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This paper gives a bibliographical review of the finite element and boundary element parallel processing techniques from the theoretical and application points of view. Topics include: theory – domain decomposition/partitioning, load balancing, parallel solvers/algorithms, parallel mesh generation, adaptive methods, and visualization/graphics; applications – structural mechanics problems, dynamic problems, material/geometrical non‐linear problems, contact problems, fracture mechanics, field problems, coupled problems, sensitivity and optimization, and other problems; hardware and software environments – hardware environments, programming techniques, and software development and presentations. The bibliography at the end of this paper contains 850 references to papers, conference proceedings and theses/dissertations dealing with presented subjects that were published between 1996 and 2002.

Wind energy plays a very important role in the future electrical power supply. With growing shares, the focus of the plant control will have to shift from maximum power yield to grid friendly aspects, like stable power output despite fluctuating wind power. The purpose of this paper is to design a new operation management for wind energy converters that combines high‐energy yield, grid friendly power output characteristics and the ability to adapt to changing wind conditions.

An operation control based on stochastic dynamic optimization was developed for the special demands of variable speed wind energy converters. The task of the operation control is to set the appliance to the optimal operation point, following the above‐mentioned goals by adapting the control pattern to changing wind conditions.

It is shown that the novel control concept, the iterative self‐adapting system management with stochastic dynamic optimization, is able to control wind energy converters in such a way that the effect of the stochastic fluctuating wind energy supply on the output power fluctuation is smoothed while maintaining a high‐energy yield.

This non‐linear stochastic dynamic optimization structure has two special characteristics, first is the iterative self‐adaptation, and second is the optimization for an infinite process, while the optimization criteria are high‐power yield and low‐power output fluctuations. This will be of great value for further increase of wind energy converters in the electrical power supply.

The purpose of this paper is to address the problem of polar quantization optimization. Particularly, the aim is to find the method for the optimal resolution‐constrained polar quantizer design.

The new iterative algorithm for determination of the optimal decision and representation magnitude levels and algorithm for optimization of number of phase cells within each magnitude level, is proposed.

At high rates, the new optimal polar quantizer outperforms the optimal polar compander for 0.2 dB, while the more significant gain should be expected at lower rates. In this paper, in order to enable practical implementation of quantizer model, algorithm which transforms real values for the optimal numbers of phase cells within magnitude levels into integer ones is also proposed. Moreover, the approximate closed form of signal‐to‐quantization ratio is derived.

Since circularly symmetric sources and complex presentation of signals arise in numerous applications, it can be concluded that the usage area of the suggested proposal is very wide (audio coding, image coding, spectral phase coding, synthetic aperture radars systems, coding of the discrete Fourier transform).

It should be emphasized that in contrast to earlier work, where models have been designed under high‐rate assumption, the obtained nonuniform unrestricted polar quantizer is optimal for all rates.

The purpose of this paper is to revisit the recursive computation of closed-form expressions for the topological derivative of shape functionals in the context of time-harmonic acoustic waves scattering by sound-soft (Dirichlet condition), sound-hard (Neumann condition) and isotropic inclusions (transmission conditions).

The elliptic boundary value problems in the singularly perturbed domains are equivalently reduced to couples of boundary integral equations with unknown densities given by boundary traces. In the case of circular or spherical holes, the spectral Fourier and Mie series expansions of the potential operators are used to derive the first-order term in the asymptotic expansion of the boundary traces for the solution to the two- and three-dimensional perturbed problems.

As the shape gradients of shape functionals are expressed in terms of boundary integrals involving the boundary traces of the state and the associated adjoint field, then the topological gradient formulae follow readily.

The authors exhibit singular perturbation asymptotics that can be reused in the derivation of the topological gradient function in the iterated numerical solution of any shape optimization or imaging problem relying on time-harmonic acoustic waves propagation. When coupled with converging Gauss−Newton iterations for the search of optimal boundary parametrizations, it generates fully automatic algorithms.

To make the robot that have real autonomous ability is always the goal of mobile robot research. For mobile robots, simultaneous localization and mapping (SLAM) research is no longer satisfied with enabling robots to build maps by remote control, more needs will focus on the autonomous exploration of unknown areas, which refer to the low light, complex spatial features and a series of unstructured environment, lick underground special space (dark and multiintersection). This study aims to propose a novel robot structure with mapping and autonomous exploration algorithms. The experiment proves the detection ability of the robot.

