The deformation detection of large machinery is usually achieved using three-dimensional displacement measurement. Binocular stereo vision measurement technology, as a commonly used digital image correlation method, h...
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The deformation detection of large machinery is usually achieved using three-dimensional displacement measurement. Binocular stereo vision measurement technology, as a commonly used digital image correlation method, has received widespread attention in the academic community. Binocular stereo vision achieves the goal of three-dimensional displacement measurement by simulating the working mode of the human eyes, but the measurement is easily affected by light refraction. Based on this, the study introduces particle swarm optimization algorithm for target displacement measurement on Canon imaging dataset, and introduces backpropagation neural network for mutation processing of particles in particleswarmalgorithm to generate fusion algorithm. It combines the four coordinate systems of world, pixel, physics, and camera to establish connections. Taking into account environmental factors and lens errors, the camera parameters and deformation coefficients were revised by shooting a black and white checkerboard. Finally, the study first conducted error analysis on binocular stereo vision technology in three dimensions, and the relative error remained stable at 1 % within about 60 seconds. At the same time, three algorithms, including the spotted hyena algorithm, were introduced to conduct performance comparison experiments using particleswarmoptimization and backpropagation network algorithms. The experiment shows that the three-dimensional error of the fusion algorithm gradually stabilizes within the range of [-0.5 %, 0.5 %] over time, while the two-dimensional error generally hovers around 0 value. Its performance is significantly superior to other algorithms, so the binocular stereo vision of this fusion algorithm can achieve good measurement results.
Computing grids utilize Internet or special networks to access computing resources which are geographically widespread, in order to solve complex problems more effectively. Task scheduling in grid plays an important r...
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Computing grids utilize Internet or special networks to access computing resources which are geographically widespread, in order to solve complex problems more effectively. Task scheduling in grid plays an important role in grid system. This paper introduces mutation into particleswarmalgorithm. The method makes the algorithm jump out local optimization and search for the global optimal solution in other areas. To some extent, it overcomes the inherent flaw of PSO that falling into local optimization. Using this method in grid task scheduling can not only generate relevant scheme dynamically, and also make the complete time minimum. The experiment shows that the algorithm achieves a better result in task scheduling.
Most supply chain programming problems are restricted to the deterministic situations or stochastic environments. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertai...
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Most supply chain programming problems are restricted to the deterministic situations or stochastic environments. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertain programming model to optimize the supply chain production-distribution cost. The programming parameters of the material suppliers, manufacturer, distribution centers, and the customers are integrated into the presented model. On the basis of the chance measure and the credibility of grey fuzzy variable, the grey fuzzy simulation methodology was proposed to generate input-output data for the uncertain functions. The designed neural network can expedite the simulation process after trained from the generated input-output data. The improved particleswarmoptimization (PSO) algorithm based on the Differential Evolution (DE) algorithm can optimize the uncertain programming problems. A numerical example was presented to highlight the significance of the uncertain model and the feasibility of the solution strategy.
This paper demonstrates the analysis of novel methodology developed to select optimal buses for installation of convertor stations in the hybrid voltage source converter based high voltage direct current system. Here,...
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This paper demonstrates the analysis of novel methodology developed to select optimal buses for installation of convertor stations in the hybrid voltage source converter based high voltage direct current system. Here, a modified unified optimal power flow model is developed for the optimal power flow problem and solved using the particleswarmoptimization technique for the voltage source converter based high voltage direct current network. The analysis has been performed for optimizing the various techno-economic objective functions, including generation cost, voltage deviation, and total power system losses, for better power system operation. The developed unified optimal power flow model's effectiveness and methodology for deciding the high voltage direct current converter's optimal location are examined, with several tests performed with modified five bus and IEEE-30 bus system. The impact of high voltage direct current line replacement is decided based on optimal results obtained for selected techno-economic objective functions by replacing each AC line with high voltage direct current independently. The obtained results have proved the voltage source converter high voltage direct current controller's impact on optimization of generation cost, voltage deviation, and total power system losses.
In order to improve the performance of the hydraulic support electro-hydraulic control system test platform, a self-tuning proportion integration differentiation (PID) controller is proposed to imitate the actual pres...
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In order to improve the performance of the hydraulic support electro-hydraulic control system test platform, a self-tuning proportion integration differentiation (PID) controller is proposed to imitate the actual pressure of the hydraulic support. To avoid the premature convergence and to improve the convergence velocity for tuning PID parameters, the PID controller is optimized with a hybrid optimizationalgorithm integrated with the particleswarmalgorithm (PSO) and genetic algorithm (GA). A selection probability and an adaptive cross probability are introduced into the PSO to enhance the diversity of particles. The proportional overflow valve is installed to control the pressure of the pillar cylinder. The data of the control voltage of the proportional relief valve amplifier and pillar pressure are collected to acquire the system transfer function. Several simulations with different methods are performed on the hydraulic cylinder pressure system. The results demonstrate that the hybrid algorithm for a PID controller has comparatively better global search ability and faster convergence velocity on the pressure control of the hydraulic cylinder. Finally, an experiment is conducted to verify the validity of the proposed method.
