Mechanism of Microstructural Variation Under Cyclic Shearing of Shantou Marine Clay: Experimental Investigation and Model Development

Lin, Peihua; Ratnam, Raval; Sankari, Hima; Garg, Akhil

doi:10.1007/s10706-019-00900-6

Cited by 11 publications

(9 citation statements)

References 20 publications

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“…The procedure followed and the parameters used to perform experiments on the batteries are taken from our previously published work. 16 Battery's full voltage, zero voltage, and discharge capacity were measured with respect to number of cycles and charging ratio. In this procedure, all the test would follow a rest step to make sure the battery can work well after 158 cycles.…”

Section: Methodsmentioning

confidence: 99%

“…The statistical parameters/setting of the experimental data (input settings of ANN used in STATISTICA for evaluating the interactions of various inputs on the discharge capacity of the Li-ion batteries) used for training, validating, and testing have been taken from our previously published work. 16 ANNs were used to develop models for the experimental values to predict the stabilized lowest, average, and highest discharge capacity of Li-ion batteries. ANS using STATISTICA 12.5 was considered to generate the model for the obtained data.…”

Section: Artificial Neural Network Frameworkmentioning

confidence: 99%

“…Moreover, during the charging/discharging cycles, the highly active materials on the electrodes would form new structures in the electrochemical reaction, which is called electrochemical reconstruction. 16,17 Further, it has been investigated by EDS analysis. The EDS spectrums of cathode and anode materials before and after experiments have been represented by Figure 13A-D, respectively.…”

Section: Sem and Eds Analysismentioning

confidence: 99%

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Illustration of experimental, machine learning, and characterization methods for study of performance of Li‐ion batteries

Garg

Singh

et al. 2020

Int J Energy Res

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The development of fault diagnosis of Li-ion batteries used in electric vehicles is vital. In this perspective, the present work conducted a comprehensive study for the evaluation of coupled and interactive influence of charging ratio, number of cycles, and voltage on the discharge capacity of Li-ion batteries to predict the life of battery. The charging-discharging experimental tests on Li-ion batteries have been performed. The data such as charging ratio, number of cycles, voltage, and discharge capacity of Li-ion batteries are measured. Machine learning approach of neural networks is then applied on the obtained data to compute the effects, normal distribution, parametric analysis, and sensitivity analysis of the input parameters on the capacity of battery. It can be noticed that discharge capacity increased with an increase in full voltage. Further, it has been observed from the sensitivity analysis that the full voltage is most relevant parameters to the capacity of the battery. Additionally, scanning electron microscopy/energy dispersive spectroscopy (SEM/EDS) of the electrodes before and after experiments have been performed, to investigate the elemental dissolution due to the charging/discharging cycles. The findings and analysis from the proposed study shall facilitate experts in making decisions on the remaining life and charging capacity of the battery.

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Section: Methodsmentioning

confidence: 99%

Section: Artificial Neural Network Frameworkmentioning

confidence: 99%

Section: Sem and Eds Analysismentioning

confidence: 99%

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Illustration of experimental, machine learning, and characterization methods for study of performance of Li‐ion batteries

Garg

Singh

et al. 2020

Int J Energy Res

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“…The update gate is used to balance the proportion of historical memory information and the input information at the current time, and the reset gate determines to forget part of the state information of the hidden layer at the previous moment. The smaller the update gate value, the more inclined the model output is to the state of the upper hidden layer, and the smaller the reset gate value, the less historical information is introduced [12][13][14]. Both values depend on the hidden layer state ht at the previous time step in the network and the input xt at the current time step, as shown in Formulas (10) and (11).…”

Section: Gru Neural Networkmentioning

confidence: 99%

Music Audio Rhythm Recognition Based on Recurrent Neural Network

Chen

2022

Wireless Communications and Mobile Computing

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Music rhythm detection and tracking is an important part of music understanding system and music visualization system. Based on the important position of rhythm in music expression and the wide range of multimedia applications, rhythm extraction has become an important hotspot in computer music analysis. In the field of audio recognition research, deep learning can automatically learn the features of audio and extract the rhythm of music. This paper takes music audio rhythm recognition as the main research object and carries out a series of researches with deep learning GRU neural network as the main technical support. A residual network is introduced into the GRU network model, and it is found that when the residual network is at 50 layers, the model has the highest accuracy for audio rhythm extraction. After adjusting the model parameters through experiments, this paper concludes that the average recognition accuracy of the ResNet_50-GRU model for recognizing the rhythm of the music audio in the MSD, AudioSet, and FMA data sets is 92.5%.

