Optimization method for clamping layout of thin-walled frame parts based on deconvolution neural network and particle swarm optimization algorithm

Parametric analysis and optimization of entropy generation in unsteady MHD flow over a stretching rotating disk using artificial neural network and particle swarm optimization algorithm

COVID-19, one of the most dangerous pandemics, is currently affecting humanity. COVID-19 is spreading rapidly due to its high reliability transmissibility. Patients who test positive more often have mild to severe symptoms such as a cough, fever, raw throat, and muscle aches. Diseased people experience severe symptoms in more severe cases. such as shortness of breath, which can lead to respiratory failure and death. Machine learning techniques for detection and classification are commonly used in current medical diagnoses. However, for treatment using neural networks based on improved Particle Swarm Optimization (PSO), known as PSONN, the accuracy and performance of current models must be improved. This hybridization implements Particle Swarm Optimization and a neural network to improve results while slowing convergence and improving efficiency. The purpose of this study is to contribute to resolving this issue by presenting the implementation and assessment of Machine Learning models. Using Neural Networks and Particle Swarm Optimization to help in the detection of COVID-19 in its early stages. To begin, we preprocessed data from a Brazilian dataset consisted primarily of early-stage symptoms. Following that, we implemented Neural Network and Particle Swarm Optimization algorithms. We used precision, accuracy score, recall, and F-Measure tests to evaluate the Neural Network with Particle Swarm Optimization algorithms. Based on the comparison, this paper grouped the top seven ML models such as Neural Networks, Logistic Regression, Nave Bayes Classifier, Multilayer Perceptron, Support Vector Machine, BF Tree, Bayesian Networks algorithms and measured feature importance, and other, to justify the differences between classification models. Particle Swarm Optimization with Neural Network is being deployed to improve the efficiency of the detection method by more accurately predicting COVID-19 detection. Preprocessed datasets with important features are then fed into the testing and training phases as inputs. Particle Swarm Optimization was used for the training phase of a neural net to identify the best weights and biases. On training data, the highest rate of accuracy gained is 0.98.738 and on testing data, it is 98.689.

The dynamic model of hypersonic vehicle has nonlinear and uncertain characteristics. The traditional sliding mode variable structure needs to combine the optimization algorithm to suppress the chattering problem. The sliding mode variable structure controller based on RBF neural network parameter adjustment can eliminate the chattering problem of sliding mode control to a certain extent, but the uncertainty of network self-learning effect will affect the convergence efficiency of parameters, in order to improve the approximation effect of the network. The RBF neural network and particle swarm optimization algorithm are combined organically, the particle swarm optimization algorithm is used to optimize the hidden layer basis function width of the RBF neural network and the self-learning, self-adaptive and self-organizing ability of the center improvement algorithm, and the PSO-RBF tuning law is designed to train and test the height step command and the speed step command. The efficiency of the parameter convergence of the sliding mode variable structure controller algorithm is improved. Through a large number of numerical simulations and controller algorithm comparison analysis, it is verified that the sliding mode variable structure controller of PSO-RBF tuning parameters has strong robustness in suppressing various uncertainties due to interference.

Combination of back propagation(BP)neural network and particle swarm optimization(PSO) algorithms is used to optimize process variables during the laser cladding.BP neural network model is developed to express the relationship between the clad process variables and the clad parameters(the width,height of clad bead),and the samples obtained in experiments are used to train network model to form the perfect map relation between input and output.Then,PSO algorithm is used to grabble the suitable values of the process variables.The experimental clad parameters with the process variable values calculated by this optimization method are coincident well with the expected ones.It is verified experimentally that combination of BP neural network and PSO algorithms can help to obtain the expected laser clad quality.

In this paper, fault diagnosis model based on the fusion of hybrid neural network (HNN) and ant colony optimization algorithm (ACOA) is presented. The associated rules are extracted based on rough set theory and are used as the theoretic basis of the connection mechanism of higher-order NN, which was composed with the feedforwardNN, the hybrid NN model is constructed. ACOA was used to optimize solving and to improve the generalization performance of HNN model. Hence the construction of presented model possesses theoretical significance and may ensure to take optimization performance and to enhance the generalization ability. Through the simulation and the analysis of fault-tolerance performance (FTP), it shows that the model based on the fusion of HNN and ACOA can effectively enhance the generalization ability and the FTP.

This paper presents a tolerance analog circuit hard fault and soft fault diagnosis method based on the BP neural network and particle swarm optimization algorithm. First, select the mean square error function of BP neural network as the fitness function of the PSO algorithm. Second, change the guidance of neural network algorithms rely on gradient information to adjust the network weights and threshold methods, through the use of the characteristics of the particle swarm algorithm groups parallel search to find more appropriate network weights and threshold. Then using the adaptive learning rate and momentum BP algorithm to train the BP neural network. Finally, the network is applied to fault diagnosis of analog circuit, can quickly and effectively to the circuit fault diagnosis.

