scholarly journals Analysis on Food Crispness Based on Time and Frequency Domain Features of Acoustic Signal

2021 ◽  
Vol 38 (1) ◽  
pp. 231-238
Author(s):  
Li Chen ◽  
Jianfeng Ding
Keyword(s):  
Neural Network ◽  
Acoustic Signal ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Analysis Model ◽  
Important Indicator ◽  
Power Spectral ◽  
Instrument Measurement ◽  
Sound Features

Crispness is an important indicator of crunchy food. However, it cannot be easily quantified by sensory evaluation, due to the high subjectivity of evaluators; instrument measurement of this indicator requires much manpower and time. To improve the efficiency of food crispness prediction, this paper attempts to build a rapid, convenient, and accurate crispness analysis model. Starting with the fracturing sound of crunchy food, the authors collected the fracturing acoustic signal, conducted wavelet denoising, analyzed the eigenvalues in time and frequency domains, and constructed crispness prediction models based on multiple linear regression (MLR) and neural network, respectively. Through fracturing test and acoustic test, cluster analysis was adopted to select the typical eigenvalues of acoustic signal, including the peak amplitude of power spectral density (PSD) curve, amplitude difference, and waveform index. Based on these eigenvalues, a crispness analysis model was established, and used to predict the crispness of four kinds of food, namely, potato, sweet potato, carrot, and turnip. The results show that the BP neural network had a smaller relative error than the MLR; when the threshold was 5%, the BP neural network maintained a prediction accuracy of >90%, and achieved 100% prediction accuracy on two types of food. To sum up, this paper reveals the relationship between the food chewing sound features and food quality, laying the theoretical basis for the research of food chewing sound mechanism.

scholarly journals Prediction of the Deformation of Aluminum Alloy Drill Pipes in Thermal Assembly Based on a BP Neural Network

2022 ◽  
Vol 12 (2) ◽  
pp. 757
Author(s):  
Xiaofeng Wang ◽  
Baochang Liu ◽  
Jiaqi Yun ◽  
Xueqi Wang ◽  
Haoliang Bai
Keyword(s):  
Neural Network ◽  
Aluminum Alloy ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Thermal Deformation ◽  
Heating Temperature ◽  
Cooling Water ◽  
Interference Fit ◽  
Steel Joint

The connection between the steel joint and aluminum alloy pipe is the weak part of the aluminum alloy drill pipe. Practically, the interference connection between the aluminum alloy rod and the steel joint is usually realized by thermal assembly. In this paper, the relationship between the cooling water flow rate, initial heating temperature and the thermal deformation of the steel joint in interference thermal assembly was studied and predicted. Firstly, the temperature data of each measuring point of the steel joint were obtained by a thermal assembly experiment. Based on the theory of thermoelasticity, the analytical solution of the thermal deformation of the steel joint was studied. The temperature function was fitted by the least square method, and the calculated value of radial thermal deformation of the section was finally obtained. Based on the BP neural network algorithm, the thermal deformation of steel joint section was predicted. Besides, a prediction model was established, which was about the relationship between cooling water flow rate, initial heating temperature and interference. The magnitude of interference fit of steel joint was predicted. The magnitude of the interference fit of the steel joint was predicted. A polynomial model, exponential model and Gaussian model were adopted to predict the sectional deformation so as to compare and analyze the predictive performance of a BP neural network, among which the polynomial model was used to predict the magnitude of the interference fit. Through a comparative analysis of the fitting residual (RE) and sum of squares of the error (SSE), it can be known that a BP neural network has good prediction accuracy. The predicted results showed that the error of the prediction model increases with the increase of the heating temperature in the prediction model of the steel node interference and related factors. When the cooling water velocity hit 0.038 m/s, the prediction accuracy was the highest. The prediction error increases with the increase or decrease of the velocity. Especially when the velocity increases, the trend of error increasing became more obvious. The analysis shows that this method has better prediction accuracy.

