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  • Open Access


    Assessing the Forecasting of Comprehensive Loss Incurred by Typhoons: A Combined PCA and BP Neural Network Model

    Shuai Yuan1, Guizhi Wang1,*, Jibo Chen1, Wei Guo2

    Journal on Artificial Intelligence, Vol.1, No.2, pp. 69-88, 2019, DOI:10.32604/jai.2019.06535

    Abstract This paper develops a joint model utilizing the principal component analysis (PCA) and the back propagation (BP) neural network model optimized by the Levenberg Marquardt (LM) algorithm, and as an application of the joint model to investigate the damages caused by typhoons for a coastal province, Fujian Province, China in 2005-2015 (latest). First, the PCA is applied to analyze comprehensively the relationship between hazard factors, hazard bearing factors and disaster factors. Then five integrated indices, overall disaster level, typhoon intensity, damaged condition of houses, medical rescue and self-rescue capability, are extracted through the PCA; Finally, the BP neural network model,… More >

  • Open Access


    Comparisons of MFDFA, EMD and WT by Neural Network, Mahalanobis Distance and SVM in Fault Diagnosis of Gearboxes

    Jinshan Lina*, Chunhong Doub, Qianqian Wanga

    Sound & Vibration, Vol.52, No.2, pp. 11-15, 2018, DOI:10.32604/sv.2018.03653

    Abstract A method for gearbox fault diagnosis consists of feature extraction and fault identification. Many methods for feature extraction have been devised for exposing nature of vibration data of a defective gearbox. In addition, features extracted from gearbox vibration data are identified by various classifiers. However, existing literatures leave much to be desired in assessing performance of different combinatorial methods for gearbox fault diagnosis. To this end, this paper evaluated performance of several typical combinatorial methods for gearbox fault diagnosis by associating each of multifractal detrended fluctuation analysis (MFDFA), empirical mode decomposition (EMD) and wavelet transform (WT) with each of neural… More >

  • Open Access


    Generating Questions Based on Semi-Automated and End-to-End Neural Network

    Tianci Xia1,2, Yuan Sun1,2,*, Xiaobing Zhao1,2, Wei Song1, Yumiao Guo3

    CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 617-628, 2019, DOI:10.32604/cmc.2019.05860

    Abstract With the emergence of large-scale knowledge base, how to use triple information to generate natural questions is a key technology in question answering systems. The traditional way of generating questions require a lot of manual intervention and produce lots of noise. To solve these problems, we propose a joint model based on semi-automated model and End-to-End neural network to automatically generate questions. The semi-automated model can generate question templates and real questions combining the knowledge base and center graph. The End-to-End neural network directly sends the knowledge base and real questions to BiLSTM network. Meanwhile, the attention mechanism is utilized… More >

  • Open Access


    Prediction of Dendritic Parameters and Macro Hardness Variation in PermanentMould Casting of Al-12%Si Alloys Using Artificial Neural Networks

    E. Abhilash1, M.A. Joseph1, Prasad Krishna1

    FDMP-Fluid Dynamics & Materials Processing, Vol.2, No.3, pp. 211-220, 2006, DOI:10.3970/fdmp.2006.002.211

    Abstract Aluminium-Silicon alloys are in high de-mand as an engineering material for automotive,aerospace and other engineering applications. Mechanical properties of Al-Si alloys depend not only on chemical composition but also more importantly on microstructural features such as dendritic alpha-aluminiumphase and eutectic silicon particles. As an additive to Al-Si alloys, sodium improves mechanical properties byforming finer and fewer needles like microstructures.Thus, prediction of the macro and microstructures obtained at the end of the solidification is of great interest for the manufacturer of aluminium alloys. Neuralnetworks are sophisticated nonlinear regression routinesthat, when properly “trained”, allow for the identificationof complex relationships between a series… More >

  • Open Access


    An Image Classification Method Based on Deep Neural Network with Energy Model

    Yang Yang1,*, Jinbao Duan1, Haitao Yu1, Zhipeng Gao1, Xuesong Qiu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 555-575, 2018, DOI:10.31614/cmes.2018.04249

    Abstract The development of deep learning has revolutionized image recognition technology. How to design faster and more accurate image classification algorithms has become our research interests. In this paper, we propose a new algorithm called stochastic depth networks with deep energy model (SADIE), and the model improves stochastic depth neural network with deep energy model to provide attributes of images and analysis their characteristics. First, the Bernoulli distribution probability is used to select the current layer of the neural network to prevent gradient dispersion during training. Then in the backpropagation process, the energy function is designed to optimize the target loss… More >

