Conducting Eco-Hydraulic Simulation Experiments Using Embodied Intelligent Fish by 涛 LI (20525192)

“…The core of this framwork is a simulation platform for intelligent fish based on deep reinforcement learning (DRL) and the immersed boundary-lattice Boltzmann (IB-LB) coupling algorithm. …”

A novel approach for solving a class of diffusion identification problems by Ştefana-Lucia Aniţa (21584227)

“…The proposed approach considers twice continuously differentiable diffusion coefficients defined as the convolution with a square-integrable optimization function, which allows to prove the PMP by spike variation and to construct and analyze an efficient PMP-based iterative algorithm that efficiently solves diffusion identification problems approximated by finite elements.…”

Data Sheet 1_A novel method for power transformer fault diagnosis considering imbalanced data samples.docx by Jun Chen (4238)

“…Hyperparameter tuning is achieved through the Bayesian optimization algorithm to identify the model parameter set that maximizes test set accuracy.…”

Logical Fault Detection Approach for Mixed Control Flipping Faults in Reversible Circuits by Mousum Handique (21385838)

“…Experimental results are evaluated based on the MixCFF detection and the MixCFF fault coverage range with the help of different benchmark circuits using the suggested ATPG algorithm.…”

Harmonic artificial potential field path planning for tracked robots in unstructured environments by Angelo Ugenti (22328960)

“…For example, when compared to an existing planner from the literature, the proposed HAPF-based planner generates a path that optimizes the expected cost based on distance to obstacles, path length, average slope, and sinkage, with an 85% reduction in overall path cost.…”

Supplementary materials S1-S3 by Yankai Wu (22577300)

“…This file contains Supplementary Materials S1-S3: detailed parameter settings for the base learners and the feature selection procedures (S1); the specific constraints and configurations of the NSGA-II algorithm (S2); and the predefined hyperparameter search ranges used for Bayesian optimization (S3).…”

Framework of MAPPO. by Jianbin Zheng (587000)

“…In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. …”

The average completion time of each method. by Jianbin Zheng (587000)

“…In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. …”

The connection of physical space. by Jianbin Zheng (587000)

“…In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. …”

End-to-end data transmission delay. by Jianbin Zheng (587000)

“…In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. …”

Production workflow of stiffened H-beams. by Jianbin Zheng (587000)

“…In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. …”

Collision risk warning. by Jianbin Zheng (587000)

“…In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. …”

Framework of rMAPPO. by Jianbin Zheng (587000)

“…In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. …”

Data_and_model_files. by Jianbin Zheng (587000)

“…In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. …”

Data Sheet 1_Statistics and behavior of clinically significant extra-pulmonary vein atrial fibrillation sources: machine-learning-enhanced electrographic flow mapping in persistent... by Peter Ruppersberg (22123543)

“…These findings, supported by the FLOW-AF trial, underscore the usefulness of clinical outcome-based machine learning to improve the efficacy of algorithm based medical diagnostics.…”

Presentation_1_Gradient-free training of recurrent neural networks using random perturbations.pdf by Jesús García Fernández (19065017)

“…An alternative strategy to using gradient-based methods like BPTT involves stochastically approximating gradients through perturbation-based methods. …”

Tree-Enhanced Latent Space Models for Two-Mode Networks by Dan Pu (3714616)

“…We have developed an efficient Alternating Direction Method of Multipliers (ADMM) algorithm to solve the resulting optimization problem. …”

Table 1_Incorporating genotype information in a precise prediction model for platinum sensitivity in epithelial ovarian cancer.docx by Nai-Yi Du (20516537)

“…Sixteen SNPs were preserved after the optimization. A predicting model for drug sensitivity was constructed based on those sixteen SNPs. …”

DosePI: A Comprehensive Dataset for Peristaltic Pump Accuracy Enhancement in Pharmaceutical Environments by Davide Privitera (20720334)

“…The dataset supports research in dosing system optimization and compensation algorithm development.…”

Conventional Raman spectra of OPP solution. by Fuchao Yan (21022006)

“…The study utilizes the Hilbert-Schmidt Independence Criterion-based Variable Space Iterative Optimization algorithm (HSIC-VSIO) for feature variable selection, and combines it with Partial Least Squares Regression (PLSR) to build a spectral fusion quantitative model. …”