[Retracted] Application of BP Neural Network Model in Risk Evaluation of Railway Construction

Changwei, Yang; Zonghao, Li; Xueyan, Guo; Wenying, Yu; Jing, Jin; Liang, Zhu

doi:https://doi.org/10.1155/2019/2946158

Complexity

On this page

Abstract Introduction Conclusion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article Retraction

!

This article has been Retracted. To view the article details, please click the ‘Retraction’ tab above.

Special Issue

Applications of Machine Learning Methods in Complex Economics and Financial Networks

View this Special Issue

Research Article | Open Access

Volume 2019 | Article ID 2946158 | https://doi.org/10.1155/2019/2946158

[Retracted] Application of BP Neural Network Model in Risk Evaluation of Railway Construction

Yang Changwei,¹Li Zonghao,¹Guo Xueyan,¹Yu Wenying,²Jin Jing,³and Zhu Liang⁴

Guest Editor: Thiago Christiano Silva

Received15 Feb 2019

Accepted30 Apr 2019

Published02 Jun 2019

Abstract

Chinese railway construction project is an important part of the implementation of the “Belt and Road” strategy, and the risk evaluation of overseas railway construction is the primary link of the project. Firstly, this paper mainly analyzes the Asian and European countries along the railway construction project, establishes a railway construction project risk evaluation system, and synthesizes various risk factors. Secondly, it establishes two independent BP neural network models by using different training algorithms because of the different political, economic, and cultural elements between the two continents.

1. Introduction

Since the “the Belt and Road” cooperation initiative was put forward, China is committed to the construction of overseas railways. Railways are the key means of forming crucial international channels and achieving interconnection [1–3]. At the same time, China's railway technology is technologically advanced, safe and reliable, compatible, and cost-effective. These advantages make this line a hot project which attract the countries involved in the Belt and Road. However, since “the Belt and Road” involves many countries, the national conditions and needs of different countries vary, which makes it necessary to design different policies according to different conditions. Besides, a railway project needs a large investment, a long construction period that involves many links. Therefore, the various risk factors are complex and need to be treated differently according to differently countries, aiming to assess project risks and provide decision support for railway construction in order to avoid and control risks [4, 5]. The traditional risk evaluation method cannot meet the current complicated situation, so it is of great practical significance to seek a new method suitable for overseas railway investment and construction risk evaluation. Figure 1 shows the overall trend of "the Belt and Road" Eurasia region railway [6, 7].

This paper mainly analyzes countries along “the Belt and Road” strategic line in Asia and Europe, establish a risk evaluation index system, and use the BP neural network toolbox in MATLAB under this risk evaluation index system to process the data to build a risk prediction model.

2. Railway Construction Risk Evaluation Method

2.1. Evaluation Index System

The railway construction technology expert resource and investment should be integrated based on the national conditions of Asian and European countries, the factors needed to be considered for railway construction, and the principles of being scientifically, systematically, typically, and feasibly practical. The opinions of experts, market operation experts, and venture capital experts establish an evaluation index system, as shown in Figure 2 [8, 9].

The risk is roughly divided into several aspects.

(1) Economic Risk. The risk refers to the risk that may be brought about by changes in the social economic situation of the country where the project is located, including the country’s import and export volume, per capital GDP, the turnover of foreign contracted projects, inflation rate, and economic prospects. It affects willingness and ability to pay of a country to some extent.

(2) Population Risk. The risk is analyzed according to the average quality of people’s life of the target country, including population density, population growth rate, average life expectancy, infectious disease mortality, and the number of refugees. It reflects the living standards of the people in that country to a certain extent.

(3) Traffic Risk. The risk is analyzed based on the current national overall traffic level of the target country. It includes elements such as transportation services, power coverage, and traffic accident rate, which reflect the current development of the country's transportation industry to a certain extent.

(4) Political Risk. The risk is analyzed based on the current domestic policies and regulations of the target country, including the cooperative relationship with China, the law, civil political freedom, and political stability. The unstable political situation of Central Asia and the Middle East is quite dangerous. If there is any big change, there will be huge impact on the project, even Casualties.

