9 research outputs found
SMAP: A Novel Heterogeneous Information Framework for Scenario-based Optimal Model Assignment
The increasing maturity of big data applications has led to a proliferation
of models targeting the same objectives within the same scenarios and datasets.
However, selecting the most suitable model that considers model's features
while taking specific requirements and constraints into account still poses a
significant challenge. Existing methods have focused on worker-task assignments
based on crowdsourcing, they neglect the scenario-dataset-model assignment
problem. To address this challenge, a new problem named the Scenario-based
Optimal Model Assignment (SOMA) problem is introduced and a novel framework
entitled Scenario and Model Associative percepts (SMAP) is developed. SMAP is a
heterogeneous information framework that can integrate various types of
information to intelligently select a suitable dataset and allocate the optimal
model for a specific scenario. To comprehensively evaluate models, a new score
function that utilizes multi-head attention mechanisms is proposed. Moreover, a
novel memory mechanism named the mnemonic center is developed to store the
matched heterogeneous information and prevent duplicate matching. Six popular
traffic scenarios are selected as study cases and extensive experiments are
conducted on a dataset to verify the effectiveness and efficiency of SMAP and
the score function
Nonlinear Dynamic Analysis and Optimization of Closed-Form Planetary Gear System
A nonlinear purely rotational dynamic model of a multistage closed-form planetary gear set formed by two simple planetary stages is proposed in this study. The model includes time-varying mesh stiffness, excitation fluctuation and gear backlash nonlinearities. The nonlinear differential equations of motion are solved numerically using variable step-size Runge-Kutta. In order to obtain function expression of optimization objective, the nonlinear differential equations of motion are solved analytically using harmonic balance method (HBM). Based on the analytical solution of dynamic equations, the optimization mathematical model which aims at minimizing the vibration displacement of the low-speed carrier and the total mass of the gear transmission system is established. The optimization toolbox in MATLAB program is adopted to obtain the optimal solution. A case is studied to demonstrate the effectiveness of the dynamic model and the optimization method. The results show that the dynamic properties of the closed-form planetary gear transmission system have been improved and the total mass of the gear set has been decreased significantly
Applications and Advances in Machine Learning Force Fields
Force fields (FFs) form the basis of molecular simulations
and
have significant implications in diverse fields such as materials
science, chemistry, physics, and biology. A suitable FF is required
to accurately describe system properties. However, an off-the-shelf
FF may not be suitable for certain specialized systems, and researchers
often need to tailor the FF that fits specific requirements. Before
applying machine learning (ML) techniques to construct FFs, the mainstream
FFs were primarily based on first-principles force fields (FPFF) and
empirical FFs. However, the drawbacks of FPFF and empirical FFs are
high cost and low accuracy, respectively, so there is a growing interest
in using ML as an effective and precise tool for reconciling this
trade-off in developing FFs. In this review, we introduce the fundamental
principles of ML and FFs in the context of machine learning force
fields (MLFF). We also discuss the advantages and applications of
MLFF compared to traditional FFs, as well as the MLFF toolkits widely
employed in numerous applications