scholarly journals Study on MPGA-BP of Gravity Dam Deformation Prediction

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaoyu Wang ◽  
Kan Yang ◽  
Changsong Shen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Population Genetic ◽  
Prediction Accuracy ◽  
Gravity Dam ◽  
Hydraulic Structures ◽  
Deformation Prediction ◽  
Multiple Population ◽  
Population Genetic Algorithm

Displacement is an important physical quantity of hydraulic structures deformation monitoring, and its prediction accuracy is the premise of ensuring the safe operation. Most existing metaheuristic methods have three problems: (1) falling into local minimum easily, (2) slowing convergence, and (3) the initial value’s sensitivity. Resolving these three problems and improving the prediction accuracy necessitate the application of genetic algorithm-based backpropagation (GA-BP) neural network and multiple population genetic algorithm (MPGA). A hybrid multiple population genetic algorithm backpropagation (MPGA-BP) neural network algorithm is put forward to optimize deformation prediction from periodic monitoring surveys of hydraulic structures. This hybrid model is employed for analyzing the displacement of a gravity dam in China. The results show the proposed model is superior to an ordinary BP neural network and statistical regression model in the aspect of global search, convergence speed, and prediction accuracy.

scholarly journals Prediction of the First Weighting from the Working Face Roof in a Coal Mine Based on a GA-BP Neural Network

2019 ◽  
Vol 9 (19) ◽  
pp. 4159
Author(s):  
Tan ◽  
Yang ◽  
Chang ◽  
Zhao
Keyword(s):  
Neural Network ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Initial Pressure ◽  
Mining Area ◽  
Mine Pressure ◽  
Mine Production ◽  
Production Safety ◽  
Matlab Programming

The accidents caused by roof pressure seriously restrict the improvement of mines and threaten production safety. At present, most coal mine pressure forecasting methods still rely on expert experience and engineering analogies. Artificial neural network prediction technology has been widely used in coal mines. This new approach can predict the surface pressure on the roof, which is of great significance in coal mine production safety. In this paper, the mining pressure mechanism of coal seam roofs is summarized and studied, and 60 sets of initial pressure data from multiple working surfaces in the Datong mining area are collected for gray correlation analysis. Finally, 12 parameters are selected as the input parameters of the model. Suitable back propagation (BP) and GA(genetic algorithm)-BP initial roof pressure prediction models are established for the Datong mining area and trained with MATLAB programming. By comparing the training results, we found that the optimized GA-BP model has a larger determination coefficient, smaller error, and greater stability. The research shows that the prediction method based on the GA-BP neural network model is relatively reliable and has broad engineering application prospects as an auxiliary decision-making tool for coal mine production safety.

scholarly journals Inversion for Damping Ratio of Flat Blade Based on BP Neural Network

Proceedings ◽  
2018 ◽  
Vol 2 (8) ◽  
pp. 547
Author(s):  
Xiamei Zhang ◽  
Shudan Xia
Keyword(s):  
Neural Network ◽  
Decay Time ◽  
Bp Neural Network ◽  
Damping Ratio ◽  
Drop Test ◽  
Analysis Model ◽  
Bp Network ◽  
The Real ◽  
Neural Network Inversion ◽  
The Impact