2.2. Comprehensive Evaluation Criteria

Among the 17 factors that affect the evaluation index of railway investment and construction project risks, there is no direct relationship between each factor, and the dimensions are very different. Therefore, before the neural network model being established, the data should be normalized. That is to say, the data is limited to a certain range and classified by the expert after the data is processed. When scoring, it should be fully reviewed by experts; the specific results are shown in Table 1.

Due to the large social and cultural differences between Asia and Europe, as well as the large gap in economic development, this paper will use the same system for evaluation in Asia and Europe but score and model independently, which means the criteria for judging in Asia and Europe are independent. Experts will score 17 factors according to different environments in Asia and Europe and work out the final score based on the final comprehensive opinions and the scores of each factor, which also satisfies the scoring standard of Table 1; that is, the higher the value the lower the risk of building a railway in that country and vice versa.

3. Establish BP Neural Network Model

3.1. Introduction to BP Neural Network

Artificial neural network is a model based on human brain [10–13]. It has a neuron system composed of many neurons, which has the advantages of massive parallelism, distributed processing, self-organization, and self-learning. Among these models, multilayer forward BP network is the most widely used neural network form. It has the universal advantages of all neural networks, self-learning and self-adaptive ability, nonlinear mapping ability, and high fault tolerance rate. Many problems that cannot be solved by traditional information processing methods have made some progress after using neural networks. For example, the risk evaluation of this paper and BP neural network can play a vital role.

Figure 3 is a typical BP neural network flow chart, which shows the whole process of BP neural network for data processing: firstly, input the variables (input layer), then deal with the data through the processing of the function, adjust the weight of each inputted variable, and finally compare the output value with the target value. If the satisfied accuracy is not met, then readjust the weight until the output value meets the error requirement. In this case, the scores of the 17 risk evaluation factor are the input values; the synthesized score of each country is the target value. By inputting 17 risk scores and synthesized scores of each country, the BP neural network functions like experts who can give an overall score; thus it forms a railway construction risk evaluation model.

3.2. Basic Principle of BP Neural Network

There are many training functions in BP neural network; the most common ones are traind (gradient descent method), traindm (momentum gradient descent method), trainda (adaptive learning rate gradient descent method), traindx (adaptive learning rate and momentum gradient descent method), traincgf/traincgp/traincgb (three conjugate gradient methods), etc. Each one of them has different parameters and different training methods that are based on the basic principles of BP neural network [14–16]. The basic principles are as follows.

The essence of neural networks is the following function:

In the input layer, the input node spreads the inputted information to the hidden layer node through activating function . There are two kinds of activation functions: Sigmoid and Purein. They are nonlinear for either nonlinear data or linear data. Thus, this model is nonlinear; the expression of sigmoid is

Supposing that is the input layer and the hidden layer, the random weight between the hidden layer and the output layer, the vector is , the output of the hidden layer node is represented by , and then there is

Similarly, the output of the output layer node is represented by P,

After the output is figured out, the node output error can be calculated as follows:

T is the target value of the output node, and for hidden layer nodes,

where is the error term of the hidden layer node i, is the output of the hidden layer node i, and is the weight of the node i to the next layer node k. And is the error term of the node k also the next layer of the node i. Finally, the weight on each link is updated:

In this expression, w_ji is the weight of node i to node j, η is the learning rate constant, δ_j is the error term of node j, and x_ji is the input passed by node i to node j.

The content above is the calculation of the error term of each node of the BP neural network and the weight update method. To calculate the error term of a node, it is necessary to firstly calculate the error term of each node connected to it. This requires to begin with calculating the error terms of the output layer, then the error terms of each hidden layer are calculated reversely until the hidden layer connected to the input layer. This is the meaning of the name of the reverse propagation (BP) algorithm.

3.3. Model and Algorithmic Principles for Asia

As mentioned earlier in this article, the railway construction project spans the Eurasian continent and passes through dozens of countries; each country has different national conditions. The regional differences between the two continents are obvious. Therefore, the target countries are divided into Asian district and European district. Thus, different learning functions and separate training models are applied.

In general, the number of hidden layers increases, complicating the network, thereby increasing the training time of the network and the tendency of “overfitting”. After many tests and extensive statistical analysis, the neural network is designed to be 3 layers containing one hidden layer based on the calculate accuracy requirement and training time, which is the same as other research results [17–21].

