The Effect of Machine Learning Algorithms on the Prediction of Layer-by-Layer Coating Properties by Tijana Šušteršič (14519970)

“…In this paper, we compared several ML algorithms for optimizing a methodology for coating thickness prediction, namely, linear regression, Support Vector Regressor, Random Forest Regressor, and Extra Tree Regressor. …”

Determination of the angular parameter of the loose layer and bedrock (direction). by Liangliang Yang (3814120)

DNN structure consisting of many hidden layers. by Cihun-Siyong Alex Gong (11874066)

Modern LSTM units and its layer structure are illustrated. by Cihun-Siyong Alex Gong (11874066)

The confusion matrix of DNN-12-layer model with MFCC datasets. by Cihun-Siyong Alex Gong (11874066)

The confusion matrix of DNN-12-layer model with LPC datasets. by Cihun-Siyong Alex Gong (11874066)

Judgment matrix of control layer U. by Yixuan Lu (9987980)

The confusion matrix of PCA-DNN-12-layer model with MFCC datasets. by Cihun-Siyong Alex Gong (11874066)

The confusion matrix of PCA-DNN-12-layer model with LPC datasets. by Cihun-Siyong Alex Gong (11874066)

Relationship between strike shift angle, boundary angle, and mining depth/loose layer thickness. by Liangliang Yang (3814120)

Relationship between uphill movement angle, boundary angle, and mining depth/thickness of the loose layer. by Liangliang Yang (3814120)

Multi-scale representation of two-dimensional linear graphs. by Guangmei Ma (21432861)

Subjects:

Judgment matrix of criterion layer U₃. by Yixuan Lu (9987980)

Judgment matrix of criterion layer U₄. by Yixuan Lu (9987980)

Judgment matrix of criterion layer U₂. by Yixuan Lu (9987980)

Judgment matrix of criterion layer U₁. by Yixuan Lu (9987980)

Expert scoring values of criterion layer U₁. by Yixuan Lu (9987980)

Schematic of the suggested ROSA network for 16-element linear array consisting of power division, phase control, amplitude control of primary and secondary array. by Maryam Shadi (14237349)

Subjects:

Order of locations tested in the SORS algorithm using four different reconstruction models. by Runjie Bill Shi (18309700)

Comparison of optimization results. by Lijie Sun (224007)

“…The process employs Maximum Second-order Cyclostationary Blind Deconvolution (CYCBD) to filter out noise from the vibration signals emitted by bearings; secondly, considering the issue with the conventional Harris Hawks Optimization (HHO) algorithm which tends to prematurely converge to local optima, the differential evolution mutation operator is introduced and the escape energy factor is improved from linear to nonlinear in IHHO; then, a double-layer network model based on DBN-ELM is proposed, to avoid the number of hidden layer nodes of DBN from human experience interference, and IHHO is used to optimize DBN structure, which is denoted as IHHO-DBN-ELM method; with the optimal structure is obtained by using a combined IHHO optimized DBN and ELM; in conclusion, the proposed IHHO-DBN-ELM approach is applied to the bearing fault detection using the Western Reserve University’s bearing fault dataset. …”