scholarly journals Design on the Winter Jujubes Harvesting and Sorting Device

2019 ◽  
Vol 9 (24) ◽  
pp. 5546 ◽  
Author(s):  
Ni ◽  
Zhang ◽  
Zhao ◽  
Wang ◽  
Lv ◽  
...  
Keyword(s):  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Element Analysis ◽  
Vibration Displacement ◽  
Evaluation Standard ◽  
Trunk Diameter ◽  
Existing Problems ◽  
Vibration Mechanism ◽  
Jujube Tree ◽  
The Impact

According to the existing problems of winter jujube harvesting, such as the intensive labor of manual picking, damage to the surface of winter jujubes, a winter jujube harvesting and sorting device was developed. This device consisted of vibration mechanism, collection mechanism, and sorting mechanism. The eccentric vibration mechanism made the winter jujubes fall, and the umbrella collecting mechanism can collect winter jujube and avoid the impact of winter jujube on the ground, and the sorting mechanism removed jujube leaves and divided the jujube into two types, and the automatic leveling mechanism made the device run smoothly in the field. Through finite element analysis and BP (Back Propagation) neural network analysis, the results show that: The vibration displacement of jujube tree is related to the trunk diameter and vibration position; the impact force of winter jujubes falling is related to the elastic modulus of umbrella material; the collecting area can be increased four times for each additional step of the collection mechanism; jujube leaves can be effectively removed when blower wind speed reaches 45.64 m/s. According to the evaluation standard grades of the jujubes harvesting and sorting, the device has good effects and the excellent rate up to 90%, which has good practicability and economy.

scholarly journals Vibration Reliability Analysis of Drum Brake Using the Artificial Neural Network and Important Sampling Method

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhou Yang ◽  
Unsong Pak ◽  
Cholu Kwon
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reliability Analysis ◽  
Sampling Method ◽  
Back Propagation ◽  
Simulation Method ◽  
Back Propagation Neural Network ◽  
Element Analysis ◽  
Analysis Methods ◽  
Artificial Neural

This research aims to evaluate the calculation accuracy and efficiency of the artificial neural network-based important sampling method (ANN-IS) on reliability of structures such as drum brakes. The finite element analysis (FEA) result is used to establish the ANN sample in ANN-based reliability analysis methods. Because the process of FEA is time-consuming, the ANN sample size has a very important influence on the calculation efficiency. Two types of ANNs used in this study are the radial basis function neural network (RBF) and back propagation neural network (BP). RBF-IS and BP-IS methods are used to conduct reliability analysis on training samples of three different sizes, and the results are compared with several reliability analysis methods based on ANNs. The results show that the probability of failure of the RBF-IS method is closer to that of the Monte-Carlo simulation method (MCS) than those of other methods (including BP-IS). In addition, the RBF-IS method has better calculation efficiency than the other methods considered in this study. This research demonstrates that the RBF-IS method is well suited to structure reliability problems.

Predicting Hairiness for Ring and Rotor Spun Yarns and Analyzing the Impact of Fiber Properties

1997 ◽  
Vol 67 (9) ◽  
pp. 694-698 ◽  
Author(s):  
Reiyao Zhu ◽  
M. Dean Ethridge
Keyword(s):  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Fiber Properties ◽  
Fiber Property ◽  
Spun Yarns ◽  
Neural Network Algorithm ◽  
Yarn Hairiness ◽  
Rotor Yarn ◽  
Optimum Model ◽  
The Impact

Models for predicting ring or rotor yarn hairiness are built using a back-propagation neural network algorithm. These models are based on fiber property input measured by three different systems, hvi, afis, and fmt. We compare the prediction results from the different models, which reveal that yarn hairiness measurements from hvi data are superior to other models. The optimum model is based on the availability of all three measurement systems. We also study the impact of each fiber property on yarn hairiness. The dominant effect is fiber length. Each of the remaining properties has a different degree of impact on ring or rotor yarn hairiness.

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.

Research on the Crashworthiness of Neotype Flexible Safety Fence

2012 ◽  
Vol 204-208 ◽  
pp. 1689-1692
Author(s):  
Zheng Bao Lei ◽  
Li Hong Li ◽  
Mu Xi Lei ◽  
Chen Chen Chen
Keyword(s):  
Impact Test ◽  
Lateral Displacement ◽  
Full Scale ◽  
Element Analysis ◽  
Evaluation Standard ◽  
Analysis Computer ◽  
Set Up ◽  
The Impact ◽  
Fence Methods

In order to identify the crashworthiness of neotype flexible safety fence, methods was created by conducting FEA (finite element analysis) computer simulation and full-scale impact test based on the current available evaluation standard. Above all, the model of “vehicle-guardrail” was set up based on virtual proving ground (VPG) pretreatment software, the safety in the impact between vehicle and neotype flexible safety fence were studied from the aspects of the moving locus of vehicle, the acceleration of vehicle and the maximum lateral displacement of guardrail etc. Secondly, full-scale impact test was conducted for the guidance quality of guardrail to the tested vehicle. The test results indicated that the neotype flexible safety fence was inconspicuous to the tested-car, which was basically the same to the simulation results, and the evaluation parameter of guardrail met the acceptance criteria.