With the development of cloud computing technology, people not only want to pursue the shortest time to complete the tasks by using cloud computing, but also hope to take into the running costs of machines. Existing t...
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With the development of cloud computing technology, people not only want to pursue the shortest time to complete the tasks by using cloud computing, but also hope to take into the running costs of machines. Existing task scheduling algorithm in the cloud computing environment has been unable to meet people's needs. As an extension and generalization of the model checking theory, probability model checking is also used in many fields, such as random distributed algorithm and other areas. The task scheduling algorithm based on the particle swarm optimization algorithm combined with probability model is proposed in this paper. The algorithm defines the fitness functions of the time cost and the running cost. The fitness functions can improve the efficiency of the cloud computing platform. At the same time, the probability model can be used to analyze the running states of machines and the computing capability of the nodes in the cloud cluster. The probability, which is calculated by the probability model, provides the basis for changing particleswarmalgorithm's the inertia factor and the learning factor, so as to solve the drawback that the inertia factor and the learning factor solely depend on the fixed value.
In this paper, a mathematical programming model is established for hybrid flow-shop scheduling problem,with the minimum of the makespan as the objective function. Based on the particle swarm optimization algorithm, a ...
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ISBN:
(纸本)9781424427239
In this paper, a mathematical programming model is established for hybrid flow-shop scheduling problem,with the minimum of the makespan as the objective function. Based on the particle swarm optimization algorithm, a distributed approach according to the process is presented to solve the global problem. Compared with the references, the experimental results indicate that the distributed approach performs better on improving computing and searching speed and being feasible and effective on global optimum.
In power market environment with energy conservation and emission reduction, clean energy power has an increasingly important position because of its low cost and environmental pollution. This paper researches on powe...
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In power market environment with energy conservation and emission reduction, clean energy power has an increasingly important position because of its low cost and environmental pollution. This paper researches on power system dynamic economy dispatch including wind system. The model of environment economic dispatch including wind power is established with the lowest generating cost of total power system as the objective function. The various constraint conditions are considered with conventional thermal power units and wind power. According to actual load data in a certain area, simulation test is completed using particle swarm optimization algorithm which has advantage of strength searching capability and fast optimizing. Simulation results show that the mathematical model is correct and the optimizationalgorithm is effective. Meanwhile, the application of the relative entropy balance theory is on evaluation and selection to the decision results.
In view of high dimension, the difficulty of training, the problem of slow learning speed in the application of BP neural network in mobile robot path planning, an algorithm of reinforcement Q learning based on online...
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In view of high dimension, the difficulty of training, the problem of slow learning speed in the application of BP neural network in mobile robot path planning, an algorithm of reinforcement Q learning based on online sequential extreme learning machine (Q-OSELM algorithm) was proposed in this paper. And then, due to the random selection of weight and threshold parameters, it also proposes an extreme learning machine algorithm optimized by particleswarm (PSO-ELM algorithm) in this paper. Firstly, Q-OSELM algorithm obtains current environment and the status information of the robot through the characteristic of reinforcement learning, which combines dynamic network with supervised learning. After that, the online sequential extreme learning machine is used to approximate the function of the current status to get the rewards and punishments of the current status; Secondly, it is used to solve the problem of slow training speed by the characteristic of less parameter settings and better generalization performance. PSO-ELM algorithm is used to optimize the input weights and the hidden layer bias of the extreme learning machine which have been seen as the particle of particle swarm optimization algorithm to improve the network structure of the extreme learning machine. It could overcome inaccuracy of traditional extreme learning machine through particle swarm optimization algorithm. Finally, the performance of two learning algorithms is verified. The simulation experimental results show that the Q-OSELM learning algorithm improves the initiative of machine learning. And compared with the Q-OSELM algorithm, the PSO-ELM algorithm has better generalization ability and higher training precision. Simulation experiments are carried out to verify the stability and convergence of the two algorithms.
The education industry, as the top priority of social operation, is constantly emerging with education systems or online education platforms based on internet technology. However, most of them are facing problems of r...
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The education industry, as the top priority of social operation, is constantly emerging with education systems or online education platforms based on internet technology. However, most of them are facing problems of rigidity, stiff, or resource scarcity. Therefore, this article aimed to establish a personalized education system to solve this problem and optimize the system based on intelligent algorithms. At the end of this article, a comparison was made on the algorithm performance of the decision tree algorithm in the intelligent algorithm. Compared with the original algorithm of the system, the accuracy increased from 70.35% to 75.68%. The system based on the intelligent algorithm also helped the students in the experimental class improve their grades, and even cleared the score record below 40 points, helping to improve the overall performance of the entire class.
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