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Improving the accuracy and reducing computational costs of predictions, especially the prediction of time series, is one of the most critical parts of the decision-making processes and management in different areas and organizations. One of the best solutions to achieve high accuracy and low computational costs in time series forecasting is to develop and use efficient hybrid methods. Among the combined methods, parallel hybrid approaches are more welcomed by scholars and often have better performance than sequence ones. However, the necessary condition of using parallel combinational approaches is to estimate the appropriate weight of components. This weighting stage of parallel hybrid models is the most effective factor in forecasting accuracy as well as computational costs. In the literature, meta-heuristic algorithms have often been applied to weight components of parallel hybrid models. However, such that algorithms, despite all unique advantages, have two serious disadvantages of local optima and iterative time-consuming optimization processes. The purpose of this paper is to develop a linear optimal weighting estimator (LOWE) algorithm for finding the desired weight of components in the global non-iterative universal manner.

In this paper, a LOWE algorithm is developed to find the desired weight of components in the global non-iterative universal manner.

Empirical results indicate that the accuracy of the LOWE-based parallel hybrid model is significantly better than meta-heuristic and simple average (SA) based models. The proposed weighting approach can improve 13/96%, 11/64%, 9/35%, 25/05% the performance of the differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO) and SA-based parallel hybrid models in electricity load forecasting. While, its computational costs are considerably lower than GA, PSO and DE-based parallel hybrid models. Therefore, it can be considered as an appropriate and effective alternative weighing technique for efficient parallel hybridization for time series forecasting.

In this paper, a LOWE algorithm is developed to find the desired weight of components in the global non-iterative universal manner. Although it can be generally demonstrated that the performance of the proposed weighting technique will not be worse than the meta-heuristic algorithm, its performance is also practically evaluated in real-world data sets.

Laser navigation without a reflector does not require setup of reflector markers at the scene and thus has the advantages of free path setting and flexible change. This technology has attracted wide attention in recent years and shows great potential in the field of automatic logistics, including map building and locating in real-time according to the environment. This paper aims to focus on the application of feature matching for map building.

First, an improved linear binary relation algorithm was proposed to calculate the local similarity of the feature line segments, and the matching degree matrix of feature line segments between two adjacent maps was established. Further, rough matching for the two maps was performed, and both the initial rotation matrix and the translation vector for the adjacent map matching were obtained. Then, to improve the rotation matrix, a region search optimization algorithm was proposed, which took the initial rotation matrix as the starting point and searched gradually along a lower error-of-objective function until the error sequence was nonmonotonic. Finally, the random-walk method was proposed to optimize the translation vector by iterating until the error-objective function reached the minimum.

The experimental results show that the final matching error was controlled within 10 mm after both rotation and translation optimization. Also, the algorithm of map matching and optimization proposed in this paper can realize accurately the feature matching of a laser navigation map and basically meet the real-time navigation and positioning requirements for an automated-guided robot.

A linear binary relation algorithm was proposed, and the local similarity between line segments is calculated on the basis of the binary relation. The hill-climbing region search algorithm and the random-walk algorithm were proposed to optimize the rotation matrix and the translation vector, respectively. This algorithm has been applied to industrial production.

The purpose of this paper is to demonstrate improvement of the accuracy of electrical tomography reconstruction by incorporation of a priori knowledge into the inverse problem solution.

The fusion of two different inversion algorithms capable of real‐time operation is discussed, namely a non‐iterative monotonicity‐based approach, determining the a priori knowledge and an iterative Gauss‐Newton (GN)‐based reconstruction algorithm. Furthermore, the method is compared with the unmodified algorithms themselves by means of reconstructions from simulated inclusions at different noise levels.

The accuracy of the inverse problem reconstructions, especially at the boundary regions of the unknown inclusions, benefit from the investigations of incorporating a priori knowledge about material values and can be considerable improved. The monotonicity method itself, which has low complexity, provides remarkable reconstruction results in electrical tomography.

The paper is applied to simulated discrete two‐phase scenarios, e.g. gas/oil mixtures. In a further step the method would be tested with measured data. Moreover, investigations have to be carried out in order to make the monotonicity‐based reconstruction principle more robust against disturbing artifacts.