A new approach for visual identification of orange varieties using neural networks and metaheuristic algorithms

Optimization study of vehicle thermal management based on vibration heat dissipation

Protein secondary structure prediction from its amino acids is purposely used to evaluate and improve the accuracy of performance as well as drug design and cell functionality. Various approaches for predicting protein secondary structure have been used, each with varying accuracy, vulnerabilities, and strengths. In view of this, this paper is aimed at training a deep neural network with particle swarm optimization and comparing the results with the state of accuracy. Also, the methodology used is basic particle swarm optimization for training a 20-15-15-15-3 deep neural network. The Java programming language and the Spring Boot framework were employed to implement the various application programming interfaces of the model. The dataset acquired after the training of JPred Server 1.2, which included 1349 training sets and 149 test sets, was used in training the model. Following the training, it was discovered that the model had a highest accuracy of 53.18 percent on epoch 140, indicating that this model is not a best fit or an alternative to the current state of the art for the prediction of protein secondary structure.

To quantitatively research about ethical level of network businesses, we propose the evaluation model of network business's professional ethics based on dynamic weighting method. The model combined BP neural network and particle swarm optimization algorithm (PSO). It can help to get a simulation and prediction network business's professional ethics level by using the practical investigation data. The experiment results show that the algorithm has the advantages of fast convergence speed, high precision and it is an effective assessment of occupation moral score model.

Hydraulic resources can be utilized in a cascade manner, with the advantages of energy conservation, economy, and environmental protection. In order to study the anti salt freezing performance of rock foundation in hydroelectric power dams and accurately predict the degree of rock and soil freeze-thaw damage under salt freezing conditions, this paper proposes a prediction method for the anti salt freezing performance of dam rock foundation based on an improved BP neural network. Firstly, freeze-thaw cycle tests were conducted on the rock foundation materials of dams poured with different proportions of fiber reinforced concrete to study the changes in soil mass loss rate and dynamic elastic modulus; Then, based on BP neural network and particle swarm optimization algorithm, a prediction model for rock and soil freeze-thaw damage is established; Finally, using the historical data of the Baihetan hydropower station, optimization was carried out and model prediction errors were compared and analyzed. The method proposed in this article has been verified to have good accuracy and stability, providing guidance for the operation and construction design of hydropower projects.

Incident angle optimization with multiple conflicting objectives based on BP-MOPSO in oblique laser interferometry

The vibration signals of rolling bearing are often highly nonstationary and nonlinear, and consequently it is not accurate to extract and identify the characteristics of these signals by the traditional methods. In order to improve the performance on the feature extraction from bearing signals and the accuracy of the diagnosis, it requires effective signal processing and diagnose algorithms. In this paper, a new fault diagnosis algorithm which combines complementary ensemble empirical mode decomposition (CEEMD), probabilistic neural network (PNN) and particle swarm optimization (PSO) algorithm optimized by improved linear decreasing weight (LDW) algorithm is proposed. In this method, firstly the vibration signals are decomposed into a number of Intrinsic Mode Functions (IMFs) by the CEEMD algorithm since it has good adaptive ability to nonstable signals and can effectively extract fault features. Then the improved LDWPSO algorithm is introduced to solve the problem that the selection of smoothing factor in PNN model is arbitrary and uncertain. Finally, train and diagnose the fault types of rolling bearing using the LDWPSO-PNN model. The proposed method is verified by the experimental datasets. The results indicate that the method can extract the feature vectors of the vibration signals and distinguish them effectively.

Thin-walled aluminum alloy parts are widely used in the aerospace field because of their favorable characteristics that cater to various applications. However, they are easily deformed during milling, leading to a low pass rate of workpieces. On the basis of on-machine measurement (OMM) and surrogate stiffness models (SSMs), we developed an iterative optimization compensation method in this study to overcome the machining deformation of thin-walled parts. In the error compensation process, the time-varying factors of workpiece stiffness and the impact of prediction model errors were considered. First, we performed machining deformation simulation and information extraction on the key nodes of the machined surface, and an SSM containing the stiffness information of discrete nodes of each cutting layer was established. Subsequently, the machining errors were monitored through intermittent OMM to suppress the adverse impact of prediction model errors. Further, an interlayer correction coefficient was introduced in the compensation process to iteratively correct the prediction model error of each node in the SSM along the depth direction, and a correction coefficient between parts was introduced to realize the iterative correction of the prediction model for the same node position between different parts. Finally, the feasibility of the proposed method was verified through multiple sets of actual machining experiments on thin-walled parts with added pads.

As global warming becomes more prominent, the need to reduce carbon emissions to achieve China's carbon peak target is increasing. It is imperative to seek effective methods to predict carbon emissions and propose targeted emission reduction measures. In this paper, a comprehensive model integrating grey relational analysis (GRA), generalized regression neural network (GRNN) and fruit fly optimization algorithm (FOA) is constructed with carbon emission prediction as the research objective. Firstly, GRA is used for feature selection to find out the factors that have a strong influence on carbon emissions. Secondly, the parameter of GRNN is optimized using FOA algorithm to improve the prediction accuracy. The results show that (1) fossil energy consumption, population, urbanization rate and GDP are important factors affecting carbon emissions; (2) FOA-GRNN outperforms GRNN and back propagation neural network (BPNN), verifying the effectiveness of FOA-GRNN model for CO2 emission prediction. Finally, by analyzing the key influencing factors and combining scenario analysis with forecasting algorithms, the carbon emission trends in China for 2020-2035 are forecasted. The results can provide guidance for policy makers to set reasonable carbon emission reduction targets and adopt corresponding energy saving and emission reduction measures.