Theory and Application of Uniform Experimental Designs

Fang, Kai‐Tai; Liu, Min-Qian; Qin, Hong; Zhou, Yixiao

doi:10.1007/978-981-13-2041-5

Cited by 124 publications

(93 citation statements)

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“…Thus, the first target was to model the variation of the BGE in each ternary system A, B, and C and to provide a suitable statistical instrument for describe these mixtures. In order to reach such goal, a DoE approach was used, and in particular, a Simple Lattice three-components mixture experiment was implemented [ 28 ].…”

Section: Resultsmentioning

confidence: 99%

Behavior of Ternary Mixtures of Hydrogen Bond Acceptors and Donors in Terms of Band Gap Energies

et al. 2021

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Three ternary mixtures composed by choline chloride (ChCl), ethylene glycol (EG), and a second hydrogen bond donor (HBD) as ethanol (A), 2-propanol (B), and glycerol (C) were studied in terms of composition related to the band gap energy (BGE). A Design of Experiments (DoE) approach, and in particular a Simple Lattice three-components design, was employed for determining the variation of the BGE upon the composition of each system. UV-VIS analysis and subsequent Tauc plot methodology provided the data requested from the DoE, and multivariate statistical analysis revealed a drop of the BGE in correspondence to specific binary compositions for systems A and B. In particular, a BGE of 3.85 eV was registered for the mixtures ChCl/EtOH (1:1) and ChCl/2-propanol (1:1), which represents one of the lowest values ever observed for these systems.

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Section: Resultsmentioning

confidence: 99%

Behavior of Ternary Mixtures of Hydrogen Bond Acceptors and Donors in Terms of Band Gap Energies

et al. 2021

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“…In order to reach such goal, a DoE approach was used and in particular a Simple Lattice three-components mixture experiment was implemented. 28 Seven different molar combinations were prepared for each system and the corresponding BGEs were determined by the known graphic UV-VIS-based Tauc plot method, following a procedure previous optimized by some of us [24].…”

Section: Resultsmentioning

confidence: 99%

Behaviour of Ternary Mixtures of Hydrogen Bond Acceptors and Donors in Terms of Band Gap Energies

Mannu¹,

Cardano²,

Baldino³

et al. 2021

Preprint

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Three ternary mixtures composed by choline chloride (ChCl), ethylene glycol (EG) and a second hydrogen bond donor (HBD) as ethanol (A), 2-propanol (B), and glycerol (C) were studied in terms of composition related to the band gap energy (BGE). A Design of Experiments (DoE) approach, and in particular a Simple Lattice three-components design, was employed for determining the variation of the BGE upon the composition of each system. UV-VIS analysis and subsequent Tauc plot methodology provided the data requested from the DoE and multivariate statistical analysis revealed a drop of the BGE in correspondence to specific binary compositions for systems A and B. In particular, a BGE of 3.85 eV was registered for the mixtures ChCl/EtOH (1:1), and ChCl/2-propanol (1:1), which represents one of the lowest values ever observed for these systems.

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“…Herein, the concept of optimizing the model structure (hyper parameters) is utilized [ 54 ]. An improvement by uniform experimental design (UED) [ 55 ], a neural network, and a PSO algorithm is introduced. It preserves the ability of the CNN and optimizes the performance.…”

Section: Machining Roughness Estimation Applicationmentioning

confidence: 99%

Deep Learning Approach for Vibration Signals Applications

Chen

Lee

2021

Sensors

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This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

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Theory and Application of Uniform Experimental Designs

Cited by 124 publications

References 0 publications

Behavior of Ternary Mixtures of Hydrogen Bond Acceptors and Donors in Terms of Band Gap Energies

Behavior of Ternary Mixtures of Hydrogen Bond Acceptors and Donors in Terms of Band Gap Energies

Behaviour of Ternary Mixtures of Hydrogen Bond Acceptors and Donors in Terms of Band Gap Energies

Deep Learning Approach for Vibration Signals Applications

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