In smart manufacturing workshops, automated guided vehicles (AGVs) are increasingly used to transport materials required for machine tools. This paper studies the AGV path planning problem of a one-line production lin...
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In smart manufacturing workshops, automated guided vehicles (AGVs) are increasingly used to transport materials required for machine tools. This paper studies the AGV path planning problem of a one-line production line in the workshop, establishes a mathematical model with the shortest transportation time as the objective function, and proposes an improvedparticleswarmoptimization(IPSO) algorithm to obtain an optimal path. In order to be suitable for solving the path planning problem, we propose a new coding method based on this algorithm, design a crossover operation to update the particle position, and adopt a mutation mechanism to avoid the algorithm from falling into the local optimum. By calculating the shortest transportation time obtained, the improvedalgorithm is compared with other intelligent optimizationalgorithms. The experimental results show that the algorithm can improve the efficiency of AGV in material transportation and verify the effectiveness of related improvement mechanisms.
Using an improved particle swarm optimization algorithm(IPSO)to drive a transfer matrix method,a nonreciprocal absorber with an ultrawide absorption bandwidth and angular insensitivity is realized in plasma-embedded p...
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Using an improved particle swarm optimization algorithm(IPSO)to drive a transfer matrix method,a nonreciprocal absorber with an ultrawide absorption bandwidth and angular insensitivity is realized in plasma-embedded photonic crystals arranged in a structure composed of periodic and quasi-periodic sequences on a normalized *** effective dielectric function,which determines the absorption of the plasma,is subject to the basic parameters of the plasma,causing the absorption of the proposed absorber to be easily modulated by these *** with other quasi-periodic sequences,the Octonacci sequence is superior both in relative bandwidth and absolute *** further optimization using IPSO with 14 parameters set to be optimized,the absorption characteristics of the proposed structure with different numbers of layers of the smallest structure unit N are shown and *** is also used to address angular insensitive nonreciprocal ultrawide bandwidth absorption,and the optimized result shows excellent unidirectional absorbability and angular insensitivity of the proposed *** impacts of the sequence number of quasi-periodic sequence M and collision frequency of plasma1ν1 to absorption in the angle domain and frequency domain are ***,the impedance match theory and the interference field theory are introduced to express the findings of the algorithm.
In order to solve the problem of movement coordination and stability between the wearable medical robotics and the wearer, analysis the structure of lower limbs and movement gait of human body, obtains the structure p...
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In order to solve the problem of movement coordination and stability between the wearable medical robotics and the wearer, analysis the structure of lower limbs and movement gait of human body, obtains the structure principle and motion characteristics of human lower limbs;Based on anthropomorphic design criteria, proposed a wearable medical robotics, use particleswarmoptimization (PSO) to optimize the installation position of the hydraulic cylinder at hip joint and knee joint, The optimal parameters of the hip and knee variables were obtained through optimization. Adopt the idea of passive drive instead of active drive, designed the hydraulic drive system and control signal amplifying circuit, established the mathematical model of the four-way sliding valve with zero opening and analyzed its stability;Use AMESim software to build the model of the hydraulic system, and conducted the motion simulation test of the hydraulic system, the results show that:The working process of the hydraulic cylinder meets the characteristics of lower limbs movement of the wearer;built the experiment platform and conducted the walking attitude experiment, the result shows that:the pressure measured by the pressure sensor modules at both ends of the soles of the feet, and the mean pressure of the soles of the feet will fluctuate between 0 and 800 N with the motion of the man-machine system, the X-axis acceleration, Y-axis acceleration, Z-axis acceleration and the acceleration scalar are all changing within the range of -10 m/s2 similar to 20 m/s2, meeting the requirements of normal human movement. (C) 2021 Elsevier B.V. All rights reserved.
In the rapid development of high and new technology, the intelligence and integration of modern equipment are constantly improving. Power electronics technology is one of the indispensable key technologies in any high...
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In the rapid development of high and new technology, the intelligence and integration of modern equipment are constantly improving. Power electronics technology is one of the indispensable key technologies in any high and new technology. In this paper, a power electronics circuit fault diagnosis based on improvedparticleswarmoptimization neural network is proposed, the algorithm design of particleswarmoptimizationalgorithm neural network is introduced, and the improved PS0 algorithm, standard PS0 algorithm, and BP algorithm optimized neural network are applied to the fault diagnosis classification system of rectifier circuits. The results show that the parameters of the basic (particleswarmoptimization) algorithm are as follows: the parameter value of the basic PSO algorithm is the number of particles is 30, W decreases from 0.9 to 0.4 linearly with the increase of iterations, and the number of iterations is 300. The BP algorithm uses the traingdx training function. The transfer functions of the hidden layer and the output layer are hyperbolic tangent sigmoid and Purelin function, respectively. The target error e = 0.01. The superiority and effectiveness of the neural network diagnosis model of the improved PS0 algorithm are shown in this paper. This method can solve the fault diagnosis problem of the double-bridge parallel rectifier circuit.
With the continuous access of large-scale distributed photovoltaics (PV) to low-voltage distribution networks, the three-phase imbalance problem of low-voltage distribution networks is becoming increasingly severe, an...
