort miss (ZEM) and zero-effort velocity miss (ZEVM). This guidance law is very popular due to its simple geometric interpretation and easy mechanization and is known as ‘Generalized Vector Explicit Guidance’ (GENEX). The second one has a closed from control update expression obtained by minimizing control energy considering nonlinear dynamic model of the interceptor. This guidance law is predictive in nature, converges in little iterations, and convergence time is very small. It is very accurate in achieving the terminal constraints. It is known as ‘Model Predictive Static Programming’ (MPSP). In GENEX formulation, a fixed final time (i.e. time-to-go) optimal control problem has been solved to obtain a closed form guidance law. It is found that, GENEX guidance depends on time-to-go and final velocity. Selection of a different final time the problem becomes different, which leads to a different solution. Hence, there exists an optimal final time for which the cost function will become minimum among all control effort minimizing solutions, leading to the minimum control effort solution. In this thesis, the scope of finding optimal final time is explored and a closed form expression of time-to-go is obtained, which finds minimum of all minimum cost. In addition to it, the magnitude of the final velocity vector is obtained by deriving an analytic expression using nonlinear dynamics of the interceptor. It has been found that use of optimal time-to-go and value of final velocity has reduced the cost, which makes GENEX more efficient and cost effective. MPSP is a numerical optimization algorithm which solves an optimal control problem with terminal constraints. The dimension of optimization problem is same as the number of discrete time instants. In this thesis, an attempt has been made to reformulate MPSP to reduce the number of optimization variables by representing the control variable as weighted sum of basis functions. This variety of MPSP is named as ‘Quasi Spectral MPSP’ (QS-MPSP). It has been found that, QS-MPSP formulation greatly reduces dimension of the optimization problem and consequently convergence time of the algorithm also reduced. A successful interception requires the target to be confined all the time within the field of view of the seeker antenna. For this reason, the ‘Look Angle’ (i.e. the angle between the body ‘x’ axis and Line of Sight) needs to be lesser than field of view angle of seeker antenna. If the angle of attack and side-slip are assumed to be small then look angle can be approximated as lead angle (defined as the angle between the velocity and LOS vector). Therefore, the lead angle should be lesser than the field of view angle during the terminal phase of an engagement. Hence, it can be considered as a path constraint optimal control problem. There exist direct and indirect methods to solve constrained optimal control problems. In this thesis the constrained optimal control problem is solved by a new technique which is developed in QS-MPSP frame work and is named as ‘Constrained QS-MPSP. The idea behind this technique is to express the constraints on states in terms of inequality constraints on coefficients which are used to represent the control expression in spectral form. Constraints on control is taken care of by deriving separate set of inequality constraints on the same coefficients. Values of these coefficients are obtained by solving a static optimization problem in which a quadratic function of these coefficients are minimized subject to a set of inequality constraints on coefficients. It can be mentioned that; the static optimization problem is solved using interior point method which is a well-established algorithm and capable of solving large dimension nonlinear programming problem with inequality constraints. The proposed technique is applied to missile guidance problem. It is found that, the algorithm converges in a few iterations and convergence time is very small. For this reason, this algorithm can be implemented in real time on an on-board missile embedded processor. The guidance commands (lateral accelerations) generated by proposed Constrained QS-MPSP, are validated using Six degree-of-freedom model of interceptor. The guidance commands are converted to control surface defections using a two loop Dynamic Inversion controller. It has been found that, the engagement happened, and lead angle constraint is satisfied.

This paper introduces a novel approach for reliability assessment with dependent variables. In this work, the boundary of the failure domain, for a computational problem with expensive function evaluations, is approximated using a Support Vector Machine and an adaptive sampling scheme. The approximation is sequentially refined using a new adaptive sampling scheme referred to as generalized “max-min”. This technique efficiently targets high probability density regions of the random space. This is achieved by modifying an adaptive sampling scheme originally tailored for deterministic spaces (Explicit Space Design Decomposition). In particular, the approach can handle any joint probability density function, even if the variables are dependent. In the latter case, the joint distribution might be obtained from copula. In addition, uncertainty on the probability of failure estimate are estimated using bootstrapping. A bootstrapped coefficient of variation of the probability of failure is used as an estimate of the true error to determine convergence. The proposed method is then applied to analytical examples and a beam bending reliability assessment using copulas.

Abstract Despite the increasing number of collaborative robots in human-centered manufacturing, currently, industrial robots are still largely preprogrammed with very little autonomous features. In this context, it is paramount that the robot planning and motion generation strategies are able to account for changes in production line in a timely and easy-to-implement fashion. The same requirements are also valid for service robotics in unstructured environments where an explicit definition of a task and the underlying path and constraints are often hard to characterize. In this regard, this paper presents a real-time point-to-point kinematic task-space planner based on screw interpolation that implicitly follows the underlying geometric constraints from a user demonstration. We demonstrate through example scenarios that implicit task constraints in a single user demonstration can be captured in our approach. It is important to highlight that the proposed planner does not learn a trajectory or intends to imitate a human trajectory, but rather explores the geometric features throughout a one-time guidance and extend such features as constraints in a generalized path generator. In this sense, the framework allows for generalization of initial and final configurations, it accommodates path disturbances, and it is agnostic to the robot being used. We evaluate our approach on the 7 DOF Baxter robot on a multitude of common tasks and also show generalization ability of our method with respect to different conditions.

