Research of WBS Construction Technology for Large Aircraft Based on Artificial Neural Network

2012 ◽  
Vol 220-223 ◽  
pp. 812-818 ◽  
Author(s):  
Kun Fei Wang ◽  
Guang Rong Yan ◽  
Wei Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prototype System ◽  
Construction Technology ◽  
Work Breakdown Structure ◽  
Relational Work ◽  
Artificial Neural ◽  
Set Up ◽  
Control Work ◽  
Large Aircraft

To ensure development work breakdown comprehensive and thorough for large aircraft product, this paper put forward a WBS decomposition technique based on artificial neural network. On the basis of analysis of the neural network model and work breakdown structure (WBS), project control work breakdown structure (PCWBS), functional work breakdown structure (FWBS), relational work breakdown structure (RWBS), I set up a model which could get PCWBS, FWBS, RWBS and then get WBS according to the knowledge of the similar aircraft development WBS decomposition, so as to realize the automatic acquisition of WBS by input the general project attribute, which replaced the traditional state of depends on the personnel’s experience, and improve efficiency. Based on this, a prototype system is developed, and has been validated by a large aircraft WBS’s generation.

Predicting Model in the Smelting Magnesium by Silicon-Thermo-Reduction Based on Artificial Neural Network

2010 ◽  
Vol 146-147 ◽  
pp. 571-574
Author(s):  
Liang Bo Ji ◽  
Yong Zhi Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mathematical Model ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Effective Means ◽  
Limited Liability ◽  
Predicting Model ◽  
Artificial Neural ◽  
Set Up

This paper described the application of neural networks in predicting the rate of producing magnesium by silicon-thermo-reduction. Fir st of all, a mathematical model between the process parameters and the the rate of producing magnesium was set up with neural network. When the model was satisfied, it could be used for predicting the rate of producing magnesium. Through doing a great number of productive tests in the winca(hebi) magnesium company with limited liability according to the satisfied model, the rate of the producing magnesium is increasing obviously. So it is a kind of effective means for increasing producing magnesium by silicon-thermo-reduction.

Identifying Damage of the Benchmark Structure by Using Artificial Neural Network Methods

2011 ◽  
Vol 219-220 ◽  
pp. 312-317 ◽  
Author(s):  
Bai Sheng Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Identification ◽  
Damage Localization ◽  
Building Structures ◽  
Bp Network ◽  
Network Methods ◽  
Damage Extent ◽  
Artificial Neural ◽  
Set Up

This paper discusses the damage identification using artificial neural network methods for the benchmark problem set up by IASC-ASCE Task Group on Health Monitoring. A three-stage damage identification strategy for building structures is proposed. The BP network and PNN are employed for damage localization and BP network for damage extent identification. Four damage patterns (patterns i~iv) in Cases 1-6 are discussed. The comparison between BP network and PNN are carried out. The results show that PNN performs better than BP network in damage localization. The damage extent identification using BPN is successful even in Cases 2 and 5&6 in which the modeling error is quite large.

scholarly journals An Automatic Logistics System using Artificial Intelligence

2019 ◽  
Vol 8 (2S4) ◽  
pp. 924-926
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Optimal Route ◽  
Logistics System ◽  
The Neural Network ◽  
Artificial Intelligence Technology ◽  
Artificial Neural ◽  
Set Up

In this paper, we propose a method to utilize machine learning to automate the system of classifying and transporting large quantities of logistics. First, establish an environment similar to the task of transferring logistics to the desired destination, and set up basic rules for classification and transfer. Next, each of the logistics that need sorting and transportation is defined as one entity, and artificial intelligence is introduced so that each individual can go to an optimal route without collision between the objects to the destination. Artificial intelligence technology uses artificial neural networks and uses genetic algorithms to learn neural networks. The artificial neural network is generated by each chromosome, and it is evolved based on the most suitable artificial neural network, and a score is given to each operation to evaluate the fitness of the neural network. In conclusion, the validity of this algorithm is evaluated through the simulation of the implemented system.

Prediction of Biosorption Capacity Using Artificial Neural Network Modeling and Genetic Algorithm

2017 ◽  
pp. 276-290 ◽  
Author(s):  
Prakash Chandra Mishra ◽  
Anil Kumar Giri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Bacillus Cereus ◽  
Living Cells ◽  
Biosorption Capacity ◽  
Chromium Vi ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Set Up

Artificial neural network model is applied for the prediction of the biosorption capacity of living cells of Bacillus cereus for the removal of chromium (VI) ions from aqueous solution. The maximum biosorption capacity of living cells of Bacillus cereus for chromium (VI) was found to be 89.24% at pH 7.5, equilibrium time of 60 min, biomass dosage of 6 g/L, and temperature of 30 ± 2 °C. The biosorption data of chromium (VI) ions collected from laboratory scale experimental set up is used to train a back propagation (BP) learning algorithm having 4-7-1 architecture. The model uses tangent sigmoid transfer function at input to hidden layer whereas a linear transfer function is used at output layer. The data is divided into training (75%) and testing (25%) sets. Comparison between the model results and experimental data gives a high degree of correlation R2 = 0.984 indicating that the model is able to predict the sorption efficiency with reasonable accuracy. Bacillus cereus biomass is characterized using AFM and FTIR.