Aero engine is impacted by foreign objects frequently during daily usage, including runway gravel, birds, fuselage components and so on, so the fan and compressor may damage, resulting in serious air crash. Thus, simulating the impact of blades and establishing the numerical analysis model of dynamic response demand immediate attention. In the analysis model, damping coefficient is one of the most important physical parameters of the blade structure and cannot be directly measured. Rayleigh damping is widely applied and can be converted to direct modal damping in ABAQUS. BP neural network is a multi-layer feedforward neural network using back propagation algorithm to adjust the network weights. It can be proved that there exists a three-layer BP network to realize the mapping of arbitrary continuous functions with arbitrary precision. In this study, a novel method for obtaining the damping ratio of the flat blade which applies BP neural network inversion is proposed. In order to demonstrate this method, a simplified experiment was conducted. Firstly, fix a section of aluminum plate and then conduct two set of drop tests on different positions with different impact velocities by a steel ball. At the same time, vibration response was recorded by displacement sensor. Secondly, establish a finite element model using ABAQUS to simulate the drop test. Adopt twenty groups of models with different damping ratio and then obtain their amplitudes and decay time, respectively. Thirdly, train a BP neural network using MATLAB program and then establish the mapping relationship between amplitude, decay time and damping ratio. Fourth, a set of experimental amplitude and decay time is substituted into the previously obtained BP neural network mapping model, and then the real damping ratio is obtained by inference. Finally, the real damping ratio is applied to the flat blade impact simulation of the other set of drop test for validation. The numerical results are consistent with the experimental data, which indicates that the damping ratio obtained by BP neural network inversion is reasonable and reliable.

A Model Based on BP Neural Network for Audible Noise Prediction

2014 ◽  
Vol 986-987 ◽  
pp. 1356-1359
Author(s):  
You Xian Peng ◽  
Bo Tang ◽  
Hong Ying Cao ◽  
Bin Chen ◽  
Yu Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Transmission Lines ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Absolute Error ◽  
Research Area ◽  
Noise Prediction ◽  
Bp Network ◽  
Model Based

Audible noise prediction is a hot research area in power transmission engineering in recent years, especially come down to AC transmission lines. The conventional prediction models at present have got some problems such as big errors. In this paper, a prediction model is established based on BP network, in which the input variables are the four factors in the international common expression of power line audible noise and the noise value is the output. Take multiple measured power lines as an example, a train is made by the BP network and then the prediction model is set up in the hidden layer of the network. Using the trained model, the audible noise values are predicted. The final results show that the average absolute error in absolute terms of the values by the audible noise prediction model based on BP neural network is 1.6414 less than that predicted by the GE formula.

scholarly journals Optimized Erosion Prediction with MAGA Algorithm Based on BP Neural Network for Submerged Low-Pressure Water Jet

2020 ◽  
Vol 10 (8) ◽  
pp. 2926
Author(s):  
Yanzhen Chen ◽  
Yihuai Hu ◽  
Shenglong Zhang ◽  
Xiaojun Mei ◽  
Qingguo Shi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Water Jet ◽  
Erosion Prediction ◽  
Pressure Water ◽  
Erosion Test ◽  
Bp Model

In order to accurately predict the erosion effect of underwater cleaning with an angle nozzle under different working conditions, this paper uses refractory bricks to simulate marine fouling as the erosion target, and studies the optimized erosion prediction model by erosion test based on the submerged low-pressure water jet. The erosion test is conducted by orthogonal experimental design, and experimental data are used for the prediction model. By combining with statistical range and variance analysis methods, the jet pressure, impact time and jet angle are determined as three inputs of the prediction model, and erosion depth is the output index of the prediction model. A virtual data generation method is used to increase the amount of input data for the prediction model. This paper also proposes a Mind-evolved Advanced Genetic Algorithm (MAGA), which has a reliable optimization effect in the verification of four stand test functions. Then, the improved back-propagating (BP) neural network prediction models are established by respectively using Genetic Algorithm (GA) and MAGA optimization algorithms to optimize the initial thresholds and weights of the BP neural network. Compared to the prediction results of the BP and GA-BP models, the R2 of the MAGA-BP model is the highest, reaching 0.9954; the total error is reduced by 47.31% and 35.01%; the root mean square error decreases by 51.05% and 31.80%; and the maximum absolute percentage error decreases by 65.79% and 64.01%, respectively. The average prediction accuracy of the MAGA-BP model is controlled within 3%, which has been significantly improved. The results show that the prediction accuracy of the MAGA-BP prediction model is higher and more reliable, and the MAGA algorithm has a good optimization effect. This optimized erosion prediction method is feasible.