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In order to alleviate the pressure of urban road traffic, the coordinated control strategy of regional traffic signals is studied. Taking the maximum capacity of the traffic control sub-area as the optimization object...
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ISBN:
(数字)9781510652118
ISBN:
(纸本)9781510652118;9781510652101
In order to alleviate the pressure of urban road traffic, the coordinated control strategy of regional traffic signals is studied. Taking the maximum capacity of the traffic control sub-area as the optimization objective, the regional signal coordinated control model is constructed, and the particleswarmoptimizationalgorithm is used to solve the problem. The performance of the particleswarmalgorithm is improved by adding interference items, decreasing inertia weight and adjusting learning factors. The above model and algorithm are verified by the traffic flow data of the actual road network. The experimental results show that the improvedparticleswarmalgorithm has faster convergence speed and higher solution accuracy, and the proposed signal control model can effectively improve the overall traffic capacity of the region.
Nowadays, world trade has been greatly impacted by the epidemic, and the level of foreign trade of enterprises is facing severe challenges. This article established an interest function with the goals of economic grow...
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Nowadays, world trade has been greatly impacted by the epidemic, and the level of foreign trade of enterprises is facing severe challenges. This article established an interest function with the goals of economic growth rate, industrial structure adjustment, and technological progress from three aspects: economic growth rate, industrial structure adjustment, and technological innovation. By adjusting the particle position, the transformation strategy can be modified to promote the optimization of China's economic and trade (E&T) structure, thereby achieving sustainable development of transformation and upgrading. It evaluated the promoting effect of the improved particle swarm optimization algorithm (PSOA) on economic and trade transfer by comparing the economic growth rate before and after its adoption. This article adopted a traditional strategy, with an economic growth rate of 6.2% in the eastern region and 5.8% in the western region in 2020. In 2021 and 2022, with the adoption of the improved PSOA strategy, the overall economic growth rate has significantly improved. The E&T transformation and upgrading strategy based on improved PSOA is conducive to promoting economic development and improving economic efficiency.
In recent years, short-term wind power forecasting has proved to be an effective technology, which can promote the development of industrial informatization and play an important role in solving the control and utiliz...
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In recent years, short-term wind power forecasting has proved to be an effective technology, which can promote the development of industrial informatization and play an important role in solving the control and utilization problems of renewable energy system. However, the application of short-term wind power prediction needs to deal with a large number of data to avoid the instability of forecasting, which is facing more and more difficulties. In order to solve this problem, this paper proposes a novel prediction approach based on kernel principal component analysis and echo state network optimized by improved particle swarm optimization algorithm. Short-term wind power generation is affected by many factors. The original multi-dimensional input variables are pre-processed by kernel principal component analysis to determine the principal components that affect wind power. The dimension of principal component is less than the original input data, which reduces the complexity of modeling. The convergence and stability of the echo state network can be improved by using the principal component of the input variable. The advantage is to reduce the input variables, eliminate the correlation between the input variables, and improve the prediction performance of the prediction model. Furthermore, an improved particle swarm optimization algorithm is proposed to optimize the dynamic reservoir parameters of echo state network. Compared with other state-of-the-art prediction models, the case studies show that the proposed approach has good prediction performance for actual wind power data.
A modified particleswarmoptimization (PSO) approach is presented for the multi-mode resource-constrained scheduling problem of automated guided vehicle (AGV) tasks. Various constraints in the scheduling process of t...
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A modified particleswarmoptimization (PSO) approach is presented for the multi-mode resource-constrained scheduling problem of automated guided vehicle (AGV) tasks. Various constraints in the scheduling process of the AGV system are analysed, and the types and quantities of AGVs as allocable resources are considered. The multiple-AGV combined distribution mode and its impact on distribution tasks is also considered. Finally, a multi-mode resource-constrained task scheduling model is established for which the object is to minimise material delivery time. Based on the above model, the discrete particleswarmoptimizationalgorithm that improved the basic PSO was proposed. The simulation results with the test set in PSPLIB standard library showed the effectiveness of the improved PSO algorithm.
Industrial furnace kiln internal combustion flame directly reflects the combustion of fuel quality and stability and determines the security of the whole production process. The flame image contains many important inf...
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Industrial furnace kiln internal combustion flame directly reflects the combustion of fuel quality and stability and determines the security of the whole production process. The flame image contains many important information that cannot be observed by people's eyes, as a result, how to effectively separate the flame image from the surrounding background by means of science and technology has the great research significance and application value. In this article, the idea of neighborhood particles is introduced into the standard particleswarmoptimizationalgorithm, and a furnace flame recognition method is proposed based on improved particle swarm optimization algorithm. The method first uses red, green and blue color space to design the extraction model of flame image, then uses the proposed improved particle swarm optimization algorithm and Otsu algorithm to solve the optimal segmentation threshold involved in the model. Experimental results show that the proposed improved particle swarm optimization algorithm can always find the optimal segmentation threshold of the flame image within no more than 100 iterations and reduce the computation time nearly 0.01 s. Compared with the previous research results, the recognition rate of the extraction model designed in this article has been greatly improved to over 93%, which is of great value for the safe and stable operation of industrial furnaces.
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