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.
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.
The purpose of the present article is to introduce theoretical and algorithmic approaches to address the problem of finding optimal test-control incomplete block designs with unequal block sizes where intra-block obse...
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The purpose of the present article is to introduce theoretical and algorithmic approaches to address the problem of finding optimal test-control incomplete block designs with unequal block sizes where intra-block observations are correlated. Theoretical approach is used to find E-tc-optimal designs analytically. In addition, due to the computational complexity of theoretical methods, in this article a two-phase optimization algorithm is proposed to construct phi-optimal or nearly phi-optimal designs. The effectiveness of the proposed algorithm is validated by comparing our results with optimal designs presented in several prior studies. Our algorithm has the advantages of being independent from the sizes of blocks, structure of correlation, and the optimality criteria. Moreover, it takes only a few minutes to obtain the optimal designs.
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.
The effects of cutting speed, cutting feed, depth of cut on the output responses in turning were investigated for different tool conditions. Three-factor and three-level fractional experiment designs completed with st...
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The effects of cutting speed, cutting feed, depth of cut on the output responses in turning were investigated for different tool conditions. Three-factor and three-level fractional experiment designs completed with statistical analysis of variance (ANOVA) were performed. Mathematical models for output responses were developed using response surface methodology (RSM). EN8 steel is work piece material and TiN coated cemented carbide is cutting tool. The experiments were conducted for fresh, worn out tools and the output responses are measured. The responses (cutting force, tool wear, surface roughness) are to be minimised. A quadratic model is developed along with combined optimisation of the response using RSM. For each test the output responses was measured for both tools. Finally an optimum cutting speed of 90 mm/min for fresh tool and 270 mm/min for worn out tool was obtained. The results concluded that pso algorithm produces better optimisation compared to firefly, cuckoo search algorithms.
The normal depth is an essential parameter for the design, operation, and maintenance of open channels. The circular, city-gate crossing and egg-shaped sections are often used in non-pressure tunnels in hydraulic engi...
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The normal depth is an essential parameter for the design, operation, and maintenance of open channels. The circular, city-gate crossing and egg-shaped sections are often used in non-pressure tunnels in hydraulic engineering, agricultural irrigation, and sewerage works. However, the equations governing the normal depth in various sections are complicated implicit transcendental equations. In order to improvements the solutions for normal depth in multiple sections of tunnels, a function model is first established for the geometric features of various sections using the mathematical transform method and while considering non-dimensional parameters. Then implement the revised pso algorithms in MATLAB, and establish three right solution's formulas for the normal depths in different non-pressure tunnel sections through optimization. The error analysis results and project cases show that the established formula has broad applicability. The maximum relative errors of the formula for normal depths are less than 0.07%, 0.04%, and 0.07% in circular, city-gate crossing, and egg-shaped sections, respectively, which are more accurate than the existing formulas. The results of this research may be useful in design, operation, and maintenance in tunnel engineering.
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 teacher's performance evaluation is an important guarantee for the development of higher education. In view of the limitations of traditional analytic hierarchy process in the teachers' performance compreh...
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The teacher's performance evaluation is an important guarantee for the development of higher education. In view of the limitations of traditional analytic hierarchy process in the teachers' performance comprehensive evaluation and the shortcomings of BP neural network in the teachers' performance comprehensive evaluation, such as non-convergence and large prediction error, the paper proposed an evaluation index system based on analytic hierarchy process as input of BP neural network, and used dynamic inertial weight and multiple empirical particles to improve pso algorithm and optimize the weights and thresholds of BP network, established teacher's performance evaluation model. The simulation results show that the model effectively reduces the number of network iterations, improves the prediction accuracy, and has a good application prospect in the teacher's performance evaluation.
Critical depth is an essential parameter for the design, operation, and maintenance of conduits. Circular, arched, and egg-shaped sections are often used in non-pressure conduits in hydraulic engineering, irrigation, ...
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Critical depth is an essential parameter for the design, operation, and maintenance of conduits. Circular, arched, and egg-shaped sections are often used in non-pressure conduits in hydraulic engineering, irrigation, and sewerage works. However, equations governing the critical depth in various sections are complicated implicit transcendental equations. The function model is established for the geometric features of multiple sections using the mathematical transform method and while considering non-dimensional parameters. Then, revised pso algorithms are implemented in MATLAB, and the right solution's formula for the critical depths in various non-pressure conduit sections is established through optimization. The error analysis results show that the established formula has broad applicability. The maximum relative errors of the formula for critical depths are less than 0.182%, 0.0629%, and 0.170% in circular, arched, and egg-shaped sections, respectively, which are more accurate than those of existing formulas;the form of the formula proposed in this work is also more compact than that of the existing formulas. The results of this research may be useful in design, operation, and maintenance in conduit engineering.
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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