Goncalves, Eduardo NunesRibeiro Belo, Mateus AlvesBatista, Ana Paula
13页
查看更多>>摘要：In networked control systems, by means of event-triggered transmission, it is possible to reduce the network usage, keeping the control system performance at satisfactory levels. There are several schemes for event-triggered transmission. In this study, we propose a multi-objective optimization problem to tune the event-triggered mechanisms. On solving the proposed problem by means of multi-objective evolutionary optimization, a set of efficient solutions is generated with different tradeoffs between control system performance and the number of transmissions. To solve the proposed problem, we also developed an improved multi-objective differential evolution algorithm that includes a self-adaptive mechanism, dynamic crowding distance operator, and novel elitism of the first front. The proposed method is applied to tune decentralized event-triggered mechanisms for a controller given a priori, considering random network-induced delays and packet loss. Two case studies are present ed, comparing the performance of eight different decentralized event-triggered schemes, analyzing the selection of the sampling period, and demonstrating the efficacy of the proposed tuning method. (C) 2021 Elsevier B.V. All rights reserved.
查看更多>>摘要：This research presents iterative optimum training (IOT), which integrates deep neural networks (DNNs) and population-based optimization techniques such as genetic algorithms (GAs). The proposed technique reduces the number of experiments needed for training without adding complexity compared with non-iterative DNN-GA techniques commonly used in the literature. In this work, IOT is used to train an optimal controller for minimizing wind-induced vibration (WIV) using distributed aerodynamic actuators. Wind tunnel experiments of a scaled cyber-physical aeroelastic building model are used to demonstrate a novel application of the technique. IOT trains a DNN to approximate building vibration at different wind conditions and actuator orientations using an initial set of experiments. After this initial training, a GA uses the DNN to predict actuator orientations that minimize WIV for the given wind condition. A group containing best orientations from the GA and uniform random orientations is used to perform additional experiments and training of the DNN to enhance exploitation and exploration. This process is repeated until the stopping criteria is achieved. This paper includes results of a benchmark study comparing IOT to GA and DNN-GA techniques. Experimental results show that IOT-based online control of the aeroelastic model reduces WIV acceleration amplitudes by up to 90% within 9.8 s upon controller activation. (C) 2021 Elsevier B.V. All rights reserved.
查看更多>>摘要：With the development of Industry 4.0 technology, it is a popular trend to reduce maintenance costs and ensure the safety of novel nuclear systems combined with deep learning (DL) technology. In this paper, an intelligent fault detection and diagnosis system (IFDDS) based on designed adaptive residual convolutional neural networks (ARCNNs) for small modular reactors (SMRs) is proposed. The features under different noise levels are learned as the residual and passed through the designed networks. Additionally, the learning efficiency is enhanced by the soft threshold (ST) method assembled in the adaptive residual processing (ARP) module. The Bayesian optimization (BO) method is adopted to improve the learning decay rate (LDR) of designed networks for better diagnosis performance. A total of 1,760 experimental data points under 11 different operation scenarios at three different noise levels are collected from the established Chinese lead-based nuclear reactor (CLEAR) platform to verify the effectiveness of the proposed IFDDS. The comparisons with the traditional RCNNs and CNNs adopted in previous works highlight the proposed diagnosis method's superiority. The performance of IFDDS is further improved by using the BO method. The proposed method, as a maiden attempt of intelligence research for SMRs, will provide remote decision-making support for nuclear operators in unattended conditions. Moreover, the universal method can also be applied to other diagnosis systems under a noise environment. (C) 2021 Elsevier B.V. All rights reserved.
Gonzalez, PatriciaOsorio, Roberto R.Pardo, Xoan C.Banga, Julio R....
14页
查看更多>>摘要：Combinatorial optimization problems arise in many disciplines, both in the basic sciences and in applied fields such as engineering and economics. One of the most popular combinatorial optimization methods is the Ant Colony Optimization (ACO) metaheuristic. Its parallel nature makes it especially attractive for implementation and execution in High Performance Computing (HPC) environments. Here we present a novel parallel ACO strategy making use of efficient asynchronous decentralized cooperative mechanisms. This strategy seeks to fulfill two objectives: (i) acceleration of the computations by performing the ants' solution construction in parallel; (ii) convergence improvement through the stimulation of the diversification in the search and the cooperation between different colonies. The two main features of the proposal, decentralization and desynchronization, enable a more effective and efficient response in environments where resources are highly coupled. Examples of such infrastructures include both traditional HPC clusters, and also new distributed environments, such as cloud infrastructures, or even local computer networks. The proposal has been evaluated using the popular Traveling Salesman Problem (TSP), as a well-known NP-hard problem widely used in the literature to test combinatorial optimization methods. An exhaustive evaluation has been carried out using three medium and large size instances from the TSPLIB library, and the experiments show encouraging results with superlinear speedups compared to the sequential algorithm (e.g. speedups of 18 with 16 cores), and a very good scalability (experiments were performed with up to 384 cores improving execution time even at that scale). (C) 2021 The Authors. Published by Elsevier B.V.
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Elsevier