The fusion of the non‐iterative monotonicity‐based method with the GN‐based algorithm demonstrates a novel approach of improving the reconstruction accuracy in electrical tomography.

The purpose of this article is to analyze the optimization process in depth, elaborating on the components of the entire process and the techniques used. Researchers have been drawn to the expanding trend of optimization since the turn of the century. The rate of research can be used to measure the progress and increase of this optimization procedure. This study is phenomenal to understand the optimization process and different algorithms in addition to their application by keeping in mind the current computational power that has increased the implementation for several engineering applications.

Two-dimensional analysis has been carried out for the optimization process and its approaches to addressing optimization problems, i.e. computational power has increased the implementation. The first section focuses on a thorough examination of the optimization process, its objectives and the development of processes. Second, techniques of the optimization process have been evaluated, as well as some new ones that have emerged to overcome the above-mentioned problems.

This paper provided detailed knowledge of optimization, several approaches and their applications in civil engineering, i.e. structural, geotechnical, hydraulic, transportation and many more. This research provided tremendous emerging techniques, where the lack of exploratory studies is to be approached soon.

Optimization processes have been studied for a very long time, in engineering, but the current computational power has increased the implementation for several engineering applications. Besides that, different techniques and their prediction modes often require high computational strength, such parameters can be mitigated with the use of different techniques to reduce computational cost and increase accuracy.

In recent decades, the field of robotic mapping has witnessed widespread research and development in light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) techniques. This paper aims to provide a significant reference for researchers and engineers in robotic mapping.

This paper focused on the research state of LiDAR-based SLAM for robotic mapping as well as a literature survey from the perspective of various LiDAR types and configurations.

This paper conducted a comprehensive literature review of the LiDAR-based SLAM system based on three distinct LiDAR forms and configurations. The authors concluded that multi-robot collaborative mapping and multi-source fusion SLAM systems based on 3D LiDAR with deep learning will be new trends in the future.

To the best of the authors’ knowledge, this is the first thorough survey of robotic mapping from the perspective of various LiDAR types and configurations. It can serve as a theoretical and practical guide for the advancement of academic and industrial robot mapping.

Electrical capacitance tomography (ECT) and electrical resistance tomography (ERT) are promising techniques for multiphase flow measurement due to their high speed, low cost, non-invasive and visualization features. There are two major difficulties in image reconstruction for ECT and ERT: the “soft-field”effect, and the ill-posedness of the inverse problem, which includes two problems: under-determined problem and the solution is not stable, i.e. is very sensitive to measurement errors and noise. This paper aims to summarize and evaluate various reconstruction algorithms which have been studied and developed in the word for many years and to provide reference for further research and application.

In the past 10 years, various image reconstruction algorithms have been developed to deal with these problems, including in the field of industrial multi-phase flow measurement and biological medical diagnosis.

This paper reviews existing image reconstruction algorithms and the new algorithms proposed by the authors for electrical capacitance tomography and electrical resistance tomography in multi-phase flow measurement and biological medical diagnosis.

The authors systematically summarize and evaluate various reconstruction algorithms which have been studied and developed in the word for many years and to provide valuable reference for practical applications.

Analytical models are often used in the first steps of the design process. They are associated with optimisation methods to find a solution that fulfil the design specifications. In this paper, the analytical model of an electric motor is built and proposed as a benchmark to highlight the optimisation methods the most fitted to analytical models.

This paper studies the optimal design of a brushless DC wheel motor. First, the analytical model is presented. Each equation used for the sizing is described, including the physical phenomenon associated, the hypotheses done, and some precautions to take before computing. All equations are ordered to ease their resolution, due to a specific procedure which is then described. Secondly, three optimisation problems with an increasing number of parameters and constraints are proposed. Finally, the results found by the sequential quadratic method point out the special features of this benchmark.

The constraint optimisation problem proposed is clearly multimodal as shown in the results of one deterministic method. Many starting points were used to initialise the optimisation methods and lead to two very different solutions.

