To improve robotic positioning accuracy and enhance overall manufacturing precision, this paper proposes an adaptive momentum Levenberg-Marquardt cascaded B-spline interpolation particleswarmoptimization (AMLM-BIPSO...
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To improve robotic positioning accuracy and enhance overall manufacturing precision, this paper proposes an adaptive momentum Levenberg-Marquardt cascaded B-spline interpolation particleswarmoptimization (AMLM-BIPSO) algorithm for calibrating robotic geometric errors. Initially, a momentum term is incorporated into the traditional Levenberg-Marquardt algorithm to suppress overshooting and oscillation, thereby improving the preliminary estimation of geometric parameters. Subsequently, inspired by the concept of Knowledge-based artificial Neural Networks, B-spline interpolation is embedded into the standard particleswarmoptimization framework to refine the final calibration. By cascading these two enhanced techniques, the proposed method achieves higher accuracy in parameter identification. Experimental validation on an industrial robot confirms that the AMLM-BIPSO algorithm yields substantial improvements in positioning accuracy and calibration reliability.
For enhancing the prediction accuracy of power load forecasting, a support vector machine (SVM) prediction model based on wavelet transform and the mutant fruit fly parameter optimization intelligent algorithm (WT-MFO...
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For enhancing the prediction accuracy of power load forecasting, a support vector machine (SVM) prediction model based on wavelet transform and the mutant fruit fly parameter optimization intelligent algorithm (WT-MFOA-LSSVM) was presented. The load data were pretreated by wavelet transform, and the load curves were decomposed into different scales, in order to strengthen the regularity and randomness of historical data. Aiming at overcoming the problems of low convergence precision and easily relapsing into local extreme in basic fruit fly optimizationalgorithm (FOA), judge whether the intelligent algorithm was trapped in local extreme from the fitness variance of the population and the current optimal. Then, it was conducted by optimal individual perturbation and Gauss mutation operation and the mutant fruit flies were second times optimized, which made the accuracy of prediction model be obviously enhanced. The next few days of historical load data of a certain area of Henan Province, China, in 2015 were predicted by using WT-MFOA-LSSVM, and then the prediction results were compared with the results predicted by the SVM model and by the SVM model based on particleswarmoptimization model. The results showed that WT-MFOA-LSSVM had high precision in short term load forecasting, and it had a very good practical significance.
Magnetic field assisted laser welding (LW-MF) shows great potential in the jointing of large structures. The quality of the welding joint in LW-MF largely depends on the selection of process parameters. In this study,...
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Magnetic field assisted laser welding (LW-MF) shows great potential in the jointing of large structures. The quality of the welding joint in LW-MF largely depends on the selection of process parameters. In this study, an integrated process parameter optimization framework is developed for magnetic field assisted laser welding. Firstly, Taguchi method is selected to generate sample points and the LW-MF experiments are carried out to obtain the bead geometrical characteristics. Secondly, a sample-sorted SVR (SS-SVR) metamodeling approach is developed to make full use of the already-acquired prediction error information for fitting the relationships between multiple process parameters and the bead geometrical characteristics. A detailed comparison between the developed SS-SVR metamodeling approach and existing SVR metamodeling approach for prediction accuracy is performed. Then, the particleswarmoptimization is used to solve the process parameters optimization problem, in which the objective function values are predicted by the developed SS-SVR metamodel. Finally, verification experiment is conducted to verify the reliability of the obtained optimal process parameters. Results illustrate that the proposed integrated process parameter optimization framework is effective for obtaining the optimal process parameters and can be used in LW-MF for practical production.
In this paper, a general type-2 fuzzy logic controller (GT2FLC), which is optimized by the particleswarmoptimization (PSO) algorithm, is applied to a power-line inspection (PLI) robot. The information fusion is used...
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In this paper, a general type-2 fuzzy logic controller (GT2FLC), which is optimized by the particleswarmoptimization (PSO) algorithm, is applied to a power-line inspection (PLI) robot. The information fusion is used to design the GT2FLC to avoid the rule explosion. The proposed controller has the ability to deal with uncertainties when the PLI robot works on the insulated access cable. In order to compare the performance of the proposed controller with that of other controllers, the type-1 fuzzy logic controller (T1FLC) and the interval type-2 fuzzy logic controller (IT2FLC) are both optimized by the PSO to adjust the PLI robot. To show the ability of different controllers to deal with uncertainties, external disturbances and parameter perturbations are added to the PLI robot. According to simulations, the performance of the proposed controller is better than that of other controllers, and the proposed controller has better ability to deal with uncertainties.
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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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.
In the north of China, the problem of wind abandonment is very serious. In order to improve the ability of wind power consumption, a cogeneration system with an electric boiler is proposed. First, the mathematical mod...
