scholarly journals Monitoring Method of Total Seed Mass in a Vibrating Tray Using Artificial Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3659 ◽  
Author(s):  
Zhan Zhao ◽  
Fang Qin ◽  
Chun-Jie Tian ◽  
Simon Yang
Keyword(s):  
Neural Network ◽  
Relative Error ◽  
Back Propagation ◽  
Seed Mass ◽  
Force Sensor ◽  
Monitoring Method ◽  
Four Corners ◽  
Measurement Results ◽  
Total Seed ◽  
The Impact

To maintain the continuous working performance of a vacuum plate seeder, it is important to monitor the total seed mass in the seed tray in real time and accurately control the pickup position of the suction plate accordingly. Under the excitation of reciprocating vibration varying with time and interference by direction angle, the motion of seeds in a rectangular tray was simulated using the discrete element method (DEM). A measurement method for seed mass in a small area was proposed based on the impulse theorem. The impact force of seeds was monitored with a cantilever force sensor, and the corresponding signal processing circuit was designed. Calibration results indicated that the relative nonlinear error was less than 2.3% with an average seeds-mass-per-unit-area (SMA) of 0.3–2.4 g/cm2. Then, four sets of force sensors were installed symmetrically near the four corners of the vibrating tray which were used to measure the SMA respectively. Back propagation (BP) neural networks which take four SMA measurement results as input parameters were developed to monitor the total seed mass in the tray. Monitoring results using DEM simulation data showed that the general relative error was 3.0%. Experiments were carried out on a test-rig and the results validated that the relative error was reduced to 5.0% by using the BP neural network method.

scholarly journals Method of Predicting Ore Dilution Based on a Neural Network and Its Application

2020 ◽  
Vol 12 (4) ◽  
pp. 1550 ◽  
Author(s):  
Xingdong Zhao ◽  
Jia’an Niu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Relative Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Average Deviation ◽  
Graph Method ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
The Difference

A back-propagation neural network prediction model with three layers and six neurons in the hidden layer is established to overcome the limitation of the equivalent linear overbreak slough (ELOS) empirical graph method in estimating unplanned ore dilution. The modified stability number, hydraulic radius, average deviation of the borehole, and powder factor are taken as input variables and the ELOS of quantified unplanned ore dilution as the output variable. The training and testing of the model are performed using 120 sets of data. The average fitting degree r2 of the prediction model is 0.9761, the average mean square error is 0.0001, and the relative error of the prediction is approximately 6.2%. A method of calculating the unplanned ore dilution is proposed and applied to a test stope of the Sandaoqiao lead–zinc mine. The calculated unplanned ore dilution is 0.717 m, and the relative error (i.e., the difference between calculation and measurement of 0.70 m) is 2.4%, which is better than the relative errors for the empirical graph method and numerical simulation (giving dilution values of 0.8 and 0.55 m, respectively). The back-propagation neural network prediction model is confirmed to predict the unplanned ore dilution in real applications.

scholarly journals Importance Degree Evaluation of Spare Parts Based on Clustering Algorithm and Back-Propagation Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Shoujing Zhang ◽  
Xiaofan Qin ◽  
Sheng Hu ◽  
Qing Zhang ◽  
Bochao Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Inventory Management ◽  
Clustering Algorithm ◽  
Back Propagation ◽  
Spare Part ◽  
Back Propagation Neural Network ◽  
Spare Parts ◽  
Training Data ◽  
Importance Value ◽  
The Impact

The quantitative evaluation of the importance degree of spare parts is essential as spare parts’ maintenance is critical for inventory management. Most of the methods used in previous research are subjective. For this reason, an accurate method for the evaluation of the importance degree combining an improved clustering algorithm with a back-propagation neural network (BPNN) is proposed in the present paper. First, we classified the spare parts by analyzing their historical maintenance and inventory data. Second, we evaluated the effectiveness of classification using the Davies–Bouldin index and the Calinski–Harabasz indicator and verified it using the training data. Finally, we used BPNN to determine the training data necessary for an accurate assessment of the importance degree of spare parts. The previous importance evaluation methods were susceptible to subjective factors during the evaluation process. The model established in this paper used the actual data of the company for machine learning and used the improved clustering algorithm to implement training and classification of spare parts data. The importance value of each spare part was output, which additionally reduced the impact of subjective factors on the importance evaluation. At the same time, the use of less data to evaluate the importance of spare parts was achieved, which improved the evaluation efficiency.

