optimization methods
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Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. Computational Intelligence (CI) techniques have been recognized as effective methods in generating and optimizing renewable tools. The complexity of this variety of energy depends on its coverage of large sizes of data and parameters, which have to be investigated thoroughly. This paper covered the most resent and important researchers in the domain of renewable problems using the learning-based methods. Various types of Deep Learning (DL) and Machine Learning (ML) algorithms employed in Solar and Wind energy supplies are given. The performance of the given methods in the literature is assessed by a new taxonomy. This paper focus on conducting comprehensive state-of-the-art methods heading to performance evaluation of the given techniques and discusses vital difficulties and possibilities for extensive research. Based on the results, variations in efficiency, robustness, accuracy values, and generalization capability are the most obvious difficulties for using the learning techniques. In the case of the big dataset, the effectiveness of the learning techniques is significantly better than the other computational methods. However, applying and producing hybrid learning techniques with other optimization methods to develop and optimize the construction of the techniques is optionally indicated. In all cases, hybrid learning methods have better achievement than a single method due to the fact that hybrid methods gain the benefit of two or more techniques for providing an accurate forecast. Therefore, it is suggested to utilize hybrid learning techniques in the future to deal with energy generation problems.

The numerical method of stamp topological optimization taking into account fatigue strength is presented in the work. It is proposed to take into account the restrictions on the stress state in accordance with the curve of the dependence of the maximum stresses on the number of loading cycles in the ESO topological optimization method. An approach to the selection of the evolutionary coefficient with a step-by-step increase in the rejection coefficient is proposed when constructing an iterative scheme for the rejection of elements by the method of topological optimization. The calculation of the stamp optimal topology with a decrease in volume due to the removal and redistribution of material was carried out in the study. The new geometric model of the optimal topology stamp is based on the predicted distribution of elements with a minimum stress level. The verification calculation of the stress state of the stamp of optimal topology with an assessment of fatigue strength was carried out in the work. The numerical calculation was carried out using the finite element method in the Ansys software package. The minimized stamp volume decreased by 35% according to the calculation results. The results of the study can be further applied in the development of topological optimization methods and in the design of stamping tools of optimal topology.

Public art planning and design in the context of smart cities need to keep pace with the times, but the integrity of the original scene needs to be maintained in the process of public art design. Therefore, this paper combines the elements of the scene and integrates the Internet of Things smart city to conduct public art planning and design research. Moreover, based on the multimedia Internet of Things environment, this paper analyzes the effects of virtual reality technology in urban public art planning and design and gives the overall optimization ideas for the organization and rendering of VR scene data. Then, this paper studies the organization and rendering optimization methods of the terrain scene model and the scene model, respectively. The experimental research results show that the smart city public art planning and design system under the multimedia Internet of Things environment designed in this paper has a good smart city public art planning and design effect.

AbstractIn this paper, we develop new affine-invariant algorithms for solving composite convex minimization problems with bounded domain. We present a general framework of Contracting-Point methods, which solve at each iteration an auxiliary subproblem restricting the smooth part of the objective function onto contraction of the initial domain. This framework provides us with a systematic way for developing optimization methods of different order, endowed with the global complexity bounds. We show that using an appropriate affine-invariant smoothness condition, it is possible to implement one iteration of the Contracting-Point method by one step of the pure tensor method of degree $$p \ge 1$$ p ≥ 1 . The resulting global rate of convergence in functional residual is then $${\mathcal {O}}(1 / k^p)$$ O ( 1 / k p ) , where k is the iteration counter. It is important that all constants in our bounds are affine-invariant. For $$p = 1$$ p = 1 , our scheme recovers well-known Frank–Wolfe algorithm, providing it with a new interpretation by a general perspective of tensor methods. Finally, within our framework, we present efficient implementation and total complexity analysis of the inexact second-order scheme $$(p = 2)$$ ( p = 2 ) , called Contracting Newton method. It can be seen as a proper implementation of the trust-region idea. Preliminary numerical results confirm its good practical performance both in the number of iterations, and in computational time.

AbstractQuantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. Mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models, thereby establishing a direct link between dynamic modelling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modelling of even larger and more complex systems than what is currently possible.

The existing optimization methods have the problem of image edge blur, which leads to a high degree of shadow residue. In order to address this problem, reduce the shadow residual degree, this paper designs a 3D video image processing effect optimization method supported by virtual reality technology. Coding was used to eliminate redundant data in video and eliminate image noise using median filtering. The virtual reality technology detects the image edge and determines the motion offset between the image frames. According to the motion parameters of the camera carrier obtained from the motion estimation, the feature point matching algorithm constructs the video image motion model, and uses the camera calibration technology to set the processing effect optimization mode. It is optimized by perspective projection transformation. Experimental results: the average shadow residual degree of the optimization method and the two existing optimization methods are 3.108%, 6.167% and 6.396% respectively, which proves that the optimization method combined with virtual reality technology has higher practical application value.