From a view of the overall situation in Asia, the economic development of some country is relatively backward, the political situation of some country is turbulent, and the overall situation is even more complicated. This paper adopts the traindx (adaptive learning rate momentum gradient descent method) algorithm that is attached to the most basic gradient descent method. The momentum and automatic adjustment of the learning rate: the standard BP algorithm is essentially a simple steepest descent static optimization method, which does not consider the direction of the error gradient descent, so that the learning process is often oscillated, the convergence is slow, and the additional momentum is the part of the last weight adjustment. It is added to the weight adjustment amount calculated according to the current error. As the actual weight adjustment amount, the essence of this is the influence of the last weight change, which is transmitted by a momentum factor. The weight adjustment formula with an additional momentum factor is

In this expression, W is the weight, is the weight increment, k is the training number, mc is the momentum factor, η is the learning rate, δ_i is the error of the output node i, and O_j is the input of the input node j. The role of momentum is to remember the direction of the change of the last connection weight (positive and negative values), so that you can use a higher learning rate to improve learning speed.

Based on the additional momentum, the traindx algorithm also uses the adaptive learning rate method. An important reason for the slow convergence rate of the standard BP algorithm is that the learning rate is not properly selected, and the rate cannot be changed after the rate is determined. When the learning rate is low, the training time will be long and the convergence will be slow. If the learning rate is too high, the overfitting will be caused and the data oscillation finally will diverge, so the adaptive learning rate method is used to solve this problem. The principle is as follows: check whether the modification of each weight changes the error. It means that the selected learning rate value is small if the error becomes smaller, and an appropriate amount can be added to it; if the error increases, then it should be reduced. The value of the learning rate can be expressed by the following equation:where η is the learning rate, K is the number of trainings, and E is the error function, , , is the actual output value after network training,_i is the expected output value of the learning sample, and n is the number of learning samples.

Additional momentum can help to find better solutions, and adaptive learning can shorten the network training time. The combination of the two is the traindx training algorithm. There are many countries in Asia which have complicated situations. The traindx algorithm is relatively suitable for these countries. The sample set of the training network should be the authoritative evaluation result with high credibility, which can be obtained by experts in some Asian countries. The trained model is stored, and the risk prediction is performed on the project that needs to be predicted. If the corresponding risk evaluation value is input, the neural network system can calculate the comprehensive risk evaluation value of the project through the previously calculated weights and thresholds. And outputting the output layer as a network result, you can get the final evaluation result.

For the confirmation of the number of neurons in the hidden layer, the formula is used in this manuscript (m is the number of neurons in the hidden layer, n is the number of neurons in the input layer, l is the number of neurons in the output layer, and a is an arbitrary integer from 0 to 5), which also is used in other researches [22–26]. In order to avoid “overfitting” during training and ensure high enough network performance and generalization ability, the basic principle for determining the number of hidden layer neurons is using the most compact structure while satisfying the accuracy requirements. That is, take as few hidden layer neurons as possible. When a=0, m=4.24, take 5, and then the number (5,6,7,8,9,10,11) of hidden layer neurons is used to verify the error rate until the best error rate is obtained. Table 1 shows the error rate of each selected hidden layer neuron, and the error meets accuracy requirement (error < 5%) when the number of hidden layer neurons is 6. Therefore, 6 neurons in the hidden layer are selected in this paper.

Based on the above BP neural network model and risk evaluation system, the macrorisks of 36 Asian countries along “the Belt and Road” were evaluated; Table 2 shows these countries. First, based on the expert experience method, the 17 risks of each country are evaluated, and then the 17 scores are combined to evaluate the risk of railway construction in each country. The results are shown in Table 3. In the model for Asian countries, the data for training set comes from United Arab Emirates/Oman/Azerbaijan/Pakistan/Bahrain/Bhutan/ Philippines/Georgia/Hassackstein/Korea/Qatar/Kuwait/ Laos/Lebanon/Maldives/Malaysia/Mongolia/Bangladesh/ Saudi Arabia/Sri Lanka/Turkey/Iran. The training function uses traindx to adjust the training parameters until the error requirements of the training set are met, as shown in Table 4. The parameters are selected as follows: 6000 cycles of maximum epochs, 0.001 for training accuracy, and 0.05 for learning rate (lr), the learning rate growth ratio is 1.05 (lr_inc), the learning rate reduction ratio (lr_dec) is 0.7, the maximum performance increment (max_perf_inc) is 1.04, the momentum factor (mc) is 0.9, and the rest are default values. The training results are shown in Table 5.