Abstract This paper presents a complete and general machine-learning assisted optimization framework for the Generalized k-ω (GEKO) turbulence model based on experimental measurements. The optimization framework is applied to the use case of a turbocharger radial compressor for three different operating conditions including design and surge conditions. The first optimization of the GEKO turbulence model is performed w.r.t measured global thermodynamic quantities, including the isentropic efficiency and pressure ratio. A second optimization is performed w.r.t local experimental flow quantities, i.e. wall pressure distribution along the diffuser vane. The testing errors of the machine-learning models are compared and analyzed, and the best performing algorithms are Support Vector Regression (SVR) and Artificial Neural Network (ANN) for modeling the response surface of the GEKO parameters, based on which the turbulence model is optimized by Genetic Algorithm (GA). The aforementioned 2 optimizations result in an equivalent GEKO parameter set, where the optimized GEKO model agrees well with the measurements and the more computationally expensive Explicit-Algebraic Reynold Stress Model (EARSM), and outperforms the Shear Stress Transport (SST) in terms of relative error, and the prediction of onset of surge and flow separation. The optimized GEKO model also generalizes well to different operating conditions as well as another radial compressor with a different diffuser geometry. In addition, a data-based approach is used to quantify the influence of the six GEKO parameters on different flow phenomena in the radial compressor. The result of the data-based approach aligns well with heuristic loss mechanism in turbomachinery.

A form of supervised machine learning was applied to highly resolved large-eddy simulation (LES) data to develop non linear turbulence stress and heat flux closures with increased prediction accuracy for trailing-edge cooling slot cases. The LES data were generated for a thick and a thin trailing-edge slot and shown to agree well with experimental data, thus providing suitable training data for model development. A Gene Expression Programming (GEP) based algorithm was used to symbolically regress novel nonlinear Explicit Algebraic Stress Models (EASM) and heat-flux closures based on either the gradient diffusion or the generalized gradient diffusion approaches. Following a-priori assessment, the new models were used for steady RANS calculations of both thin and thick trailing-edge slot geometries, testing their performance and robustness. Overall, the best agreement with LES data was found when training the RANS model in the near wall region where high levels of anisotropy exist and using the mean squared error of the anisotropy tensor as cost function. In the case of the thin lip geometry, combining an improved EASM model with the standard eddy-diffusivity model predicted the adiabatic wall effectiveness in good agreement with the LES and experimental data. Crucially, the obtained model was also applied to different blowing ratios of the thin lip geometry and a significant improvement in the predictive accuracy of adiabatic wall effectiveness was observed for those cases not previously seen in the training process. For the thick lip case the match with reference values deteriorated due to the presence of large-scale, relative to the slot height, vortex shedding. The machine-learning algorithm was therefore also used to ‘learn’ an appropriate closure for the turbulent heat flux vector. The constructed scalar flux model, in conjunction with a trained RANS model, was found to have the capability to further improve the prediction of the adiabatic wall effectiveness.

The development is presented of an analytical model for the prediction of the stochastic nonlinear wave loads on the support structure of bottom mounted and floating offshore wind turbines. Explicit expressions are derived for the time-domain nonlinear exciting forces in a seastate with significant wave height comparable to the diameter of the support structure based on the fluid impulse theory. The method is validated against experimental measurements with good agreement. The higher order moments of the nonlinear load are evaluated from simulated force records and the derivation of analytical expressions for the nonlinear load statistics for their efficient use in design is addressed. The identification of the inertia and drag coefficients of a generalized nonlinear wave load model trained against experiments using Support Vector Machine learning algorithms is discussed.

Change detection is a widely adopted technique in remote sense imagery (RSI) analysis in the discovery of long-term geomorphic evolution. To highlight the areas of semantic changes, previous effort mostly pays attention to learning representative feature descriptors of a single image, while the difference information is either modeled with simple difference operations or implicitly embedded via feature interactions. Nevertheless, such difference modeling can be noisy since it suffers from non-semantic changes and lacks explicit guidance from image content or context. In this paper, we revisit the importance of feature difference for change detection in RSI, and propose a series of operations to fully exploit the difference information: Alignment, Perturbation and Decoupling (APD). Firstly, alignment leverages contextual similarity to compensate for the non-semantic difference in feature space. Next, a difference module trained with semantic-wise perturbation is adopted to learn more generalized change estimators, which reversely bootstraps feature extraction and prediction. Finally, a decoupled dual-decoder structure is designed to predict semantic changes in both content-aware and content-agnostic manners. Extensive experiments are conducted on benchmarks of LEVIR-CD, WHU-CD and DSIFN-CD, demonstrating our proposed operations bring significant improvement and achieve competitive results under similar comparative conditions. Code is available at https://github.com/wangsp1999/CD-Research/tree/main/openAPD