First, an analytical model for the optimal design is detailed and each equation is explained. A specific procedure is presented to order all equations in order to ease their resolution. Secondly, a multimodal benchmark is proposed to promote the development of hybrid methods and special heuristics.

A mathematical description of temperature-dependent boundary conditions is crucial in manifold model-based control or prototyping applications, where accurate thermal simulation results are required. Estimation of boundary condition coefficients for complex geometries in complicated or unknown environments is a challenging task and often does not fulfill given accuracy limits without multiple manual adaptions and experiments. This paper aims to describe an efficient method to identify thermal boundary conditions from measurement data using model order reduction.

An optimization problem is formulated to minimize temperature deviation over time between simulation data and available temperature sensors. Convection and radiation effects are expressed as a combined heat flux per surface, resulting in multiple temperature-dependent film coefficient functions. These functions are approximated by a polynomial function or splines, to generate identifiable parameters. A formulated reduced order system description preserves these parameters to perform an identification. Experiments are conducted with a test-bench to verify identification results with radiation, natural and forced convection.

The generated model can approximate a nonlinear transient finite element analysis (FEA) simulation with a maximum deviation of 0.3 K. For the simulation of a 500 min cyclic cooling and heating process, FEA takes a computation time of up to 13 h whereas the reduced model takes only 7-11 s, using time steps of 2 s. These low computation times allow for an identification, which is verified with an error below 3 K. When film coefficient estimation from literature is difficult due to complex geometries or turbulent air flows, identification is a promising approach to still achieve accurate results.

A well parametrized model can be further used for model-based control approaches or in observer structures. To the knowledge of the authors, no other methodology enables model-based identification of thermal parameters by physically preserving them through model order reduction and therefore derive it from a FEA description. This method can be applied to much more complex geometries and has been used in an industrial environment to increase product quality, due to accurate monitoring of cooling processes.

This paper aims to improve the alignment accuracy of large components in aircraft assembly and an evaluation algorithm, which is based on manufacture accuracy and coordination accuracy, is proposed.

With relative deviations of manufacturing feature points and coordinate feature points, an evaluation function of assembly error is constructed. Then the optimization model of large aircraft digital alignment is established to minimize the synthesis assembly error with tolerance requirements, which consist of three-dimensional (3D) tolerance of manufacturing feature points and relative tolerance between coordination feature points. The non-linear constrained optimization problem is solved by Lagrange multiplier method and quasi-Newton method with its initial value provided by the singular value decomposition method.

The optimized postures of large components are obtained, which makes the tolerance of both manufacturing and coordination requirements be met. Concurrently, the synthesis assembly error is minimized. Compared to the result of the singular value decomposition method, the algorithm is validated in three typical cases with practical data.

The proposed method has been used in several aircraft assembly projects and gained a good effect.

This paper proposes a method to optimize the manufacturing and coordination accuracy with tolerance constraints when the postures of several components are adjusted at the same time. The results of this paper will help to improve the quality of component assemblies.

This paper aims to introduce a new fault protection scheme for microgrid DC networks with ring buses.

It is well recognized that the protection scheme in a DC ring bus microgrid becomes very complicated due to the bidirectional power flow. To provide reliable protection, the differential current signal is decomposed into several basic modes using adaptive variational mode decomposition (VMD). In this method, the mode number and the penalty factor are chosen optimally by using arithmetic optimization algorithm, yielding satisfactory decomposition results than the conventional VMD. Weighted Kurtosis index is used as the measurement index to select the sensitive mode, which is used to evaluate the discrete Teager energy (DTE) that indicates the occurrence of DC faults. For localizing cable faults, the current signals from the two ends are used on a sample-to-sample basis to formulate the state space matrix, which is solved by using generalized least squares approach. The proposed protection method is validated in MATLAB/SIMULINK by considering various test cases.

DTE is used to detect pole-pole and pole-ground fault and other disturbances such as high-impedance faults and series arc faults with a reduced detection time (10 ms) compared to some existing techniques.

Verification of this method is performed considering various test cases in MATLAB/SIMULINK platform yielding fast detection timings and accurate fault location.