It can be seen from Table 5 that the average relative error of the 22 training data in the Asian training set countries is 2.70%, and the maximum error in the sample set is 4.88% which satisfies the accuracy requirement of 5%; this means the model training learning result is good. Then the verification samples are replaced by the remaining 14 countries to verify whether the model is valid; eventually the results will be compared with the expert score results. The results are shown in Table 6.

It can be seen from Table 6 that the average relative error of the verification data set is 2.99% and the maximum relative error of the prediction set is 4.22%, which meets the accuracy requirement of 5%, indicating that the neural network model can achieve the accuracy required by the project and can be used for risk assessment and prediction of Asian countries in railway construction projects.

3.4. Modeling and Algorithmic Principles for Europe

In Europe, the gap between various factors in various countries is not very large, the situation is relatively stable, the economy is more developed, and traindx is suitable for networks that have more complex and more data and may be used for smaller models. The error is increased so that trainda (adaptive learning rate algorithm) is selected; that is, no momentum is added except that the adaptive learning rate is added. The principle is shown in the previous section, Table 7 shows these countries; the expert risk evaluation in Europe is shown in Table 8. In the model for European countries, the data for training set comes from Albania/Estonia/Belarus/Bulgaria/Bosnia/Poland/Russia/ Montenegro/Croatia/Latvia/Lithuania/Romania/Macedonia/ Czech/Moldova. Trainda is chosen as the training function; then adjusting the training parameters should be done until the error requirements of the training set are met. The parameter selection is as follows: the European model hidden layer neurons are determined in the same way as the Asian model. Table 9 shows the error of the number of neurons, and the error meets accuracy requirement (error < 5%) when the number of hidden layer neurons is 6. Therefore, 6 neurons in the hidden layer are selected in this manuscript. The maximum number of cycles (epochs) is 5000 times, the training accuracy (goal) is 0.001, the learning rate (lr) takes 0.01, learning rate growth ratio (lr_inc) is 1.05, the learning rate reduction ratio (lr_dec) is 0.8, and the maximum performance increment (max_perf_inc) is 1.04. The rest are taken as default values. The training results are shown in Table 10.

It can be seen from Table 10 that the average relative error of the 15 training data in Europe is 3.69%, and the maximum error in the sample set is 4.77% which meets the accuracy requirement of 5%; that is to say, the model training learning result is better. Then the verification sample is replaced by the remaining 5 countries to verify whether the model is valid or not; lastly compare the results with the expert score results. The results are shown in Table 11.

It can be seen from Table 11 that the average relative error of the verification data set is 4.37% and the maximum relative error of the prediction set is 4.97%, indicating that the neural network model has been able to achieve the accuracy required by the project and can be used for railway outbuilding projects in European national risk evaluation forecast.

3.5. Model Robustness Test

It can be seen from the above training and testing that the accuracy of the model is good, and for machine learning, robustness is also an important feature, and the artificial neural network itself is robust [27–29]. The following is a robust test using the European model as an example: it is assumed that some data in the first 10 countries are randomly interfered and the data is shown in Table 12. The interference data is substituted into the model for simulation calculation, and the error between the verification result and the score given by the expert is shown in Table 13. The error between the verification result and the model score is shown in Table 14.

It can be seen from the data in the table that even if the input sample data is partially interfered with the data, the model can guarantee the result that the accuracy requirement (error < 5%) is met after the network is running; that means the robustness test result is good.

4. Conclusion

According to different situations in Asia and Europe, BP neural network model is established by using different functions for risk evaluation. Through the created neural network model, only experts can give the scores of the various risks in the macrorisk evaluation of the target country, and the overall construction risk score of the target country can be obtained without cumbersome manual scoring.

Due to the complex relationship between the various influencing factors in this project, it is not intuitive to use a linear expression to carry out risk prediction. BP neural network is selected to nonlinearly process the data to obtain the quantitative value of risk assessment. BP neural network does not need corresponding function equations for a set of nonlinear data; instead it iterates out the corresponding results and obtains an equation model that meets the requirements through its own training, which can meet the requirements of the project. It is more effective and convenient than traditional methods; neural networks have broad application prospects in such nonlinear fields.