A small bio-inspired mobile robot suitable for underground special space (dark and multiintersection) is designed, and the control system is set up based on STM32 and Jetson Nano. The robot is equipped with double laser sensor and Ackerman chassis structure, which can adapt to the practical requirements of exploration in underground special space. Based on the graph optimization SLAM method, an optimization method for map construction is proposed. The Iterative Closest Point (ICP) algorithm is used to match two frames of laser to recalculate the relative pose of the robot, which improves the sensor utilization rate of the robot in underground space and also increase the synchronous positioning accuracy. Moreover, based on boundary cells and rapidly-exploring random tree (RRT) algorithm, a new Bio-RRT method for robot autonomous exploration is proposed in addition.

According to the experimental results, it can be seen that the upgraded SLAM method proposed in this paper achieves better results in map construction. At the same time, the algorithm presents good real-time performance as well as high accuracy and strong maintainability, particularly it can update the map continuously with the passing of time and ensure the positioning accuracy in the process of map updating. The Bio-RRT method fused with the firing excitation mechanism of boundary cells has a more purposeful random tree growth. The number of random tree expansion nodes is less, and the amount of information to be processed is reduced, which leads to the path planning time shorter and the efficiency higher. In addition, the target bias makes the random tree grow directly toward the target point with a certain probability, and the obtained path nodes are basically distributed on or on both sides of the line between the initial point and the target point, which makes the path length shorter and reduces the moving cost of the mobile robot. The final experimental results demonstrate that the proposed upgraded SLAM and Bio-RRT methods can better complete the underground special space exploration task.

Based on the background of robot autonomous exploration in underground special space, a new bio-inspired mobile robot structure with mapping and autonomous exploration algorithm is proposed in this paper. The robot structure is constructed, and the perceptual unit, control unit, driving unit and communication unit are described in detail. The robot can satisfy the practical requirements of exploring the underground dark and multiintersection space. Then, the upgraded graph optimization laser SLAM algorithm and interframe matching optimization method are proposed in this paper. The Bio-RRT independent exploration method is finally proposed, which takes shorter time in equally open space and the search strategy for multiintersection space is more efficient. The experimental results demonstrate that the proposed upgrade SLAM and Bio-RRT methods can better complete the underground space exploration task.

In this paper an application of structural optimization to the design of structures with constraints in frequency and mode shape is presented. The objective is to obtain an optimum design making adequate changes in the structure to modify its dynamic characteristics. The method is based on an iterative process of optimization that includes structural analysis by the Finite Element Method (FEM), sensitivity analysis, and optimization techniques. An efficient and accurate method is used to calculate the sensitivities of the dynamic behaviour of the structure. The sensitivity analysis is accomplished using a semi‐analytical procedure based on the Nelson method. A Sequential Linear Programming (SLP) algorithm is used to solve the optimization problem. In the minimization process the convergence is assured even in a short number of iterations. The validation of the method is also shown by means of two examples of application.

This paper aims to empirically test, from a regulatory portfolio management standpoint, the application of liquidity-adjusted risk techniques in the process of getting optimum and investable economic-capital structures in the Gulf Cooperation Council financial markets, subject to applying various operational and financial optimization restrictions under crisis outlooks.

The author implements a robust methodology to assess regulatory economic-capital allocation in a liquidity-adjusted value at risk (LVaR) context, mostly from the standpoint of investable portfolios analytics that have long- and short-sales asset allocation or for those portfolios that contain long-only asset allocation. The optimization route is accomplished by controlling the nonlinear quadratic objective risk function with certain regulatory constraints along with LVaR-GARCH-M (1,1) procedure to forecast conditional risk parameters and expected returns for multiple asset classes.

The author’s conclusions emphasize that the attained investable economic-capital portfolios lie-off the efficient frontier, yet those long-only portfolios seem to lie near the efficient frontier than portfolios with long- and short-sales assets allocation. In effect, the newly observed market microstructures forms and derived deductions were not apparent in prior research studies (Al Janabi, 2013).

The attained empirical results are quite interesting for practical portfolio optimization, within the environments of big data analytics, reinforcement machine learning, expert systems and smart financial applications. Furthermore, it is quite promising for multiple-asset portfolio management techniques, performance measurement and improvement analytics, reinforcement machine learning and operations research algorithms in financial institutions operations, above all after the consequences of the 2007–2009 financial crisis.