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ISBN:
(数字)9781728158556
ISBN:
(纸本)9781728158556
In the north of China, the problem of wind abandonment is very serious. In order to improve the ability of wind power consumption, a cogeneration system with an electric boiler is proposed. First, the mathematical model of the minimum power generation cost of the traditional unit and the optimized model are established. Second, the model is solved by using the particle swarm optimization algorithm. In addition, a power structure of combined heat and power system (CHP) is constructed for simulation experiments. Through experimental analysis, proving the feasibility of the model. Finally, Simulation results show that making the electric boiler work in the period of wind abandonment can effectively alleviate the wind abandonment phenomenon. It also can provide more space for the wind power and enhance the wind power consumption.
Visual attention mechanism is one of the important means for human beings to perceive the external *** mathematical models to introduce visual attention mechanism into computer vision to simulate human visual percepti...
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ISBN:
(纸本)9781665448109
Visual attention mechanism is one of the important means for human beings to perceive the external *** mathematical models to introduce visual attention mechanism into computer vision to simulate human visual perception system is a hot research topic in the field of computer *** research of visual attention model is not only helpful for human beings to better explore the working mechanism of human visual attention,but also has very important significance for solving large-scale data screening and improving image processing efficiency,which has important application value in moving object detection,machine vision,image information matching,image compression and other *** visual attention model is used to preprocess the video sequence,and the region of interest is found as the candidate region for target *** the color and shape matching algorithm is used to match the candidate *** on tennis video show that the algorithm can recognize the target well when the color and shape of the target are relatively single and the significance in the background is high.
This paper describes the Design of controllers for Switched Reluctance Motor using technique such as particleswarmoptimization. Conventional PID controller is nowadays used in most Engineering being acknowledged its...
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ISBN:
(纸本)9781538607961
This paper describes the Design of controllers for Switched Reluctance Motor using technique such as particleswarmoptimization. Conventional PID controller is nowadays used in most Engineering being acknowledged its ability to give up a superior control in power electronic system. The purpose of this work is to design a speed controller for the motor to achieve minimum time domain integral squared error. This work concentrates mainly on the design of feedback PID controller to achieve the minimum integral squared error and hence the controller parameters k(p), k(i), and k(d) are identified. It can be done through PSO algorithm. The model of a converter works along with the algorithm which results with a robust feedback PID controller was developed using MATLAB/SIMULINK software.
Data security is the major problem in cloud computing. To overcome this problem in the existing work Password can be used as a key to encrypt and decrypt the data in cloud environment. Some of the limitations having P...
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Data security is the major problem in cloud computing. To overcome this problem in the existing work Password can be used as a key to encrypt and decrypt the data in cloud environment. Some of the limitations having Password system because it is not secured, and easily forgotten. In order to overcome these problems the proposed technique utilizes effective data storage using biometric-based authentication to support the user authentication for the cloud environment. For user authentication here we are considering iris and fingerprint. Initially the feature values are extracted from the iris and fingerprint using local binary pattern. In order to improve the security Extracting the feature value of fingerprint and iris and it is given input to the hybrid Genetic algorithm and particle swarm optimization algorithm to find the best solution using Cross over mutation technique. Best solution value can be act as a key for encrypting and decrypting data using Triple Data Encryption Standard algorithm. Finally encrypted data can be stored in cloud using cloud simulator in the Working platform of net beans in java. Finally randomly tested with 5 fingerprint and 5 Iris image for the purpose of man in the middle attack. After tested with fingerprint and iris proposed Hybrid Genetic algorithm with particle swarm optimization algorithm having less attack compared with the existing particle swarm optimization algorithm. So the intruder cannot be able to access the data in cloud environment.
Selective maintenance is widely used as a reliability-centered maintenance strategy due to the limited maintenance resources. However, existing selective maintenance studies only consider basic reliability, which cann...
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Selective maintenance is widely used as a reliability-centered maintenance strategy due to the limited maintenance resources. However, existing selective maintenance studies only consider basic reliability, which cannot systematically describe the operating mechanism of a multistate system, thereby resulting in the inability to obtain an optimal maintenance strategy. Moreover, intelligent manufacturing systems are highly representative of typical multistate industrial systems. In this study, a mission reliability-oriented selective maintenance optimization model for intelligent manufacturing systems that considers the uncertain maintenance effect was proposed. First, a new connotation and modeling method for mission reliability based on multistate system theory was presented to comprehensively characterize the operating mechanism of intelligent manufacturing systems. Second, a quantitative model between maintenance resources and quality based on real-time data was established to reflect the uncertain characteristics caused by repairmen and tools. Third, a selective maintenance decision model of a multistate manufacturing system was developed under the constraints of maintenance cost and time. This constraint combination optimization problem was solved using the particle swarm optimization algorithm. Finally, a case study of selective maintenance optimization for a cylinder head manufacturing system was presented to verify the proposed method.
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