NC Ultrasonic Machining Efficiency: Neural Network-Based Modeling and Simulation

2011 ◽  
Vol 291-294 ◽  
pp. 406-410 ◽  
Author(s):  
Gao Yan Zhong ◽  
Yan Qing Wang
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Machining Process ◽  
Feed Speed ◽  
Machining Efficiency ◽  
Ultrasonic Machining ◽  
Bp Network ◽  
Feed Pressure ◽  
Two Samples ◽  
The Impact

To explore the impact of abrasive granularity, feed pressure and cutting feed speed on NC ultrasonic machining efficiency, a technological test was carried out, and based on the test results, back propagation (BP) neural network model was established and validated by simulation. The validation process showed that when relative error is less than ±10%, only two samples among 18 tested have larger errors. By the utilization of the BP network for training, correct fitting rate of machining efficiency target can be reached up to 88.9%. Our study indicates that (i) the output of the network is well fitted with the test data, (ii) the established model has good generalization ability to reflect the laws of NC ultrasonic machining process, and (iii) the model is suitable as a prediction tool for NC ultrasonic machining efficiency.

Natural Gas Load Forecasting Based on Improved Back Propagation Neural Network

2014 ◽  
Vol 563 ◽  
pp. 312-315
Author(s):  
Yu Lian Jiang
Keyword(s):  
Neural Network ◽  
Natural Gas ◽  
Relative Error ◽  
Load Forecasting ◽  
Back Propagation ◽  
Absolute Error ◽  
Back Propagation Neural Network ◽  
Back Propagation Algorithm ◽  
Traditional Algorithm ◽  
Improved Algorithm

To suit for the condition that the relative error is more popular than the absolute error, and overcome the shortcoming of the traditional Back propagation neural network, this paper proposed an improved Back propagation algorithm with additional momentum item based on the sum of relative error square. The improved algorithm was applied to the example of the natural gas load forecasting, simulations showed that the improved algorithm has faster training speed than the traditional algorithm, and has higher accuracy as while.

Neural Network Processing of Optical Fiber Sensor Signals for Impact Location

1994 ◽  
Vol 360 ◽  
Author(s):  
Paul M. Schindler ◽  
John K. Shaw ◽  
Russell G. May ◽  
Richard O. Claus
Keyword(s):  
Neural Network ◽  
Optical Fiber Sensor ◽  
Back Propagation ◽  
Strain Sensors ◽  
Time Data ◽  
Impact Location ◽  
Fabry Perot ◽  
Sensor Signals ◽  
Propagation Network ◽  
The Impact

AbstractA system to detect and locate impacts by foreign bodies on a surface was developed and tested. Fiber optic extrinsic Fabry-Perot interferometer (EFPI) strain sensors were attached to or embedded in the surface, so that stress waves emanating from an impact could be detected. By employing an artificial neural network to process the sensor outputs, the impact location could be inferred to centimeter range accuracy directly from the arrival time data. In particular, the network could be trained to determine impact location regardless of material anisotropy. Results demonstrate that a back-propagation network identifiesimpact location for an anisotropic graphite/bismaleimide plate with the same accuracy as that for an isotropic aluminum plate.