EARLY DETECTION AND VISUALIZATION OF BREAST TUMOR WITH THERMOGRAM AND NEURAL NETWORK

2002 ◽  
Vol 02 (02) ◽  
pp. 185-195 ◽  
Author(s):  
S. C. FOK ◽  
E. Y. K. NG ◽  
K. TAI
Keyword(s):  
Neural Network ◽  
Early Detection ◽  
Cancer Detection ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Detection Sensitivity ◽  
Physiological Data ◽  
Element Analysis ◽  
D Model ◽  
Prediction Capability

Although mammography is still the benchmark technique for breast cancer detection, many advantages of thermography make it a suitable adjunct tool for early detection. This paper describes the development of a computer-aided system for use together with thermography to assist in the detection and visualization/analysis of breast tumors. The system consists of a detection module for predicting the presence of tumors from thermograms, and a visualization module for generating the 3-D volumetric geometry of the suspected tumor inside the breast based on the 2-D thermogram. Detection is achieved through an artificial neural network taking the thermogram image as input, while the visualization is obtained by generating the 3-D model of the breast that produces a matching thermal image as the thermogram under a 3-D finite element analysis. A study with 200 subjects indicate that the detection sensitivity was good but the specificity was poor, but the reverse performance result was true for another back-propagation neural network which used physiological data instead of thermograms as input. This suggests that overall prediction capability can be improved by appropriate combination of the two results.

scholarly journals A machine learning based forecast model for the COVID-19 pandemic and investigation of the impact of government intervention on COVID-19 transmission in China

2020 ◽  
Author(s):  
Xingcheng Lu ◽  
Dehao Yuan ◽  
Wanying Chen ◽  
Jimmy Fung
Keyword(s):  
Machine Learning ◽  
Back Propagation ◽  
Forecast Model ◽  
Back Propagation Neural Network ◽  
Flow Index ◽  
Economic Activities ◽  
Government Interventions ◽  
Migration Index ◽  
Machine Learning Model ◽  
The Impact

Abstract The coronavirus disease 2019 (COVID-19) pandemic has killed over 0.3 million people, disrupted people’s normal lives, and severely restricted economic activities globally. In this work, a model for the next-day COVID-19 prediction in China was built based on the ensemble back-propagation neural network machine learning technique, Baidu migration index, internal travel flow index, and confirmed cases from the previous days. The 10-fold cross-validation results showed that the model performs well in estimating the next-day confirmed cases with a correlation coefficient of 0.97. To investigate the impacts of government interventions on the spread of this new coronavirus infection, the Baidu migration index and internal travel flow index multiplied by a factor of two were input into the trained machine learning model, and the results showed that the confirmed cases in the analyzed cities would increase dramatically. The correlation between the daily new confirmed cases and some meteorological factors were also analyzed, and the results revealed that these factors are not dominant in influencing the spread of this disease. Overall, the results of this work suggest that besides early diagnosis and medical treatment, a city lockdown policy is one of the most effective methods in suppressing the rapid spread of COVID-19.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

Classroom Lighting Energy-Saving Control System Based on Machine Vision Technology

2018 ◽  
pp. 143-149 ◽  
Author(s):  
Ruijie CHENG
Keyword(s):  
Control System ◽  
Machine Vision ◽  
Energy Saving ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Lighting Control ◽  
Lighting Energy ◽  
Neural Network Algorithm ◽  
Energy Saving Control ◽  
Model Algorithm

In order to further improve the energy efficiency of classroom lighting, a classroom lighting energy saving control system based on machine vision technology is proposed. Firstly, according to the characteristics of machine vision design technology, a quantum image storage model algorithm is proposed, and the Back Propagation neural network algorithm is used to analyze the technology, and a multi­feedback model for energy­saving control of classroom lighting is constructed. Finally, the algorithm and lighting model are simulated. The test results show that the design of this paper can achieve the optimization of the classroom lighting control system, different number of signals can comprehensively control the light and dark degree of the classroom lights, reduce the waste of resources of classroom lighting, and achieve the purpose of energy saving and emission reduction. Technology is worth further popularizing in practice.

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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