Research on the smooth velocity control of a driving switching process of a new pneumatic electric hybrid actuator

In order to meet the application requirements of fast-moving, accurate positioning, and low cost, a new type of pneumatic electric hybrid actuator was developed with a positioning accuracy of 0.01 mm. It was found that the velocity fluctuation would occur in the process of driving switching due to the “stop then restart” strategy, resulting in stumbling when positioning. To solve this problem, the basic characteristics of the drive switching mechanism were tested and analyzed. A driving switching strategy called “relay and run” was proposed based on the response characteristics of the locking mechanism in the braking stage and the starting characteristics of the motor. The uniform acceleration was controlled by the motor drive to compensate for the velocity loss so that the overall velocity was slowly reduced. The control model was established, and the experiments were conducted. The results showed that the relay and run switching strategy can eliminate the velocity fluctuation caused by the stop then restart strategy.

A Restart Local Search for Solving Diversified Top-k Weight Clique Search Problem

Diversified top-k weight clique (DTKWC) search problem is an important generalization of the diversified top-k clique (DTKC) search problem with practical applications. The diversified top-k weight clique search problem aims to search k maximal cliques that can cover the maximum weight in a vertex weighted graph. In this work, we propose a novel local search algorithm called TOPKWCLQ for the DTKWC search problem which mainly includes two strategies. First, a restart strategy is adopted, which repeated the construction and updating processes of the maximal weight clique set. Second, a scoring heuristic is designed by giving different priorities for maximal weight cliques in candidate set. Meanwhile, a constraint model of the DTKWC search problem is constructed such that the research concerns can be evaluated. Experimental results show that the proposed algorithm TOPKWCLQ outperforms than the comparison algorithm on large-scale real-world graphs.

Simulated Annealing with Restart Strategy for the Path Cover Problem with Time Windows

This research presents a variant of the vehicle routing problem known as the path cover problem with time windows (PCPTW), in which each vehicle starts with a particular customer and finishes its route at another customer. The vehicles serve each customer within the customer’s time windows. PCPTW is motivated by a practical strategy for companies to reduce operational cost by hiring freelance workers, thus allowing workers to directly service customers without reporting to the office. A mathematical programming model is formulated for the problem. This research also proposes a simulated annealing heuristic with restart strategy (SARS) to solve PCPTW and test it on several benchmark datasets. Computational results indicate that the proposed SARS effectively solves PCPTW.

Fast recovery strategy for MMC-HVDC based on coordination of fault current suppression and adaptive restart

The rapid suppression of fault current flowing through overhead transmission lines and safe restart of the system are critical problems to be solved urgently to improve the stability of the DC grid. A novel adaptive restart strategy combined with fault current suppression is proposed, which suitable for full-bridge modular multilevel converter high voltage direct current system. The resistance-capacitance energy transfer branch is used to achieve a large reduction and rapid attenuation of the fault current amplitude at restart time. The transient fault and permanent fault can be discriminated in a short interval after restarting based on the frequency domain amplitude characteristics. The proposed scheme has both restart-current protection and fault property identification functions, overcoming the high cost and low component reusability of traditional single-function fault current limiting schemes. The simulation results show that the scheme has a significant fault current suppression effect and accurately discriminates the fault property to realize rapid recovery.

Inertial gradient method for fluorescence molecular tomography

Image reconstruction in fluorescence molecular tomography involves seeking stable and meaningful solutions via the inversion of a highly under-determined and severely ill-posed linear mapping. An attractive scheme consists of minimizing a convex objective function that includes a quadratic error term added to a convex and nonsmooth sparsity-promoting regularizer. Choosing [Formula: see text]-norm as a particular case of a vast class of nonsmooth convex regularizers, our paper proposes a low per-iteration complexity gradient-based first-order optimization algorithm for the [Formula: see text]-regularized least squares inverse problem of image reconstruction. Our algorithm relies on a combination of two ideas applied to the nonsmooth convex objective function: Moreau–Yosida regularization and inertial dynamics-based acceleration. We also incorporate into our algorithm a gradient-based adaptive restart strategy to further enhance the practical performance. Extensive numerical experiments illustrate that in several representative test cases (covering different depths of small fluorescent inclusions, different noise levels and different separation distances between small fluorescent inclusions), our algorithm can significantly outperform three state-of-the-art algorithms in terms of CPU time taken by reconstruction, despite almost the same reconstructed images produced by each of the four algorithms.

Methodology for Modified Whale Optimization Algorithm for Solving Appliances Scheduling Problem

Whale Optimization Algorithm (WOA) is considered as one of the newest metaheuristic algorithms to be used for solving a type of NP-hard problems. WOA is known of having slow convergence and at the same time, the computation of the algorithm will also be increased exponentially with multiple objectives and huge request from n users. The current constraints surely limit for solving and optimizing the quality of Demand Side Management (DSM) case, such as the energy consumption of indoor comfort index parameters which consist of thermal comfort, air quality, humidity and vision comfort. To address these issues, this proposed work will firstly justify and validate the constraints related to the appliances scheduling problem, and later proposes a new model of the Cluster based Multi-Objective WOA with multiple restart strategy. In order to achieve the objectives, different initialization strategy and cluster-based approaches will be used for tuning the main parameter of WOA under different MapReduce application which helps to control exploration and exploitation, and the proposed model will be tested on a set of well-known test functions and finally, will be applied on a real case project i.e. appliances scheduling problem. It is anticipating that the approach can expedite the convergence of meta-heuristic technique with quality solution.