High-Pressure Water Spray Research Based on Neural Network

2010 ◽  
Vol 29-32 ◽  
pp. 138-142
Author(s):  
Rui Li ◽  
Zi Ming Kou
Keyword(s):  
Neural Network ◽  
High Pressure ◽  
Influencing Factors ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Industrial Processes ◽  
Water Spray ◽  
Key Factors ◽  
Pressure Water ◽  
The Relationship

The spray cleaning method is important and universal in many industrial processes and other occasion. Because the size of the waterdrop is one of key factors for cleaning, this paper not only studied the relationship between the size of waterdrop and other influencing factors, but also researched the forecasted method for the size of waterdrop. In lab, by measuring the size of the waterdrop, jetted by one kind of nozzle, data were acquired and were used to train the Back Propagation Neural Network ( BPNN ). Through comparing those diameters, between measured in lab and calculated by BPNN after trained. It was acquired that the maximum errors was smaller than 1.62%, between the computed results and the factual measured ones. The experimental results showed that BPNN is an effective tool to predict the variation of the non-linear waterdrop diameter.

Optimization of the Hot Pressing Parameters of Nanocomposite Ceramic Tool and Die Materials: I. with BP Neural Network Method

2010 ◽  
Vol 154-155 ◽  
pp. 1114-1118
Author(s):  
Jing Jie Zhang ◽  
Chong Hai Xu ◽  
Ming Dong Yi ◽  
Hui Fa Zhang ◽  
Xing Hai Wang
Keyword(s):  
Neural Network ◽  
Hot Pressing ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Ceramic Tool ◽  
Network Method ◽  
Die Materials ◽  
Die Material ◽  
Set Up ◽  
The Relationship

In this paper, back propagation neural network was used in the optimum design of the hot pressing parameters of an advanced ZrO2/TiB2/Al2O3 nanocomposite ceramic tool and die material. The BP algorithm could set up the relationship well between the hot pressing parameters and mechanical property of nanocomposite ceramic tool and die materials. After analyzed the predicted results, the best predicted results were the sintering temperature was 1420°C and the holding time was 60min. Under these hot pressing parameters, the best flexural strength and the best fracture toughness of the material could be obtained.

Risk Assessment for Remanufacturing System Based on Neural Network

2011 ◽  
Vol 204-210 ◽  
pp. 1382-1385 ◽  
Author(s):  
Qiu Lian Wang ◽  
Cong Bo Li
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Assessment Model ◽  
The Internet ◽  
Risk Assessment Model ◽  
Key Indicators ◽  
The Relationship ◽  
Evaluating Method

To provide referenced risk assessment model for implementing remanufacturing program in enterprise, a set of evaluating indicators was proposed according to the characteristics of the remanufacturing program’s life cycle, which includes acquisition, assessment, disassembly, reproducing and reprocessing phases; And Back Propagation neural network (BPNN) was applied to measure the risk of the remanufacturing system as evaluating method; In addition, the influence of the evaluating indicators on the output was calculated by the Relationship Function between the networked weights, so the key indicators can be found out. The risk assessment model is trained by five samples obtained from the Internet, and is verified by the case of one machining tools company.

scholarly journals Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network Method

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Wenlong Tian ◽  
Zhaoyong Mao ◽  
Fuliang Zhao ◽  
Zhicao Zhao
Keyword(s):  
Neural Network ◽  
Autonomous Underwater Vehicles ◽  
Back Propagation ◽  
Three Dimensional ◽  
Optimization Method ◽  
Back Propagation Neural Network ◽  
Underwater Vehicles ◽  
Minimum Drag ◽  
Computational Fluid Dynamics Cfd ◽  
The Relationship

This paper presents an optimization method for the design of the layout of an autonomous underwater vehicles (AUV) fleet to minimize the drag force. The layout of the AUV fleet is defined by two nondimensional parameters. Firstly, three-dimensional computational fluid dynamics (CFD) simulations are performed on the fleets with different layout parameters and detailed information on the hydrodynamic forces and flow structures around the AUVs is obtained. Then, based on the CFD data, a back-propagation neural network (BPNN) method is used to describe the relationship between the layout parameters and the drag of the fleet. Finally, a genetic algorithm (GA) is chosen to obtain the optimal layout parameters which correspond to the minimum drag. The optimization results show that (1) the total drag of the AUV fleet can be reduced by 12% when the follower AUV is located directly behind the leader AUV and (2) the drag of the follower AUV can be reduced by 66% when it is by the side of the leader AUV.

Setting Speed Limits on Kansas Two-Lane Highways: Neuronet Approach

2000 ◽  
Vol 1708 (1) ◽  
pp. 20-27 ◽  
Author(s):  
Yacoub M. Najjar ◽  
Robert W. Stokes ◽  
Eugene R. Russell
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Field Studies ◽  
Back Propagation Neural Network ◽  
Speed Limit ◽  
Speed Limits ◽  
Rural Highways ◽  
Resource Limitations ◽  
Traffic Volumes ◽  
The Relationship

Recent federal legislation allowing states to set their own speed limits on highways, as well as increases in the number of requests from citizens and neighborhood groups to implement actions to reduce “excessive” speeding on their streets and highways, has created considerable debate about and scrutiny of the appropriate speed limits that should be posted on state highways. Various speed studies have indicated that sensible and cautious drivers will most likely drive at the speed dictated by roadway and traffic conditions rather than relying on a posted speed limit. To incorporate roadway characteristics and traffic volumes into the selection of the most appropriate (i.e., comfortable, safe, and efficient) speed limit, actual engineering field speed studies are carried out. Generally, the 85th percentile speed at which the drivers surveyed are driving is selected as a primary factor in determining the posted speed limit. Carrying out such field studies for all highway sections is a costly and time-consuming process. Therefore, characterizing the relationship between the 85th percentile speed and the roadway characteristics will assist in selecting the most appropriate posted speed limit on highway sections where field surveying is difficult due to resource limitations. A back-propagation neural network is used to extract the relationship between roadway characteristics and 85th percentile speed. The developed neural-network-based speed model was found to perform satisfactorily for characterization of speed on Kansas two-lane, uninterrupted-flow rural highways and for quantifying the influence of prevailing roadway characteristics on the anticipated 85th percentile speed.

Sensitivity analysis of the influencing factors of parking lot selection based on BP neural network

2021 ◽  
pp. 1-9
Author(s):  
Jingjing Yin ◽  
Qiang Sun ◽  
Juan Zhou
Keyword(s):  
Neural Network ◽  
Influencing Factors ◽  
Selection Process ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Walking Distance ◽  
Management Measures ◽  
Parking Lot ◽  
Parking Management ◽  
Selection Of

The driver’s selection process of parking lot will consider a variety of influencing factors, and consider different influencing factors for different travel purposes. In this paper, the driver’s travel purposes were divided into three categories according to the degree of emergency: emergency, routine and leisure. Four influencing factors of parking lot selection including walking distance, charge, parking index and parking convenience were selected, and ranked according to their sensitivity, and their sensitivity was analyzed by using the BP (back propagation) neural network, which provides a basis for the development of differentiated parking guidance and parking management measures to avoid the uneven parking due to random selection of parking lot and realize the maximum utilization of parking resources.

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

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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