Shaofei Ren^1,2, Guorong Chen² , Tiange Li² , Qijun Chen², Shaofan Li^{2, *}

CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 287-307, 2018, DOI:10.31614/cmes.2018.04697

Abstract In this work, we have developed a novel machine (deep) learning computational framework to determine and identify damage loading parameters (conditions) for structures and materials based on the permanent or residual plastic deformation distribution or damage state of the structure. We have shown that the developed machine learning algorithm can accurately and (practically) uniquely identify both prior static as well as impact loading conditions in an inverse manner, based on the residual plastic strain and plastic deformation as forensic signatures. The paper presents the detailed machine learning algorithm, data acquisition and learning processes, and validation/verification examples. This development may have… More >

Rahul Kumar Sharma^*1, V. Sugumaran¹, Hemantha Kumar², Amarnath M³

Structural Durability & Health Monitoring, Vol.11, No.1, pp. 1-16, 2017, DOI:10.3970/sdhm.2017.012.001

Abstract Most of the machineries in small or large scale industry have rotating element supported by bearings for rigid support and accurate movement. For proper functioning of machinery, condition monitoring of the bearing is very important. In present study sound signal is used to continuously monitor bearing health as sound signals of rotating machineries carry dynamic information of components. There are numerous studies in literature that are reporting superiority of vibration signal of bearing fault diagnosis. However, there are very few studies done using sound signal. The cost associated with condition monitoring using sound signal (Microphone) is less than the cost… More >

Xu Zhang¹, Wenpeng Lu^1,*, Fangfang Li^2,3, Xueping Peng³, Ruoyu Zhang¹

CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 601-616, 2019, DOI:10.32604/cmc.2019.06045

Abstract Sentence semantic matching (SSM) is a fundamental research in solving natural language processing tasks such as question answering and machine translation. The latest SSM research benefits from deep learning techniques by incorporating attention mechanism to semantically match given sentences. However, how to fully capture the semantic context without losing significant features for sentence encoding is still a challenge. To address this challenge, we propose a deep feature fusion model and integrate it into the most popular deep learning architecture for sentence matching task. The integrated architecture mainly consists of embedding layer, deep feature fusion layer, matching layer and prediction layer.… More >

Xia Li^1,2, Peifeng Niu^1,*, Jianping Liu², Qing Liu²

CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.1, pp. 29-57, 2018, DOI:10.31614/cmes.2018.04020

Abstract An improved teaching-learning-based optimization (I-TLBO) algorithm is proposed to adjust the parameters of extreme learning machine with parallel layer perception (PELM), and a well-generalized I-TLBO-PELM model is obtained to build the model of NO_X emissions of a boiler. In the I-TLBO algorithm, there are four major highlights. Firstly, a quantum initialized population by using the qubits on Bloch sphere replaces a randomly initialized population. Secondly, two kinds of angles in Bloch sphere are generated by using cube chaos mapping. Thirdly, an adaptive control parameter is added into the teacher phase to speed up the convergent speed. And then, according to… More >

Yang Yang^1,*, 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 >

A. M. E. Ramírez-Mendoza¹

CMES-Computer Modeling in Engineering & Sciences, Vol.115, No.3, pp. 295-311, 2018, DOI: 10.3970/cmes.2018.00239

Abstract A spike response model (SRM) based on the spikes generator circuit (SGC) of adaptive fuzzy spiking neurons (AFSNs) is developed. The SRM is simulated in MatlabTM environment. The proposed model is applied to a configuration of a fuzzy exclusive or (fuzzy XOR) operator, as an illustrative example. A description of the comparison of AFSNs with other similar methods is given. The novel method of the AFSNs is used to determine the value of the weights or parameters of the fuzzy XOR, first with dynamic weights or self-tuning parameters that adapt continuously, then with fixed weights obtained after training, finally with… More >

Minshan Li^1,3, Dongchuan Mo^2,3*, Shushen Lyu^2,3*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.3, pp. 165-165, 2019, DOI:10.32604/icces.2019.05510

Abstract In the past few years, machine learning algorithms and models have shown great power in the emerging field of data mining and artificial intelligence, attracting a great deal of attention. Given specific learning task and training data set, a machine learning model can improve automatically through training and can help people make decisions and predictions. To date, a lot of advanced machine learning algorithms and theories have been proposed and developed, including random forest, support vector machine, artificial neural network, deep learning and so on. Well-chosen and well-trained machine learning model is proved to have high efficiency, accuracy and robustness,… More >

Satoshi Minamoto*, Takuya Kadohira, Kaita Ito, Makoto Watanabe, Masahiko Demura

The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.2, pp. 136-136, 2019, DOI:10.32604/icces.2019.05447

Abstract The Materials Integration (MI) System is a domestically developed system in the “Cross-ministerial Strategic Innovation Promotion Program” to analyze structural materials performance. The performance on structural materials having complicated inputs/outputs would be solved with the combination of different scientific programs or data from experiment. One of the merits of constructing a combined model (here we call workflow) is that calculations are performed and the data would be stored in the system automatically.
Furthermore, we developed a web application (“MIREA”: MI REgression Analyzer) that enables us to build high versatile prediction models based on machine learning techniques by using the… More >

Masahiko Demura^1,*, Junya Sakurai^1,2, Masayoshi Yamazaki¹, Junya Inoue^1,2

The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.2, pp. 123-123, 2019, DOI:10.32604/icces.2019.05303

Abstract Creep is a complicated and time-dependent phenomenon, which is affected by the initial state and the degradation of microstructures. It is thus considered that the information about the microstructure is essential to predict the creep rupture time. On the other hand, there is a strong, practical need for the prediction without the investigation of microstructures nor the disclosure of the detailed process that should control the initial microstructures. In this study, we examined how modern machine learning technique can help to predict the creep rupture time in heat-resistant ferrite-type steels without the direct information about the microstructures and the process… More >

Dmitry S. Bulgarevich^1,*, Susumu Tsukamoto¹, Tadashi Kasuya², Masahiko Demura¹, Makoto Watanabe^1,3

The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.2, pp. 122-122, 2019, DOI:10.32604/icces.2019.05271

Abstract The microstructure of steel greatly determines its mechanical properties/performance and holds information on chemical composition and processing history. Therefore, quantitative analysis of optical or SEM images on formed microstructure phases is one of the primary interests for metallurgy. So far, such analyses in laboratories are done manually by experts and are very time consuming. However, with modern microscopy techniques of automated image acquisitions over the large imaging areas and even by using of sample slicing for three-dimensional imaging, the amount of image data could be overwhelming for manual examinations. In this respect, there is a possibility that machine learning (ML)… More >