This study focuses on solving multi-objective optimization problems in distributed power generation systems (DPGS) for renewable energy in China and Russia, including low economic efficiency, poor environmental benefi...
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For a class of linear uncertain flight control systems, in terms of the strip region, the problem of actuator continuous gain fault and reliable control is studied. At the same time, in order to solve the difficulty o...
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ISBN:
(纸本)9789881563804
For a class of linear uncertain flight control systems, in terms of the strip region, the problem of actuator continuous gain fault and reliable control is studied. At the same time, in order to solve the difficulty of selecting parameters of support vector machine, a parameter optimizationalgorithm based on improved particleswarmoptimization (MPSO) is proposed. Compared with grid search method, particleswarmoptimization and genetic algorithm, this method has the advantages of fast convergence, short time consuming and high accuracy. For the problem that the pole information is difficult to obtain, the algorithm theory of pole observer is given to realize the real-time observation of unknown system. Finally, the effectiveness of reliable controller design is further proved by numerical simulation.
In Multi-specialty hospitals, the quantity of accumulation of Bio-Medical Waste (BMW) is enormous when compared to clinics. The safe and timely disposal of BMW is very essential to avoid harmful effects to humans and ...
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In Multi-specialty hospitals, the quantity of accumulation of Bio-Medical Waste (BMW) is enormous when compared to clinics. The safe and timely disposal of BMW is very essential to avoid harmful effects to humans and environment. In this article, the inbound logistics involved in the collection of Bio-Medical Waste at a Private Multi-Specialty Hospital located in Coimbatore which contains 59 wards has been improved to avoid time delay. An optimized vehicle routing model has been framed for a set of 6 dedicated vehicles with the objective to minimize the time taken during the collection of BMW. For this purpose a mathematical model is generated and solved using particle swarm optimization algorithm (PSO). The results infer that, by following the optimized vehicle routes, the time delay is totally eliminated and in addition the time taken for collecting the BMW is reduced by 42%, i.e. from 6 h to 3 h 46 min. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Advances in Materials Research-2019.
Aiming at the problems of large positioning error and low positioning effect at the boundary of traditional RFID virtual tag positioning algorithm, this paper compares the influence of four different interpolation met...
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ISBN:
(纸本)9781665422604
Aiming at the problems of large positioning error and low positioning effect at the boundary of traditional RFID virtual tag positioning algorithm, this paper compares the influence of four different interpolation methods of virtual reference tag signal strength on positioning accuracy, and proposes a RFID virtual tag positioning algorithm based on Monte Carlo. The algorithm uses dynamic particles to replace the traditional static reference tag;introduces particle swarm optimization algorithm to update the Monte Carlo sample particleswarm, and gives different weights to the sampling particles based on the signal strength difference between the sampling particles and the undetermined tags, and finally completes the localization of the unknown tags through Monte Carlo resampling. The simulation results show that the algorithm can effectively improve the accuracy and stability of RFID positioning system compared with the traditional virtual tag positioning algorithm.
An on-line intelligent optimization method based on an artificial neural network is proposed for the parameter adjustment of the active disturbance rejection controller. And a cascaded ADRC controller including the ar...
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ISBN:
(纸本)9789881563804
An on-line intelligent optimization method based on an artificial neural network is proposed for the parameter adjustment of the active disturbance rejection controller. And a cascaded ADRC controller including the artificial neural network attitude ADRC is investigated for trajectory tracking of the six-rotor UAV. First, establish the kinematics and dynamics model of the six-rotor, and design a cascaded active disturbance rejection controller for the six-rotor to deal with the non-linear disturbance problem in flight. Secondly, an artificial neural network is designed to optimize the parameters of the attitude ADRC controller on-line, and the particleswarmalgorithm is used to set the initial value of the artificial neural network. Finally, the simulation results demonstrated that ADRC based on the artificial neural network can effectively resist the disturbances and enhance the robustness of the attitude controller and the cascade ADRC controller based on the artificial neural network can track the reference trajectory quickly and accurately.
The loop-closing operation is usually used to transfer power supply without power interruption in the distribution network, but the excessive current induced by loop-closing will endanger the safety and stability of t...
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ISBN:
(纸本)9781665432634
The loop-closing operation is usually used to transfer power supply without power interruption in the distribution network, but the excessive current induced by loop-closing will endanger the safety and stability of the distribution network. This paper presents a multi-objective optimal control method of loop-closing in distribution network. Firstly, the mathematical optimization model of loop-closing optimal regulation is established, which not only ensures the security of loop-closing operation, but also takes into account the stability and voltage quality of distribution network operation. Then, Pareto entropy-based multi-objective particleswarmoptimization (PE-MOPSO) algorithm is proposed to solve the optimization problem. Finally, an actual example is simulated based on MATLAB platform. The simulation results show that the optimization model is correct, and PE-MOPSO algorithm has good diversity and convergence, and can give a reasonable control scheme.
