Prediction of Springback of the One-Axle Rotary Shaping Based on Artificial Neural Network

One-axle rotary shaping with the elastic medium (RSEM) is a kind of advanced sheet metal forming process. The research object is the springback of aluminous U-section. The orthogonal method is used to arrange the simulation experiments, the forming and springback of the workpiece are simulated successfully with the Finite Element Simulation software, and The main factors influenced the RSEM are analyzed. The simulation results are used as the training samples of the artificial neural network (ANN), and the ANN prediction model of RSEM process is set up. The prediction results would be tested with the experiment data, and only a little tolerance was existed between the two values. It demonstrated that the combination of orthogonal test, numerical simulation and neural network could effectively predict the springback of RSEM, the design efficiency of process parameters would be improved. It would guide the development of precision forming technology.

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Optimization Waiting Time at Berthing Area of Port Container Terminal with Hybrid Genetic Algorithm (GA) and Artificial Neural Network (ANN)

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.902.431 ◽

2014 ◽

Vol 902 ◽

pp. 431-436 ◽

Cited By ~ 9

Author(s):

A. Shahpanah ◽

S. Poursafary ◽

S. Shariatmadari ◽

A. Gholamkhasi ◽

S.M. Zahraee

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Waiting Time ◽

Container Terminal ◽

Hybrid Approach ◽

Simulation Software ◽

Optimum Number ◽

Artificial Neural ◽

Artificial Neural Network Ann

A queuing network model related to arrival, departure and berthing process of ships at port container terminal is presented in this paper. The important datas collected from PTP port container terminal located at Malaysia. Based on the case study the model was built with using Arena 13.5 simulation software. Especially this study proposes a hybrid approach consisting of Genetic algorithm (GA), Artificial Neural Network (ANN) to find the the optimum number of equipments at berthing area of port container terminal. The input data that used in ANN obtained from Arena results. The main goal of this study is reduced waiting time of each ship at port container terminal, and Based on the result the optimum waiting time 50 will be achieved.

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Artificial neural network as a predictive tool for emissions from heavy-duty diesel vehicles in Southern California

International Journal of Engine Research ◽

10.1243/14680874jer00807 ◽

2007 ◽

Vol 8 (4) ◽

pp. 321-336 ◽

Cited By ~ 19

Author(s):

N Hashemi ◽

N. N. Clark

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Oxides Of Nitrogen ◽

Heavy Duty ◽

Predictive Tool ◽

Diesel Vehicles ◽

Artificial Neural ◽

Heavy Duty Diesel ◽

Artificial Neural Network Ann ◽

The One

An artificial neural network (ANN) was trained on chassis dynamometer data and used to predict the oxides of nitrogen (NO x), carbon dioxide (CO2), hydrocarbons (HC), and carbon monoxide (CO) emitted from heavy-duty diesel vehicles. Axle speed, torque, their derivatives in different time steps, and two novel variables that defined speed variability over 150 seconds were defined as the inputs for the ANN. The novel variables were used to assist in predicting off-cycle emissions. Each species was considered individually as an output of the ANN. The ANN was trained on the Highway cycle and applied to the City/Suburban Heavy Vehicle Route (CSHVR) and Urban Dynamometer Driving Schedule (UDDS) with four different sets of inputs to predict the emissions for these vehicles. The research showed acceptable prediction results for the ANN, even for the one trained with only eight inputs of speed, torque, their first and second derivatives at one second, and two variables related to the speed pattern over the last 150 seconds. However, off-cycle operation (leading to high NO x emissions) was still difficult to model. The results showed an average accuracy of 0.97 for CO2, 0.89 for NO x, 0.70 for CO, and 0.48 for HC over the course of the CSHVR, Highway, and UDDS.

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Reusing Source Task Knowledge via Transfer Approximator in Reinforcement Transfer Learning

Symmetry ◽

10.3390/sym11010025 ◽

2018 ◽

Vol 11 (1) ◽

pp. 25 ◽

Cited By ~ 1

Author(s):

Qiao Cheng ◽

Xiangke Wang ◽

Yifeng Niu ◽

Lincheng Shen

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Transfer Learning ◽

Mapping Rule ◽

Learning Methods ◽

Target Task ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

The One

Transfer Learning (TL) has received a great deal of attention because of its ability to speed up Reinforcement Learning (RL) by reusing learned knowledge from other tasks. This paper proposes a new transfer learning framework, referred to as Transfer Learning via Artificial Neural Network Approximator (TL-ANNA). It builds an Artificial Neural Network (ANN) transfer approximator to transfer the related knowledge from the source task into the target task and reuses the transferred knowledge with a Probabilistic Policy Reuse (PPR) scheme. Specifically, the transfer approximator maps the state of the target task symmetrically to states of the source task with a certain mapping rule, and activates the related knowledge (components of the action-value function) of the source task as the input of the ANNs; it then predicts the quality of the actions in the target task with the ANNs. The target learner uses the PPR scheme to bias the RL with the suggested action from the transfer approximator. In this way, the transfer approximator builds a symmetric knowledge path between the target task and the source task. In addition, two mapping rules for the transfer approximator are designed, namely, Full Mapping Rule and Group Mapping Rule. Experiments performed on the RoboCup soccer Keepaway task verified that the proposed transfer learning methods outperform two other transfer learning methods in both jumpstart and time to threshold metrics and are more robust to the quality of source knowledge. In addition, the TL-ANNA with the group mapping rule exhibits slightly worse performance than the one with the full mapping rule, but with less computation and space cost when appropriate grouping method is used.

