Series Arc Fault Identification Method Based on Multi-Feature Fusion

With the increase of various loads connected to the low-voltage distribution system, the difficulty of identifying low-voltage series fault arcs has greatly increased, which seriously threatens the electricity safety. Aiming at such problems, a neural network algorithm based on multi-feature fusion is proposed. The fault current has the characteristics of randomness, high frequency noise, and singularity. A GA-BP neural network model is built, and the wavelet analysis method (based on singularity), Fourier transform method (based on high frequency noise), current cycle difference method (based on randomness), and current cycle similarity derivation method (based on randomness) are used for feature extraction and can more comprehensively reflect the characteristics of arc faults. Simulation results show that the multi-feature fusion algorithm has a higher recognition rate than other algorithms. Moreover, compared with the support vector machine model, logistic regression model, and AlexNet model, the GA-BP neural network model has a higher recognition accuracy than the other three models, which can reach 99%.

Demand Forecast on the Orchard Cold Chain Logistics in Hunan Province Based on Cognitive Neuroscience

How to scientifically and effectively predict the cold chain logistics demand and provide basis for decision making has always been the focus of forestry and orchard logistics research. From the learning environment of neurons, cognitive neuroscience provides a new perspective for forecasting the demand for cold chain logistics. This paper uses the cognitive neuroscience theory to construct a BP neural network model containing two hidden layers to predict the cold chain logistics demand of the forestry and orchard industry in Hunan province in 2017-2021. Suggestions are then given from the aspects of cold chain logistics construction, transportation infrastructure construction, government policy, enterprise and industry according to the prediction results, thus, providing a theoretical basis for the planning of the cold chain logistics system of Hunan province in a certain period of time, as well as references for the development of cold chain logistics in other parts of the country.

Demand Forecast on the Orchard Cold Chain Logistics in Hunan Province Based on Cognitive Neuroscience

How to scientifically and effectively predict the cold chain logistics demand and provide basis for decision making has always been the focus of forestry and orchard logistics research. From the learning environment of neurons, cognitive neuroscience provides a new perspective for forecasting the demand for cold chain logistics. This paper uses the cognitive neuroscience theory to construct a BP neural network model containing two hidden layers to predict the cold chain logistics demand of the forestry and orchard industry in Hunan province in 2017-2021. Suggestions are then given from the aspects of cold chain logistics construction, transportation infrastructure construction, government policy, enterprise and industry according to the prediction results, thus, providing a theoretical basis for the planning of the cold chain logistics system of Hunan province in a certain period of time, as well as references for the development of cold chain logistics in other parts of the country.

Prediction of PM2.5 concentration based on BP neural network optimized by bee colony algorithm

According to the ambient air pollutants data and meteorological conditions data of Mianyang City in 2017, the BP neural network model based on MATLAB is established to predict the daily average PM2.5 concentration of Mianyang City in the next two days. However, the traditional BP network has the disadvantages of slow convergence speed and easy to fall into local optimum. In order to improve the prediction accuracy of the model, an optimization algorithm is added to the prediction model to avoid the model falling into local minimum. In this paper, the bee colony algorithm is added to the prediction model to improve the accuracy of BP neural network prediction model. The data from January to November are used for training, and the data from December are used as the verification results. The results show that the optimization model can accurately predict the daily average PM2.5 concentration of Mianyang City in the next two days, which provides a new idea for the prediction of PM2.5 concentration of the city, provides a theoretical basis for the early warning and decision-making of air pollution, and also provides more reliable prediction services for people’s daily travel.

Deformation Prediction of a Deep Foundation Pit Based on the Combination Model of Wavelet Transform and Gray BP Neural Network

The purpose of this study was to predict the deformation of a deep foundation pit based on a combination model of wavelet transform and gray BP neural network. Using a case of a deep foundation pit, a combination model of wavelet transform and gray BP neural network was used to predict the deformation of the deep foundation pit. The results show that compared with the traditional gray BP neural network model, the relative error of the combination model of wavelet transform and gray BP neural network was reduced by 2.38%. This verified that the combined model has high accuracy and reliability in the prediction of foundation pit deformation and also conforms to the actual situation of the project. The research results can provide a valuable reference for foundation pit deformation monitoring.

The Prediction of Atherosclerosis Index Based on Photoplethysmograph

Current atherosclerosis (AS) assessment devices have a disadvantage for users to carry around. In response to this shortcoming, we propose to collect the wrist photoplethysmograph (PPG) signal and create models to predict the indicators of atherosclerosis (cardiovascular age and right brachial and ankle pulse wave velocity (baPWV)). This study uses the maximum correlation coefficient method for feature selection and establishes multiple models to predict cardiovascular age and the right baPWV. The study results show that the prediction of cardiovascular age using the backpropagation (BP) neural network model is the best. Its Pearson correlation coefficient (PCC) is 0.9501 ( P < 0.05 ), and the model finds the best six physiological features as crest time (CT), crest time ratio (CTR), slop K, stiffness index (SI), reflection index (RI), and heart rate (HR). When predicting the right baPWV value on the right side, we propose a hybrid method MLR_BP, which has better experimental results than BP and MLR. The MLR_BP model improves the prediction accuracy, the predicted PCC value is 0.9204 ( P < 0.05 ), and the model only needs two features, HR and cardiovascular age. This study further verified the results of related literature and proved the relationship between AS and related physiological parameters. The proposed method is applied to wearable devices and has an application value for diagnosing AS and preventing cardiovascular diseases.

Selection Technology of Deep Leak Proof Drilling Fluid in Changsheng Block Based on BP Grey Wolf Neural Network

In this paper, a BP neural network model with 5-9-6 structure is constructed according to various fault gases and different fault types of drilling fluid. On the basis of BP neural network model, genetic algorithm and gray wolf algorithm are used to optimize the threshold and weight of neural network. By applying the system to Horizontal Well Changshen #5 on site used Grey Wolf algorithm, lost circulation problem in the horizontal section of the reservoir was effectively controlled, ensuring rapid and safe drilling operation.