This model is mainly for the risk evaluation of railway macroconstruction. For specific railway project risks such as construction risks and environmental risks, it is necessary to consider the actual target needs and actual risk indicators.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors of this manuscript do not have any conflicts of interest regarding the publication of this article.

Acknowledgments

This study is supported by Science and Technology Research and Development Project of China Railways Corporation (no. K2018T003); Intelligent High-Speed Rail Strategy Research (2035) of Chinese Academy of Engineering Consultative Project (no. 2018-ZD-05); Sichuan Provincial Science and Technology Support Project (nos. 2016GZ0338, 18MZGC0247, 18MZGC0186, and 2019JDRC0133); 2017-2019 Young Elite Scientist Sponsorship Program by CAST (YESS); Nanchang Railway Bureau Scientific Research Project (nos. 20171106 and 201710); Technology Research and Development Project of China Railway Eryuan Engineering Group Co. Ltd. (nos. KYY2017069(17-17) and KYY2019070(19-20)).

References

National Development and Reform Commission, Ministry of Foreign Affairs, and Ministry of Commerce, Vision and action to promote the Silk Road Economic Belt and the 21st Century Maritime Silk Road, vol. 3-29, No. 4, The People Daily, 2015.
A. Vangeli, “China's engagement with the sixteen countries of central, east and southeast Europe under the belt and road initiative,” China and World Economy, vol. 25, no. 5, pp. 101–124, 2017.
View at: Publisher Site | Google Scholar
E. Fardella and G. Prodi, “The belt and road initiative impact on Europe: an Italian perspective,” China and World Economy, vol. 25, no. 5, pp. 125–138, 2017.
View at: Publisher Site | Google Scholar
L. Zhang, H. Du, Y. Zhao, R. Wu, and X. Zhang, “Urban networks among Chinese cities along "the belt and road": a case of web search activity in cyberspace,” PLoS ONE, vol. 12, no. 12, pp. 1–20, 2017.
View at: Google Scholar
Y. Zhao, J. Guo, X. Zhao, L. Tang, and Y. Wang, “The characteristics of city network along Belt and Road in China based on railway and aviation passenger transport,” Journal of Arid Land Resources and Environment, vol. 31, no. 01, pp. 51–57, 2017.
View at: Google Scholar
W. Cui, H. Xuexi, Z. Shihong, and Z. Hongliang, “Risk evaluation model of agricultural high-tech investment project based on BP neural network,” Journal of Northwest Agriculture and Forestry University (Natural Science Edition), vol. 34, no. 7, pp. 160–164, 2006.
View at: Google Scholar
X. Wang, S. Feng, Y. Lei, and L. Yang, 43 case analysis of MATLAB neural network, Beijing University of Aeronautics and Astronautics Press, 2013.
W. Mccluskey, P. Davis, M. Haran, M. Mccord, and D. Mcilhatton, “The potential of artificial neural networks in mass appraisal: the case revisited,” Journal of Financial Management of Property and Construction, vol. 17, no. 3, pp. 274–292, 2012.
View at: Publisher Site | Google Scholar
T.-S. Quah and B. Srinivasan, “Improving returns on stock investment through neural network selection,” Expert Systems with Applications, vol. 17, no. 4, pp. 295–301, 1999.
View at: Publisher Site | Google Scholar
A. Saidi and M. Mirzaei, “Application of gold-labeled antibody biosensor in simultaneous determination of total aflatoxins using artificial neural network,” Journal of the Iranian Chemical Society, vol. 11, no. 2, pp. 391–398, 2014.
View at: Publisher Site | Google Scholar
G. Satyanarayana, G. Swami Naidu, and N. H. Babu, “Artificial neural network and regression modelling to study the effect of reinforcement and deformation on volumetric wear of red mud nano particle reinforced aluminium matrix composites synthesized by stir casting,” Boletín de la Sociedad Española de Cerámica y Vidrio, vol. 57, no. 3, pp. 91–100, 2018.
View at: Publisher Site | Google Scholar
L. L. Kien, C. Li-Lee, V. C. Chong et al., “Automated identification of copepods using digital image processing and artificial neural network,” BMC Bioinformatics, vol. 16, no. S18, 2015.