Predicting Flow Stress of Four Types of Microalloyed Forging Steel at High Temperature by Artificial Neural Network Model

2017 ◽  
Vol 729 ◽  
pp. 75-79
Author(s):  
Hu Sen Jiang ◽  
Jin Wang ◽  
Li Hua Li ◽  
Hai Tao Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Temperature ◽  
Flow Stress ◽  
Constitutive Model ◽  
Relative Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Artificial Neural ◽  
Input Variables

Artificial neural network (ANN) gets a lot of applications in predicting flow stress of steels at high temperature. However, few studies have been devoted to simultaneously predict flow stress of several steels by ANN. The purpose of this paper is to determine the effect of ANN on simultaneously predicting flow stress of several steels. Based on the results of previous compression experiments of four types of microalloyed forging steel, using the mass percentage of major chemical composition of the steels, such as as C, Mn, Si and V, and deformation temperature, strain rate and strain as input variables, a three-layers back propagation neural network was established as the constitutive model for them. Standard statistical methods were employed to quantitatively measure the accuracy of predicted results by the model. The calculated correlation coefficient and the average relative error absolute value between the predicted values by the model and experimental values were 0.9982 and 2.4181%, respectively. In addition, the relative error between the two kinds of values was calculated, and for more than 89% samples, the relative error was within ± 5%. The results show that the developed constitutive model can predict the flow stress of the four types of microalloyed forging steel accurately and simultaneously.

scholarly journals Power Loss Prediction for Aging Characteristics and Condition Monitoring for Parallel-Connected Power Modules Using Nonlinear Autoregressive Neural Network

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Shengyou Xu ◽  
Xin Yang ◽  
Li Ran ◽  
Minyou Chen ◽  
Wei Lai
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Condition Monitoring ◽  
Power Loss ◽  
Junction Temperature ◽  
Monitoring Method ◽  
Current Sharing ◽  
Power Modules ◽  
The Impact ◽  
Nonlinear Autoregressive

Power modules connected in parallel may have different electrothermal performance variances resulting from aging because of the nonuniform rate of degradation; different electrothermal performance variances mean different current sharing, different junction temperature, and power losses, which will directly influence the overall characteristics of them. Thus, it is essential to monitor the condition and evaluate the degradation grade to improve the reliability of large-scale power modules. In this paper, the impact of thermal resistance difference on current sharing, junction temperature, and power loss of parallel-connected power modules has been discussed and analyzed. Additionally, a methodology is proposed for condition monitoring and evaluation of the power modules without intruding them by recognizing the increase in external power loss due to internal degradation from aging. In this method, power modules are deemed as a whole system considering only external factors associated with them, all important electrical and thermal parameters are classified as the inputs, and power loss is considered as the output. Firstly, power dissipation is predicted by models using NARX (nonlinear autoregressive with exogenous input) neural network. Then, a monitoring method is illustrated based on the prediction model; a reasonable criterion for the error between the normal and the predicted real-time power loss is established. Finally, the real-time condition and the degradation grade of aging can be evaluated so that the operator can take suitable operating measures by means of this approach. Experimental results validated the effectiveness of the proposed methodology.

scholarly journals Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network

2020 ◽  
Vol 17 (20) ◽  
pp. 7466
Author(s):  
Arshad Jamal ◽  
Waleed Umer
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Injury Severity ◽  
Back Propagation ◽  
Model Performance ◽  
Mitigation Strategies ◽  
Feed Forward Neural Network ◽  
Crash Data ◽  
The Impact ◽  
Crash Injury Severity