The rapid development of the Internet has broadened the existing channels of rumor propagation, it has added many new features during the dynamic process of the rumor propagation that are different from those in the p...
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The rapid development of the Internet has broadened the existing channels of rumor propagation, it has added many new features during the dynamic process of the rumor propagation that are different from those in the past. In this paper, we construct an improved XYZ -ISR two-layer model considering time delay to describe the dynamic process of rumor propagation in multiple channels. Firstly, we obtain the basic regeneration number R0 by the next generation matrix method. Secondly, we derive the stability condition of the equilibrium point by constructing Lyapunov functions. In order to reduce the negative impact caused by rumor propagation at min-imum cost, we design an event-triggered impulsive control strategy and optimize the control parameters by using a particle swarm optimization algorithm. Then, by comparing the rumor propagation process in the XYZ -ISR two-layer model and the classical rumor DK model, we find that although rumors propagate faster in the XYZ -ISR model in the initial stage, the rumor spreads on a smaller scale than in the DK model in terms of the whole life cycle of the rumor due to the official supervision and timely release of corresponding rumor-debunking infor-mation. Besides, individuals need a certain amount of careful consideration time between receiving the infor-mation and deciding whether to forward it or not, and this time delay slows the behavior spreading and reduces its scale. Finally, we verify the reasonableness of the above theoretical results through numerical simulations. Moreover, by simulating a rumor case on the microblog with real data, we find that the model proposed in this paper can fit the real rumor propagation process well, and the event-triggered impulsive control strategy can control the rumor propagation more effectively in multiple channels.
High-precision short-term wind generation prediction results are conducive to making a scientific generation plan and improving the wind power absorption capacity of the power grids. Based on the analysis of the relat...
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ISBN:
(纸本)9781665434980
High-precision short-term wind generation prediction results are conducive to making a scientific generation plan and improving the wind power absorption capacity of the power grids. Based on the analysis of the relationship between the numerical weather prediction and wind power, this paper proposes a short-term wind generation combined forecast model considering meteorological similarity to improve the prediction accuracy of short-term wind power. In this method, the meteorological similarity day model, the extreme gradient boosting algorithm and the back propagation neural network algorithm are selected for achieving the short-term wind power prediction. Then, the particle swarm optimization algorithm is applied to determine the weight of each single forecasting model. Finally, the prediction results are obtained through the combination of the single model prediction results. With the realistic wind power data collected from a wind farm in Xinjiang province, the short-term wind forecasting task is achieved by the proposed method. The simulation results illustrate that the combined model proposed in this paper can effectively improve the forecasting performance of the benchmark models.
Fuzzy C-Means clustering, FCM, is an unsupervised learning algorithm. The algorithm is easily affected by noise points and depends on the initial values. When the sample value is large, the algorithm is easy to fall i...
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Fuzzy C-Means clustering, FCM, is an unsupervised learning algorithm. The algorithm is easily affected by noise points and depends on the initial values. When the sample value is large, the algorithm is easy to fall into local extremum. In this study, the traditional fuzzy clustering algorithm is improved, and the particle swarm optimization algorithm with global optimization ability is applied to the FCM algorithm, and chaotic technology is added. Chaotic variables produce a chaotic sequence based on the current global optimal position, using chaotic sequence has the best fitness value of particles randomly instead of a particle of the particleswarm, the improved algorithm can effectively avoid the stagnation of particles in the iteration, fast search to the global optimal solution, avoid convergence to local extremum. Experimental results indicate that this algorithm overcomes the dependence on the initial clustering centre of FCM, which brings high robustness and segmentation accuracy, and has more faster convergence speed.
Asphalt mixture is the construction material of asphalt pavement surface. Its thickness, compactness and asphalt content are the main indexes of pavement quality evaluation. The LR dielectric model is modified by expe...
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ISBN:
(纸本)9781538662434
Asphalt mixture is the construction material of asphalt pavement surface. Its thickness, compactness and asphalt content are the main indexes of pavement quality evaluation. The LR dielectric model is modified by experiments in this thesis. The modified dielectric model can represent the essential relationship between the composite dielectric properties of asphalt mixture and the three-phase volume ratio of aggregate, asphalt and air voids. Based on the finite-difference time-domain method, the particleswarmoptimization (PSO) algorithm for inverse analysis of three-phase volume fraction (air voids, asphalt and aggregate volume fraction) of asphalt mixtures is established by taking the mean square error of actual received signal and analog forward signal as fitness function. Based on this method, the inverse analysis of three-phase asphalt volume fraction mixtures is realized. By comparing the theoretical waveform inversion results with the theoretical model, the feasibility of the algorithm for multi-parameter inversion was verified, and the accuracy of the algorithm was further verified by comparing the inversion results of field measured data with the results of core sampling.
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