scholarly journals Artificial neural network based detection of neutral relay defects

2019 ◽  
Vol 294 ◽  
pp. 03001
Author(s):  
Volodymyr Havryliuk
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Dependence ◽  
Transient Current ◽  
Practical Utilization ◽  
Training Time ◽  
Train Control ◽  
Artificial Neural ◽  
Set Up ◽  
Wavelet Energy Entropy

The problem considered in the work is concerned to the automatic detecting and identifying defects in a neutral relay. The special design of electromechanical neutral relays is responsible for the strong asymmetry of its output signal for all possible safety-critical influences, and therefore neutral relays have negligible values of dangerous failures rate. To ensure the safe operation of relay-based train control systems, electromechanical relays should be periodically subjected to routine maintenance, during which their main operating parameters are measured, and the relays are set up in accordance with technical regulations. These measurements are mainly done manually, so they take a lot of time (up to four hours per relay), are expensive, and the results are subjective. In recent years, fault diagnosis methods based on artificial neural networks (ANN) have received considerable attention. The ANN-based classification of relay defects using the time dependence of the transient current in the relay coil during its switching is very promising for practical utilization, but for efficient use of ANN a lot of data is required to train the artificial neural network. To reduce the ANN training time, a pre-processing of the time dependence of relay transient current was proposed using wavelet transform and wavelet energy entropy, which makes it possible to reveal the features of the main defects of the relay armature, contact springs, and magnetic system. The effectiveness of the proposed approach for automatic detecting and identifying of the neutral relays defects was confirmed during testing of the relays with various artificially created defects.

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

2013 ◽  
Vol 535-536 ◽  
pp. 318-321
Author(s):  
Xia Jin ◽  
Shi Hong Lu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Simulation Software ◽  
Forming Process ◽  
Training Samples ◽  
Metal Forming Process ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Set Up ◽  
The One

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.

scholarly journals Transceiver error reduction by design prototype system based on neural network analysis method

2020 ◽  
Vol 18 (3) ◽  
pp. 1244
Author(s):  
Muthna Jasim Fadhil ◽  
Maitham Ali Naji ◽  
Ghalib Ahmed Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Code Word ◽  
Prototype System ◽  
Spiking Neural Network ◽  
Analysis Method ◽  
Neural Network Structure ◽  
Artificial Neural ◽  
Source Dimension ◽  
Proper Condition

<p><span>Code words traditional can be decoding when applied in artificial neural network. Nevertheless, explored rarely for encoding of artificial neural network so that it proposed encoder for artificial neural network forward with major structure built by Self Organizing Feature Map (SOFM). According to number of bits codeword and bits source mentioned the dimension of forward neural network at first then sets weight of distribution proposal choosing after that algorithm appropriate using for sets weight initializing and finally sets code word uniqueness check so that matching with existing. The spiking neural network (SNN) using as decoder of neural network for processing of decoding where depending on numbers of bits codeword and bits source dimension the spiking neural network structure built at first then generated sets codeword by network neural forward using for train spiking neural network after that when whole error reached minimum the process training stop and at last sets code word decode accepted. In tests simulation appear that feasible decoding and encoding neural network while performance better for structure network neural forward a proper condition is achieved with γ node output degree. The methods of mathematical traditional can not using for decoding generated Sets codeword by encoder network of neural so it is prospect good for communication security. </span></p>

Prediction of Biosorption Capacity Using Artificial Neural Network Modeling and Genetic Algorithm

2020 ◽  
pp. 144-158
Author(s):  
Prakash Chandra Mishra ◽  
Anil Kumar Giri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Bacillus Cereus ◽  
Living Cells ◽  
Biosorption Capacity ◽  
Chromium Vi ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Set Up

Artificial neural network model is applied for the prediction of the biosorption capacity of living cells of Bacillus cereus for the removal of chromium (VI) ions from aqueous solution. The maximum biosorption capacity of living cells of Bacillus cereus for chromium (VI) was found to be 89.24% at pH 7.5, equilibrium time of 60 min, biomass dosage of 6 g/L, and temperature of 30 ± 2 °C. The biosorption data of chromium (VI) ions collected from laboratory scale experimental set up is used to train a back propagation (BP) learning algorithm having 4-7-1 architecture. The model uses tangent sigmoid transfer function at input to hidden layer whereas a linear transfer function is used at output layer. The data is divided into training (75%) and testing (25%) sets. Comparison between the model results and experimental data gives a high degree of correlation R2 = 0.984 indicating that the model is able to predict the sorption efficiency with reasonable accuracy. Bacillus cereus biomass is characterized using AFM and FTIR.

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

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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