View at: Google Scholar
G. B. José, H. J. Alfredo, A. M. Joel et al., “Estimation of umbilical cord blood leptin and insulin based on anthropometric data by means of artificial neural network approach: identifying key maternal and neonatal factors,” Bmc Pregnancy & Childbirth, vol. 16, no. 1, p. 17, 2016.
View at: Google Scholar
A. N. Ran, Research on Risk Evaluation of Logistics Park Construction Based on BP Neural Network, Chang’An University, 2013.
A. B. Badiru and D. B. Sieger, “Neural network as a simulation metamodel in economic analysis of risky projects,” European Journal of Operational Research, vol. 105, no. 1, pp. 130–142, 1998.
View at: Publisher Site | Google Scholar
M. Chen, Principles and Examples of MATLAB Neural Network, Tsinghua University Press, 2013.
L. Zhang, K. Wu, Y. Zhong, and P. Li, “A new sub-pixel mapping algorithm based on a BP neural network with an observation model,” Neurocomputing, vol. 71, no. 10-12, pp. 2046–2054, 2008.
View at: Publisher Site | Google Scholar
F. Yu and X. Z. Xu, “A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network,” Applied Energy, vol. 134, pp. 102–113, 2014.
View at: Publisher Site | Google Scholar
B. H. M. Sadeghi, “A BP-neural network predictor model for plastic injection molding process,” Journal of Materials Processing Technology, vol. 103, no. 3, pp. 411–416, 2000.
View at: Publisher Site | Google Scholar
S. Wang, N. Zhang, L. Wu, and Y. Wang, “Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method,” Journal of Renewable Energy, vol. 94, pp. 629–636, 2016.
View at: Publisher Site | Google Scholar
C. Ren, N. An, J. Wang, L. Li, B. Hu, and D. Shang, “Optimal parameters selection for BP neural network based on particle swarm optimization: a case study of wind speed forecasting,” Knowledge-Based Systems, vol. 56, pp. 226–239, 2014.
View at: Publisher Site | Google Scholar
S. Ding and C. Su, “Application of optimizing BP neural networks algorithm based on genetic algorithm,” in Proceedings of the 29th Chinese Control Conference, CCC'10, pp. 2425–2428, China, July 2010.
View at: Google Scholar
Z.-H. Guo, J. Wu, H.-Y. Lu, and J.-Z. Wang, “A case study on a hybrid wind speed forecasting method using BP neural network,” Knowledge-Based Systems, vol. 24, no. 7, pp. 1048–1056, 2011.
View at: Publisher Site | Google Scholar
W. K. Wong, C. W. M. Yuen, D. D. Fan, L. K. Chan, and E. H. K. Fung, “Stitching defect detection and classification using wavelet transform and BP neural network,” Expert Systems with Applications, vol. 36, no. 2, pp. 3845–3856, 2009.
View at: Publisher Site | Google Scholar
L. J. Guo, J. J. Gao, J. F. Yang, and J. X. Kang, “Criticality evaluation of petrochemical equipment based on fuzzy comprehensive evaluation and a BP neural network,” Journal of Loss Prevention in the Process Industries, vol. 22, no. 4, pp. 469–476, 2009.
View at: Publisher Site | Google Scholar
Z. Guoyi and H. Zheng, “Improved BP neural network model and its stability analysis,” Journal of Central South University, vol. 42, no. 1, pp. 115–124, 2011.
View at: Google Scholar
N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in Proceedings of the 2017 IEEE Symposium on Security and Privacy, SP 2017, pp. 39–57, May 2017.
View at: Google Scholar
C. Melchiorre, M. Matteucci, and J. Remondo, “Artificial neural networks and robustness analysis in landslide susceptibility zonation,” in Proceedings of the International Joint Conference on Neural Networks 2006, IJCNN '06, pp. 4375–4381, Canada, July 2006.
View at: Google Scholar
S. Ding and Q. Wu, “Research on robustness of BP neural network based inverse model for induction motor drives,” in Proceedings of the International Conference on Electronics and Optoelectronics (ICEOE '11), vol. 2, pp. V2127–V2131, July 2011.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2019 Yang Changwei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

2127

Downloads

1503

Citations