A better understanding of circumstances contributing to the severity outcome of traffic crashes is an important goal of road safety studies. An in-depth crash injury severity analysis is vital for the proactive implementation of appropriate mitigation strategies. This study proposes an improved feed-forward neural network (FFNN) model for predicting injury severity associated with individual crashes using three years (2017–2019) of crash data collected along 15 rural highways in the Kingdom of Saudi Arabia (KSA). A total of 12,566 crashes were recorded during the study period with a binary injury severity outcome (fatal or non-fatal injury) for the variable to be predicted. FFNN architecture with back-propagation (BP) as a training algorithm, logistic as activation function, and six number of hidden neurons in the hidden layer yielded the best model performance. Results of model prediction for the test data were analyzed using different evaluation metrics such as overall accuracy, sensitivity, and specificity. Prediction results showed the adequacy and robust performance of the proposed method. A detailed sensitivity analysis of the optimized NN was also performed to show the impact and relative influence of different predictor variables on resulting crash injury severity. The sensitivity analysis results indicated that factors such as traffic volume, average travel speeds, weather conditions, on-site damage conditions, road and vehicle type, and involvement of pedestrians are the most sensitive variables. The methods applied in this study could be used in big data analysis of crash data, which can serve as a rapid-useful tool for policymakers to improve highway safety.

scholarly journals Evaluation of Normalization Techniques on Neural Networks for the Prediction of 305-Day Milk Yield

2020 ◽  
pp. 354-367
Author(s):  
Asli AKILLI ◽  
Hulya ATIL
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Milk Yield ◽  
Conjugate Gradient ◽  
Network Architecture ◽  
Back Propagation ◽  
The Neural Network ◽  
Validation Set ◽  
The Impact ◽  
Quasi Newton

In this study, the impact of data preprocessing on the prediction of 305-day milk yield using neural networks were investigated with regard to the effect of different normalization techniques. Eight normalization techniques “Z-Score, Min-Max, D-Min-Max, Median, Sigmoid, Decimal Scaling, Median and MAD, TanhEstimators" and five different back propagation algorithms “Levenberg-Marquardt (LM), Bayesian Regularization (BR), Scaled Conjugate Gradient (SCG), Conjugate Gradient Back propagation with Powell-Beale Restarts (CGB) and Brayde Fletcher Gold Farlo Shanno Quasi Newton Back propagation (BFG)” were examined and tested comparatively for the analysis. Neural network architecture was optimized and tested with several experiments. Results of the analysis show that applying different normalization techniques affect the performance and the distribution of outputs influences the learning process of the neural network. The magnitude of the effects varied with the type of back propagation algorithms, activation functions, and network's architectural structure. According to the results of the analysis, the most successful performance value in the 305-day milk yield estimation was obtained by using the neural network structured by using the Decimal Scaling normalization technique with the Bayesian Regulation algorithm (R2Adj = 0.8181, RMSE= 0.0068, MAPE= 160.42 for test set; R2Adj =0.8141, RMSE= 0.0067, MAPE= 114.12 for validation set).

scholarly journals Embedded FBG Sensor Based Impact Identification of CFRP Using Ensemble Learning

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1452
Author(s):  
Jun Li ◽  
Yinghong Yu ◽  
Xinlin Qing
Keyword(s):  
Neural Network ◽  
Ensemble Learning ◽  
Composite Structures ◽  
Back Propagation ◽  
Support Vector ◽  
Time Of Arrival ◽  
Neural Network Ensemble ◽  
Monitoring Method ◽  
Reinforced Plastics ◽  
Fbg Sensors

Impact brings great threat to the composite structures that are extensively used in an aircraft. Therefore, it is necessary to develop an accurate and reliable impact monitoring method. In this paper, fiber Bragg grating (FBG) sensors are embedded in unidirectional carbon fiber reinforced plastics (CFRPs) during the manufacturing process to monitor the strain that is related to the elastic modulus and the state of resin. After that, an advanced impact identification model is proposed. Support vector regression (SVR) and a back propagation (BP) neural network are combined appropriately in this stacking-based ensemble learning model. Then, the model is trained and tested through hundreds of impacts, and the corresponding strain responses are recorded by the embedded FBG sensors. Finally, the performances of different models are compared, and the influence of the time of arrival (ToA) on the neural network is also explored. The results show that compared with a single neural network, ensemble learning has a better capability in impact identification.

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