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“…In recent years, frozen soil constitutive theory has been being studied by some methods, such as experimental research, theoretical derivation, formula fitting, numerical simulation of particle flow, and data-driven deep learning. Numerous ML algorithms have been used to study thawing and frozen soil constitutive, such as Evolutionary Polynomial Regression (EPR) (Nassr et al, 2018), Support Vector Machine (SVM) (Zhao et al, 2014;Kohestani and Hassanlourad, 2016), Back Propagation Neural Network (BPNN) (Shahin and Indraratna, 2006;Johari et al, 2011;Rashidian and Hassanlourad, 2014;Stefanos and Gyan, 2015;Lin et al, 2019), radial basis function (RBF) neural network (Peng et al, 2008), recurrent neural network (RNN) (Zhu et al, 1998;Romo et al, 2001), long short-term memory (LSTM) neural network . Problems such as gradient explosion or gradient disappearance can be better avoided by LSTM neural network.…”

Section: Introductionmentioning

confidence: 99%

Study on triaxial test and constitutive prediction model of frozen silty clay

Teng

Teng²,

Liu³

et al. 2023

Front. Earth Sci.

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With the increasing demand for engineering construction in the seasonal frozen area and the background of the Belt and Road Initiative, the frozen soil constitutive model should be studied in depth. At present, the constitutive prediction model of frozen silty clay has many problems, such as complex formula, single model application and poor prediction ability. Random forest optimal model hyperparameter input was very difficult. Particle Swarm Optimization (PSO) was used to optimize the parameters of the number of neurons, dropout and batch_size in the Long-term and Short-Term Memory network (LSTM) structure. The optimization results were 61, 0.09 and 95 respectively. The results showed that the strength tended to be stable after 6,9,6,9 and 9 freeze-thaw cycles under initial moisture content = 25, 22.5, 20, 17.5, and 15%, respectively. After 18 freeze-thaw cycles, the strength decreased by 2.66%, 11.85%, 18.83%, 16.79, and 29.02%, respectively. The predicted values of frozen soil binary medium model (BM), random forest model (RF) and PSO-LSTM model were compared with the measured values under different working conditions, and good accuracy was obtained. The R2 of the PSO-LSTM model test set was trained to more than 98%, and RMSE, MAE and MAPE were also trained to the lowest under the same working conditions. The influencing factors of deviator stress of frozen silty clay were given in order from strong to weak: initial moisture content>strain>confining pressure>number of freeze-thaw cycles. The LSTM optimal combination input parameters were searched by PSO, and the parameter adjustment speed of the model for the data learning process of frozen silty clay was greatly increased, which was conducive to the promotion of other soil constitutive prediction models. A new constitutive prediction model of frozen silty clay was developed using PSO-LSTM algorithm. 15 working conditions had been verified, and the optimal model had high accuracy in the constitutive prediction of frozen silty clay, which provided a good reference for the application of frozen soil engineering in cold regions.

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Mechanism of Microstructural Variation Under Cyclic Shearing of Shantou Marine Clay: Experimental Investigation and Model Development

Cited by 11 publications

References 20 publications

Illustration of experimental, machine learning, and characterization methods for study of performance of Li‐ion batteries

Illustration of experimental, machine learning, and characterization methods for study of performance of Li‐ion batteries

Music Audio Rhythm Recognition Based on Recurrent Neural Network

Study on triaxial test and constitutive prediction model of frozen silty clay

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