Traffic bottleneck refers to the road section or node that often causes the propagation or spread of traffic congestion in the road network, which is the source of the whole road network congestion. In this paper, the...
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Traffic bottleneck refers to the road section or node that often causes the propagation or spread of traffic congestion in the road network, which is the source of the whole road network congestion. In this paper, the author analyzes the bottleneck prediction of urban road network based on improved pso algorithms and fuzzy control. This paper analyzes the factors and characteristics of the main road of the system, proposes the traffic coordination control of the main road based on the delay model, and carries on the statistical simulation to the actual traffic data, develops the basic theory of the traffic coordination control which is more effective than the traditional timing control strategy. Compared with the traditional model, the algorithm considers the waiting time of the red light at the intersection. For the congested road section, it can better calculate the travel time of the vehicle, making the results more accurate and more applicable. The results of this study can provide a strong theoretical basis and prediction scheme for the traffic management and control of the road network in the target area.
To solve the strong randomicity and slow convergence of the Particle Swarm Optimization(pso) algorithms, two new particle's position renewal formulas were analyzed on the basis of extrapolation in mathematics. A n...
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ISBN:
(纸本)9780769537290
To solve the strong randomicity and slow convergence of the Particle Swarm Optimization(pso) algorithms, two new particle's position renewal formulas were analyzed on the basis of extrapolation in mathematics. A new modified pso algorithms (called Leading pso algorithms) was put forward. The Direct Torque Control(DTC) System was built in the environment of Matlab(Simulink). The weight and threshold values of BP Neural Network were trained using the modified pso algorithms. Some disadvantages such as slow convergence speed and easily plunging into the local solution were avoided effectively. The simulation result shows that the system works well, and the rotor speed identifier has great static and dynamic performance.
The process parameters of plasma sprayed nanostructured ZrO2-7%Y2O3 coatings were optimised based on a particle swarm optimisation (pso) algorithm. A BP neural network was applied to compute the fit of the pso algorit...
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The process parameters of plasma sprayed nanostructured ZrO2-7%Y2O3 coatings were optimised based on a particle swarm optimisation (pso) algorithm. A BP neural network was applied to compute the fit of the pso algorithm, and was assembled with four process parameters which included spraying distance, spraying electric current, primary gas pressure, and secondary gas pressure as the inputs;the bonding strength of the coating was the output. The results of the pso algorithm and BP neural network show that the maximal bonding strength of the coatings was 42.5822 MPa. And the optimal process parameters discovered in this research for the plasma sprayed nanostructured ZrO2-7%Y2O3 coatings are a spraying distance of 80 mm, spraying electric current of 994.3707 A, primary gas pressure of 0.2575 MPa, and secondary gas pressure 1.1611 MPa.
Computational intelligence is employed to solve factual and complicated global problems, though neural networks (NNs) and evolutionary computing have also affected these issues. Biometric traits are applicable for det...
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Computational intelligence is employed to solve factual and complicated global problems, though neural networks (NNs) and evolutionary computing have also affected these issues. Biometric traits are applicable for detecting crime in security systems because they offer attractive features such as stability and uniqueness. Although various methods have been proposed for this objective, feature shortcomings such as computational complexity, long run times, and high memory consumption remain. The current study proposes a novel human iris recognition approach based on a multi-layer perceptron NN and particle swarm optimisation (pso) algorithms to train the network in order to increase generalisation performance. A combination of these algorithms was used as a classifier. A pre-processing step was performed on the iris images to improve the results and two-dimensional gabor kernel feature extraction was applied. The data was normalised, trained, and tested using the proposed method. A pso algorithm was applied to train the NN for data classification. The experimental results show that the proposed method performs better than many other well-known techniques. The benchmark Chinese Academy of Science and Institute of Automation (CASIA)-iris V3 and Center for Machine Learning and Intelligent Systems at the University of California, Irvine (UCI) machine learning repository datasets were used for testing and comparison.
The synthesis of a simple electronically reconfigurable annular ring monopole antenna using a designing optimisation process based on particle swarm optimisation (pso) and artificial bee colony (ABC) algorithms is pro...
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The synthesis of a simple electronically reconfigurable annular ring monopole antenna using a designing optimisation process based on particle swarm optimisation (pso) and artificial bee colony (ABC) algorithms is proposed. Several antenna dimensions are selected as objective functions of pso and ABC and their best solutions are considered as the optimised dimensions of the antenna geometry. Two radio-frequency p-i-n diodes, connecting the antenna feeding line to two microstrip stubs are used to change the antenna frequency response from ultra-wideband (UWB), from 3.1 to 10.6 GHz, to narrowband (NB) operation about 5.8 GHz. The antenna design and simulation are performed using Ansoft high-frequency structure simulator software. pso and ABC algorithms are written in Java language. Thereafter, antenna prototypes are fabricated and measured for validation purposes. Simulation and measurement results are obtained showing good agreement. The measured optimised impedance bandwidths of the UWB and NB bands are up to 128 and 23%, respectively. Additionally, simulated and measured radiation patterns are very similar when the reconfigurable antenna is operating in OFF-state (UWB sensing antenna) and in ON-state (NB transmitting antenna) modes, indicating the proposed antenna geometry as a promising candidate for applications such as UWB and cognitive radio.
With increasing demand for electricity, wind turbines have gained significant attention from the public. Offshore wind power generation has emerged as a popular choice due to its potential to reduce power transmission...
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With increasing demand for electricity, wind turbines have gained significant attention from the public. Offshore wind power generation has emerged as a popular choice due to its potential to reduce power transmission losses and minimal impact on humans and organisms. However, it also presents challenges in detecting defects in Wind Turbine Rotor Blades (WTRB) and repairing them. To address this issue, this paper proposes an improved wavelet-based S-U-Net network to denoise WTRB images followed by a weakly supervised CNN method for removing background parts that could affect defective feature extraction. Defective features are then extracted using VGG16 network on a deep learning server platform, while an enhanced Particle Swarm Optimization (pso) algorithm combined with K-means is used to classify defect features of WTRBs. Experimental results demonstrate that classification accuracy of unlabelled blade defect datasets improved significantly from 62.6% using only Kmeans clustering method up to 96.4% using our proposed algorithm approach. This study applies improved pso and Kmeans algorithms towards offshore Wind Turbine Rotor Blade condition monitoring with precise detection test results enabling early-stage detection of Wind Turbine Rotor Blades defects leading to timely repairs.
In this paper, a decentralized cooperative adaptive cruise control algorithm for vehicles in the vicinity of intersections (CACC-VI) is proposed. This algorithm is designed to make use of the road capacity to let more...
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In this paper, a distributed trajectory generation strategy is proposed to control a group of Unmanned Aerial Vehicles (UAVs). The issue, treated as an online optimization problem, is solved using a Particle Swarm Opt...
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The problem of finding optimal block designs can be formulated as a combinatorial optimization, but its resolution is still a formidable challenge. This paper presents a general and user-friendly algorithm, namely Mod...
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The problem of finding optimal block designs can be formulated as a combinatorial optimization, but its resolution is still a formidable challenge. This paper presents a general and user-friendly algorithm, namely Modified Particle Swarm Optimization (Mpso), to construct optimal or near-optimal block designs. It can be used for several classes of block designs such as binary, non-binary and test-control block designs with correlated or uncorrelated observations. In order to evaluate the algorithm, we compare our results with the optimal designs presented in some published papers. An advantage of our algorithm is its independency to the sizes of blocks and the structure of correlations.
Planning rational product structure of coal preparation is the key to attain the maximization of economic benefit in coal preparation enterprise and to save energy resources. There are many factors effect the preparat...
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