An adaptive generation method for free curve trajectory based on NURBS

To realize the high precision and real-time interpolation of the NURBS （non-uniform rational B-spline） curve, a kinetic model based on the modified sigmoid function is proposed. The constraints of maximum feed rate, chord error, curvature radius and interpolator cycle are discussed. This kinetic model reduces the cubic polynomial S-shape model and the trigonometry function S-shape model from 15 sections into 3 sections under the precondition of jerk, acceleration and feedrate continuity. Then an optimized Adams algorithm using the difference quotient to replace the derivative is presented to calculate the interpolator cycle parameters. The higher-order derivation in the Taylor expansion algorithm can be avoided by this algorithm. Finally, the simplified design is analyzed by reducing the times of computing the low-degree zero-value B-spline basis function and the simplified De Boor-Cox recursive algorithm is proposed. The simulation analysis indicates that by these algorithms, the feed rate is effectively controlled according to tool path. The calculated amount is decreased and the calculated speed is increased while the machining precision is ensured. The experimental results show that the target parameter can be correctly calculated and these algorithms can be applied to actual systems.

Self-correcting wavelet neural network control of continuous rotary electro-hydraulic servo motor

In allusion to the problem of friction,leakage,vibration and noise existing in continuous rotary motor electro-hydraulic servo system,highly nonlinearity and uncertainties affecting the system performance,based on the transfer function of electro-hydraulic servo system,a kind of Pol-Ind friction model is proposed.The parameters of Pol-Ind friction model are identified and the accurate mathematical model of friction torque is obtained by experiment.The self-correcting wavelet neural network(WNN)controller is proposed,and Adam optimization algorithm is used to perform gradient optimization on scale factor and displacement factor in wavelet basis function,so as to improve the speed and precision of parameter optimization.Through comparative simulation analysis,it is clearly that the self-correcting WNN controller can effectively improve the frequency response and tracking accuracy of continuous rotary motor electro-hydraulic servo system.

Research on classification diagnosis model of psoriasis based on deep residual

Objective A classification and diagnosis model for psoriasis based on deep residual network is proposed in this paper.Which using deep learning technology to classify and diagnose psoriasis can help reduce the burden of doctors,simplify the diagnosis and treatment process,and improve the quality of diagnosis.Methods Firstly,data enhancement,image resizings,and TFRecord coding are used to preprocess the input of the model,and then a 34-layer deep residual network(ResNet-34)is constructed to extract the characteristics of psoriasis.Finally,we used the Adam algorithm as the optimizer to train ResNet-34,used cross-entropy as the loss function of ResNet-34 in this study to measure the accuracy of the model,and obtained an optimized ResNet-34 model for psoriasis diagnosis.Results The experimental results based on k-fold cross validation show that the proposed model is superior to other diagnostic methods in terms of recall rate,F1-score and ROC curve.Conclusion The ResNet-34 model can achieve accurate diagnosis of psoriasis,and provide technical support for data analysis and intelligent diagnosis and treatment of psoriasis.

CNN-based Traffic Sign Recognition

Background:The rapid development of the automobile industry has led to an increase in the output and holdings of automobiles year by year,which has brought huge challenges to the current traffic management.Method:This paper adopts a traffic sign recognition technology based on deep convolution neural network(CNN):step 1,preprocess the collected traffic sign images through gray processing and near interpolation;step 2,automatically extract image features through the convolutional layer and the pooling layer;step 3,recognize traffic signs through the fully connected layer and the Dropout technology.Purpose:Artificial intelligence technology is applied to traffic management to better realize intelligent traffic assisted driving.Results:This paper adopts an Adam optimization algorithm for calculating the loss value.The average accuracy of the experimental classification is 98.87%.Compared with the traditional gradient descent algorithm,the experimental model can quickly converge in a few iteration cycles.

Prediction of Health Level of Multiform Lithium Sulfur Batteries Based on Incremental Capacity Analysis and an Improved LSTM

Capacity estimation plays a crucial role in battery management systems,and is essential for ensuring the safety and reliability of lithium-sulfur(Li-S)batteries.This paper proposes a method that uses a long short-term memory(LSTM)neural network to estimate the state of health(SOH)of Li-S batteries.The method uses health features extracted from the charging curve and incre-mental capacity analysis(ICA)as input for the LSTM network.To enhance the robustness and accuracy of the network,the Adam algorithm is employed to optimize specific hyperparameters.Experimental data from three different groups of batteries with varying nominal capac-ities are used to validate the proposed method.The results demonstrate the effectiveness of the method in accurately estimating the capacity degradation of all three batteries.Also,the study examines the impact of different lengths of network training sets on capacity estimation.The results reveal that the ICA-LSTM model achieves a prediction accuracy of mean absolute error 4.6%and mean squared error 0.21%with three different training set lengths of 20%,40%,and 60%.The analysis demonstrates that the lightweight model maintains high SOH estimation accu-racy even with a small training set,and exhibits strong adaptive and generalization capabilities when applied to different Li-S batteries.Overall,the proposed method,supported by experimental validation and analysis,demonstrates its efficacy in ensuring accurate and reliable SOH estimation,thereby enhancing the safety and per-formance of Li-S batteries.Index Terms—Adam algorithm,incremental capacity analysis,Li-S battery,long short-term memory,state of health.

A new dust detection method for photovoltaic panel surface based on Pytorch and its economic benefit analysis

Dust accumulation on the surface of solar photovoltaic panels diminishes their power generation efficiency,leading to reduced energy generation.Regular monitoring and cleaning of solar photovoltaic panels is essential.Thus,developing optimal procedures for their upkeep is crucial for improving component efficiency,reducing maintenance costs,and conserving resources.This study introduces an improved Adam optimization algorithm designed specifically for detecting dust on the surface of solar photovoltaic panels.Although the traditional Adam algorithm is the preferred choice for optimizing neural network models,it occasionally encounters problems such as local optima,overfitting,and not convergence due to inconsistent learning rates during the optimization process.To mitigate these issues,the improved algorithm incorporates Warmup technology and cosine annealing strategies with traditional Adam algorithm,that allows for a gradual increase in the learning rate,ensuring stability in the preliminary phases of training.Concurrently,the improved algorithm employs a cosine annealing strategy to dynamically tweak the learning rate.This not only counters the local optimization issues to some degree but also bolsters the generalization ability of the model.When applied on the dust detection on the surface of solar photovoltaic panels,this improved algorithm exhibited superior convergence and training accuracy on the surface dust detection dataset of solar photovoltaic panels in comparison to the standard Adam method.Remarkably,it displayed noteworthy improvements within three distinct neural network frameworks:ResNet-18,VGG-16,and MobileNetV2,thereby attesting to the effectiveness of the novel algorithm.These findings hold significant promise and potential applications in the field of surface dust detection of solar photovoltaic panels.These research results will create economic benefits for enterprises and individuals,and are an important strategic development direction for the country.