  • Open Access


    AdaBoosting Neural Network for Short-Term Wind Speed Forecasting Based on Seasonal Characteristics Analysis and Lag Space Estimation

    Haijian Shao1, 2, Xing Deng1, 2, *

    CMES-Computer Modeling in Engineering & Sciences, Vol.114, No.3, pp. 277-293, 2018, DOI:10.3970/cmes.2018.114.277

    Abstract High accurary in wind speed forcasting remains hard to achieve due to wind’s random distribution nature and its seasonal characteristics. Randomness, intermittent and nonstationary usually cause the portion problem of the wind speed forecasting. Seasonal characteristics of wind speed means that its feature distribution is inconsistent. This typically results that the persistence of excitation for modeling can not be guaranteed, and may severely reduce the possibilities of high precise forecasting model. In this paper, we proposed two effective solutions to solve the problems caused by the randomness and seasonal characteristics of the wind speed. (1) Wavelet analysis is used to… More >

  • Open Access


    Ensemble Recurrent Neural Network-Based Residual Useful Life Prognostics of Aircraft Engines

    Jun Wu1,*, Kui Hu1, Yiwei Cheng2, Ji Wang1, Chao Deng2,*, Yuanhan Wang3

    Structural Durability & Health Monitoring, Vol.13, No.3, pp. 317-329, 2019, DOI:10.32604/sdhm.2019.05571

    Abstract Residual useful life (RUL) prediction is a key issue for improving efficiency of aircraft engines and reducing their maintenance cost. Owing to various failure mechanism and operating environment, the application of classical models in RUL prediction of aircraft engines is fairly difficult. In this study, a novel RUL prognostics method based on using ensemble recurrent neural network to process massive sensor data is proposed. First of all, sensor data obtained from the aircraft engines are preprocessed to eliminate singular values, reduce random fluctuation and preserve degradation trend of the raw sensor data. Secondly, three kinds of recurrent neural networks (RNN),… More >

  • Open Access


    Applying Neural Networks for Tire Pressure Monitoring Systems

    Alex Kost1, Wael A. Altabey2,3,4, Mohammad Noori1,2,*, Taher Awad4

    Structural Durability & Health Monitoring, Vol.13, No.3, pp. 247-266, 2019, DOI:10.32604/sdhm.2019.07025

    Abstract A proof-of-concept indirect tire-pressure monitoring system is developed using artificial neural networks to identify the tire pressure of a vehicle tire. A quarter-car model was developed with MATLAB and Simulink to generate simulated accelerometer output data. Simulation data are used to train and evaluate a recurrent neural network with long short-term memory blocks (RNN-LSTM) and a convolutional neural network (CNN) developed in Python with Tensorflow. Bayesian Optimization via SigOpt was used to optimize training and model parameters. The predictive accuracy and training speed of the two models with various parameters are compared. Finally, future work and improvements are discussed. More >

  • Open Access


    Probabilistic Neural Network for Predicting the Stability numbers of Breakwater Armor Blocks

    Doo Kie Kim1, Dong Hyawn Kim2, Seong Kyu Chang1, Sang Kil Chang1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.2, No.2, pp. 35-40, 2007, DOI:10.3970/icces.2007.002.035

    Abstract The stability numbers determining the Armor units are very important to design breakwaters, because armor units are designed for defending breakwaters from repeated wave loads. This study presents a probabilistic neural network (PNN) for predicting the stability number of armor blocks of breakwaters. PNN used the experimental data of van der Meer as train and test data. The estimated results of PNN were compared with those of empirical formula and previous artificial neural network (ANN) model. The comparison results showed the efficiency of the proposed method in the prediction of the stability numbers in spite of data incompleteness and incoherence.… More >

  • Open Access


    Advanced Probabilistic Neural Network for the Prediction of Concrete Strength

    Doo Kie Kim1, Seong Kyu Chang1, Sang Kil Chang1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.2, No.1, pp. 29-34, 2007, DOI:10.3970/icces.2007.002.029

    Abstract Accurate and realistic strength estimation before the placement of concrete is highly desirable. In this study, the advanced probabilistic neural network (APNN) was proposed to reflect the global probability density function by summing the heterogeneous local probability density function automatically determined in the individual standard deviation of variables. Currently, the estimation of the compressive strength of concrete is performed by a probabilistic neural network (PNN) on the basis of concrete mix proportions, and the PNN is improved by the iteration method. However, an empirical method has been incorporated to specify the smoothing parameter in the PNN technique, causing significant uncertainty… More >

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