While this paper builds on Al Janabi’s (2013) optimization algorithms and modeling techniques, it varies in the sense that it covers the outcomes of a multi-asset portfolio optimization method under severe event market scenarios and by allowing for both long-only and combinations of long-/short-sales multiple asset. The achieved empirical results, optimization parameters and efficient and investable economic-capital figures were not apparent in Al Janabi’s (2013) paper because the prior evaluation were performed under normal market circumstances and without bearing in mind the impacts of the 2007–2009 global financial crunch.

This work aims to present a multilevel optimization strategy based on manifold‐mapping combined with multiquadric interpolation for the coarse model construction.

In the proposed approach the coarse model is obtained by interpolating the fine model using multiquadrics in a small number of points. As the algorithm iterates the response surface model is improved by enriching the set of interpolation points.

This approach allows to accurately solve the TEAM Workshop Problem 25 using as little as 33 finite element simulations. Furthermore, it allows a robust sizing optimization of a cylindrical voice‐coil actuator with seven design variables.

Further analysis is required to gain a better understanding of the role that the initial coarse model accuracy plays in the convergence of the algorithm. The proposed model allows to carry out such analysis by varying the number of points included in the initial response surface model. The effect of the trust‐region stabilization in the presence of manifolds of equivalent solutions is also a topic of further investigations.

Unlike the closely related space‐mapping algorithm, the manifold‐mapping algorithm is guaranteed to converge to a fine model optimal solution. By combining it with multiquadric response surface models, its applicability is extended to problems for which other kinds of coarse model such as lumped parameter approximations for instance are tedious or impossible to construct.

The liquefied natural gas (LNG) business comprises a number of economic activities with inherent risks. The purpose of this paper is to propose an integrated modelling approach, as part of the investment decision‐making process, for optimising economic returns from LNG whilst taking into account uncertainty in various key input parameters.

Inter‐linked cash flow and pricing models of the LNG chain were constructed. Net present value was maximised based on selection of netback pricing variables and level of investment shareholding. Constraints were placed on the minimum acceptable returns. The risk affinity of the decision maker was captured in the form of a chance‐constrained optimisation problem. A genetic algorithm was applied for numerical optimisation, in combination with Monte Carlo simulations to account for the stochastic nature of the problem.

Based on the results of a case study, the deterministic solution, having no consideration to uncertainty, was found to be both sub‐optimal and provided an unsatisfactory risk outcome. The stochastic approach yielded an optimal solution with due consideration to risk. Various scenarios show that the choice of the decision variables significantly impacts the trade‐off between risk and returns along the LNG chain to government and investor.

The suitability of the methodology to the operational phase of the LNG business which incorporates different elements of risk, such as market dynamics and logistics, is as yet untested.

This framework may be useful in the formulation of optimal commercial structure of firms, investment portfolio and gas/LNG pricing arrangements for host governments involved in the LNG business.

Simultaneous localization and map building (SLAM), as a state estimation problem, is a prerequisite for solving the problem of autonomous vehicle motion in unknown environments. Existing algorithms are based on laser or visual odometry; however, the lidar sensing range is small, the amount of data features is small, the camera is vulnerable to external conditions and the localization and map building cannot be performed stably and accurately using a single sensor. This paper aims to propose a laser three dimensions tightly coupled map building method that incorporates visual information, and uses laser point cloud information and image information to complement each other to improve the overall performance of the algorithm.

The visual feature points are first matched at the front end of the method, and the mismatched point pairs are removed using the bidirectional random sample consensus (RANSAC) algorithm. The laser point cloud is then used to obtain its depth information, while the two types of feature points are fed into the pose estimation module for a tightly coupled local bundle adjustment solution using a heuristic simulated annealing algorithm. Finally, the visual bag-of-words model is fused in the laser point cloud information to establish a threshold to construct a loopback framework to further reduce the cumulative drift error of the system over time.

Experiments on publicly available data sets show that the proposed method in this paper can match its real trajectory well. For various scenes, the map can be constructed by using the complementary laser and vision sensors, with high accuracy and robustness. At the same time, the method is verified in a real environment using an autonomous walking acquisition platform, and the system loaded with the method can run well for a long time and take into account the environmental adaptability of multiple scenes.

A multi-sensor data tight coupling method is proposed to fuse laser and vision information for optimal solution of the positional attitude. A bidirectional RANSAC algorithm is used for the removal of visual mismatched point pairs. Further, oriented fast and rotated brief feature points are used to build a bag-of-words model and construct a real-time loopback framework to reduce error accumulation. According to the experimental validation results, the accuracy and robustness of the single-sensor SLAM algorithm can be improved.