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Design of frequency selective surface comprising of dipoles using artificial neural network

International Journal of Advances in Applied Sciences ◽

10.11591/ijaas.v9.i4.pp276-283 ◽

2020 ◽

Vol 9 (4) ◽

pp. 276

Author(s):

Monojit Rudra ◽

P Soni Reddy ◽

Rajatsubhra Chakraborty ◽

Partha Pratim Sarkar

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Simulated Data ◽

Frequency Selective Surface ◽

Simulation Software ◽

Computational Time ◽

Ann Models ◽

Artificial Neural ◽

Frequency Selective ◽

Artificial Neural Network Ann

<span>This paper depicts the design of Frequency Selective Surface (FSS) comprising of dipoles using Artificial Neural Network (ANN). It has been observed that with the change of the dimensions and periodicity of FSS, the resonating frequency of the FSS changes. This change in resonating frequency has been studied and investigated using simulation software. The simulated data were used to train the proposed ANN models. The trained ANN models are found to predict the FSS characteristics precisely with negligible error. Compared to traditional EM simulation softwares (like ANSOFT Designer), the proposed technique using ANN models is found to significantly reduce the FSS design complexity and computational time. The FSS simulations were made using ANSOFT Designer v2 software and the neural network was designed using MATLAB software.</span>

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A Novel User Classification Method for Femtocell Network by Using Affinity Propagation Algorithm and Artificial Neural Network

The Scientific World JOURNAL ◽

10.1155/2014/253787 ◽

2014 ◽

Vol 2014 ◽

pp. 1-14 ◽

Cited By ~ 4

Author(s):

Afaz Uddin Ahmed ◽

Mohammad Tariqul Islam ◽

Mahamod Ismail ◽

Salehin Kibria ◽

Haslina Arshad

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Affinity Propagation ◽

Training Time ◽

User Classification ◽

Training Samples ◽

Femtocell Network ◽

Artificial Neural ◽

The Difference ◽

Artificial Neural Network Ann

An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation.

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Analysis of unsymmetrical faults based on artificial neural network using 11 kV distribution network of University of Lagos as case study

Journal of Advances in Science and Engineering ◽

10.37121/jase.v4i1.91 ◽

2021 ◽

Vol 4 (1) ◽

pp. 53-64

Author(s):

Akintunde S. Alayande ◽

Ignatius K. Okakwu ◽

Olakunle E. Olabode ◽

Okwuchukwu K. Nwankwoh

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Power System ◽

High Speed ◽

Distribution Network ◽

Programming Tools ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Set Up

The occurrence of faults in any operational power system network is inevitable, and many of the causative factors such as lightning, thunderstorm among others is usually beyond human control. Consequently, there is the need to set up models capable of prompt identification and classification of these faults for immediate action. This paper, explored the use of artificial neural network (ANN) technique to identify and classify various faults on the 11 kV distribution network of University of Lagos. The ANN is applied because it offers high speed, higher efficiency and requires less human intervention. Datasets of the case study obtained were sectioned proportionately for training, testing, and validation. The mathematical formulations for the method are presented with python used as the programming tools for the analysis. The results obtained from this study, for both the voltage and current under different scenarios of faults, are displayed in graphical forms and discussed. The results showed the effectiveness of the ANN in fault identification and classification in a distribution network as the model yielded satisfactory results for the available limited datasets used. The information obtained from this study could be helpful to the system operators in faults identification and classification for making informed decisions regarding power system design and reliability.

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EQUIVOX: AN EXAMPLE OF ADAPTATION USING AN ARTIFICIAL NEURAL NETWORK ON A CASE-BASED REASONING PLATFORM

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237213500270 ◽

2013 ◽

Vol 25 (02) ◽

pp. 1350027 ◽

Cited By ~ 2

Author(s):

Julien Henriet ◽

Brigitte Chebel-Morello ◽

Michel Salomon ◽

Jad Farah ◽

Rémy Laurent ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Emergency Situation ◽

Case Based Reasoning ◽

Radiological Emergency ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

The One ◽

Numerical Representations ◽

Case Based

In case of a radiological emergency situation involving accidental human exposure, a dosimetry evaluation must be established as soon as possible. In most cases, this evaluation is based on numerical representations and models of victims. Unfortunately, personalized and realistic human representations are often unavailable for the exposed subjects. However, accuracy of treatment depends on the similarity of the phantom to the victim. The EquiVox platform (Research of Equivalent Voxel phantom) developed in this study uses case-based reasoning (CBR) principles to retrieve and adapt, from among a set of existing phantoms, the one to represent the victim. This paper introduces the EquiVox platform and the artificial neural network (ANN) developed to interpolate the victim's 3D lung contours. The results obtained for the choice and construction of the contours are presented and discussed.

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