Transfer learning‐based online multiperson tracking with Gaussian process regression

Zhang, Baobing; Li, Siguang; Huang, Zhengwen; Rahi, Babak H.; Wang, Qicong; Li, Maozhen

doi:10.1002/cpe.4917

Cited by 9 publications

(5 citation statements)

References 40 publications

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“…the model hyper-parameters are constant when transferring from one task to another thus reducing the training data required for the new task. This is mostly implemented for deep learning models where data requirements can be significant, although, implementation for regression models like decision trees 152 and Gaussian Process 153 can also be found. Transfer learning is an effective tool when computing binding energies of related adsorbates ( e.g.…”

Section: Catalyst Screeningmentioning

confidence: 99%

The design and optimization of heterogeneous catalysts using computational methods

Shambhawi,

Mohan,

Choksi

et al. 2024

Catal. Sci. Technol.

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Section: Catalyst Screeningmentioning

confidence: 99%

The design and optimization of heterogeneous catalysts using computational methods

Shambhawi,

Mohan,

Choksi

et al. 2024

Catal. Sci. Technol.

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“…The first application of DL algorithms had been started with image processing applications, where these algorithms achieved outstanding results compared to their counterpart methods. Nowadays, DL algorithms had become a new trending solution method for solving challenging problems in many fields such as segmentation, 45,46 multiobject tracking, 47,48 biomedical applications, 49,50 lip reading, 51 activity recognition of humans via mobile sensors, 52 remaining life estimation of subsystems via sensor data, 53 estimation of time series of financial data, 54 power and speed estimation of wind, 55,56 electrical impedance tomography (EIT) imaging, 57 full‐wave nonlinear inverse scattering problems, 58 large‐scale offline signature recognition, 59 high‐speed receiver adaptation, 60 parametric modeling, and extraction of passive microwave components 7,61 …”

Section: Case Studymentioning

confidence: 99%

Artificial intelligence–based design optimization of nonuniform microstrip line band pass filter

Mahouti

Yıldırım

Kuskonmaz

2021

Int J Numerical Modelling

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Design optimization of many electromagnetic and multiphysics problems have multiscale issues that require a fast, efficient, and accurate surrogate‐based model to be used. Recently, in microwave engineering field, artificial intelligence–based models are being used for modeling of complex microwave stages. In all the studies, the main aim is to form models inner structure parameters, by using the given data to predict the linear/nonlinear relationships between given inputs and outputs. Herein, a surrogate‐based model of a nonuniform microstrip transmission line (NTL) with a typical application of design optimization of a band‐pass filter for ISM band application using deep learning (DL) and meta‐heuristic optimization has been presented. In order to have a computationally efficient and accurate optimization process, firstly a 3D EM unit element model of NTL has been designed. The training and test data sets are created based on different sampling methods. A DL regression model modified multilayer perceptron M2LP have been used for prediction of scattering parameters (S) of the NTL, with respect to the variation of geometrical design parameters. The proposed S‐parameters will then be used to calculate the equivalent S‐parameters of the cascading NTL to be used to calculate the NTL‐based microstrip band‐pass filter S‐parameter response. The optimal design parameters of each line used in the filter design have been determined using a fast and powerful optimization algorithm differential evolutionary algorithm.

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“…In particular, the successes of classification problems have been overcome even at human level . DL has a wide range application area such as segmentation, multi‐object tracking, biomedical applications, and even lip reading …”

Section: Modified Multi Layer Perceptronmentioning

confidence: 99%

Deep learning base modified MLP model for precise scattering parameter prediction of capacitive feed antenna

Çalık

Belen

Mahoutı

2019

Int J Numerical Modelling

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The relations between the antennas' geometrical parameters and design specifications usually consist of linear and nonlinear components. Especially with the increase of the requested performance measures, the design procedure becomes much more complex due to the conflicting performance criteria or design limitations. To achieve a design with high performance with feasible design parameters, a fast, accurate, and reliable design optimization process is required. Herein, to have a fast, accurate, and high‐performance capacitive‐feed antenna model to be used in design optimization problems, a modified multi‐layer perceptron (M2LP) model has been proposed. The M2LP is an equivalent convolutional neural network (CNN) model of a standard multilayer perceptron (MLP), where instead of traditional training parameters of MLP, more advanced training parameters of CNN models such as batch‐norm layer, leaky‐rectified linear unit (ReLU) layer, and Adam training algorithm had been used. Furthermore, the M2LP model had been used in a design optimization process and the obtained optimal antenna had been prototyped using 3D printing technology for justification of the proposed M2LP model with experimental results. As can be seen from the results, the proposed M2LP model is a fast, accurate, and reliable regression model for design optimization of microwave antennas.

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Transfer learning‐based online multiperson tracking with Gaussian process regression

Cited by 9 publications

References 40 publications

The design and optimization of heterogeneous catalysts using computational methods

The design and optimization of heterogeneous catalysts using computational methods

Artificial intelligence–based design optimization of nonuniform microstrip line band pass filter

Deep learning base modified MLP model for precise scattering parameter prediction of capacitive feed antenna

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