scholarly journals Application of the Artificial Neural Network and Support Vector Machines in Forest Fire Prediction in the Guangxi Autonomous Region, China

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yudong Li ◽  
Zhongke Feng ◽  
Shilin Chen ◽  
Ziyu Zhao ◽  
Fengge Wang
Keyword(s):  
Neural Network ◽  
Support Vector Machines ◽  
Forest Fire ◽  
Bp Neural Network ◽  
Forest Fires ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Autonomous Region ◽  
Svm Model ◽  
Vector Machines

The study of forest fire prediction is of great environmental and scientific significance. China’s Guangxi Autonomous Region has a high incidence rate of forest fires. At present, there is little research on forest fires in this area. The application of the artificial neural network and support vector machines (SVM) in forest fire prediction in this area can provide data for forest fire prevention and control in Guangxi. In this paper, based on Guangxi’s 2010–2018 satellite monitoring hotspot data, meteorology, terrain, vegetation, infrastructure, and socioeconomic data, the researchers determined the main forest fire driving factors in Guangxi. They used feature selection and backpropagation neural networks and radial basis SVM to build forest fire prediction models. Finally, the researchers use the accuracy, precision, and area under the characteristic curve (ROC-AUC) and other indicators to evaluate the predictive performance of the two models. The results showed that the prediction accuracy of the BP neural network and SVM is 92.16% and 89.89%, respectively. As both results are over 85%, the requirements of prediction accuracy is met. These results can be used for forest fire prediction in the Guangxi Autonomous Region. Specifically, the accuracy of the BP neural network was 0.93, which was higher than that of the SVM model (0.89); the recall of the SVM model was 0.84, which was lower than the BANN model (0.92), and the AUC value of the SVM model was 0.95, which was lower than the BP neural network model. The obtained results confirm that the BP neural network model can provide more prediction accuracy than support vector machines and is therefore more suitable for forest fire prediction in Guangxi, China. This research provides the necessary theoretical basis and data support for application in the field of forestry of the Guangxi Autonomous Region, China.

scholarly journals An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Wei He
Keyword(s):  
Neural Network ◽  
Supply Chain ◽  
Convergence Rate ◽  
Inventory Control ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Inventory Level ◽  
Bp Neural Network Model

Inventory control is a key factor for reducing supply chain cost and increasing customer satisfaction. However, prediction of inventory level is a challenging task for managers. As one of the widely used techniques for inventory control, standard BP neural network has such problems as low convergence rate and poor prediction accuracy. Aiming at these problems, a new fast convergent BP neural network model for predicting inventory level is developed in this paper. By adding an error offset, this paper deduces the new chain propagation rule and the new weight formula. This paper also applies the improved BP neural network model to predict the inventory level of an automotive parts company. The results show that the improved algorithm not only significantly exceeds the standard algorithm but also outperforms some other improved BP algorithms both on convergence rate and prediction accuracy.

Study on Wind Speed Forecasting Based on STC and BP Neural Network

2013 ◽  
Vol 724-725 ◽  
pp. 623-629
Author(s):  
Xing Jie Liu ◽  
Wen Shu Zheng ◽  
Tian Yun Cen
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Wind Farm ◽  
Target Prediction ◽  
Temporal Correlation ◽  
Economic Security ◽  
Wind Speed Forecasting ◽  
Spatio Temporal

Accurate wind speed forecasting of wind farm is of great significance in economic security and stability of the grid. In order to improve the prediction accuracy, the paper first proposed a spatio-temporal correlation predictor method. Based on physical characteristics of wind speed evolution, the method looked for the wind speed and direction information at sites close to the target prediction site, and established STCP model to forecast. And then we established the BP neural network to finish multi-step forecast with wind speed time series of target forecast site .Last, two methods were combined to form STCP-BP method. Simulation tests are conducted with operation data from certain wind farm group in China and results show that STCP-BP method can effectively improve the prediction accuracy compared with BP model.

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