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736 results on '"Zhao, Xingang"'

156. A Time-Independent Control System for Natural Human Gait Assistance With a Soft Exoskeleton

Author

Tan, Xiaowei, Zhang, Bi, Liu, Guangjun, Zhao, Xingang, and Zhao, Yiwen

Abstract

When applying exoskeletons for walking assistance, one important consideration is to ensure that the users retain full control over the exoskeleton-provided assistance, which is quite limited in existing exoskeletons due to the absence of a suitable control system. In this article, a time-independent exoskeleton control system is developed based on a novel assistance profile generation method and an iterative force control method to enable continuous assistance adjustment. The assistance profile is formulated as a Gaussian function with a human state variable and can be updated online to adapt to different users. The proposed profile continuously self-adjusts along the movement of the user's leg, especially when users change their walking patterns. The proposed control system iteratively compensates for the force control lag and amplitude attenuation to enable precise tracking of the assistance profile during natural human walking. Experiments have been conducted using a soft exoskeleton on subjects with and without prior experience using an exoskeleton. The experimental results have shown the effectiveness of the proposed control system compared with a common time-dependent control system.

Published
2023
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159. An Automatic Diagnosis Scheme for Gliomas Using Gray Level Co- occurrence Matrix, Histogram of Oriented Gradient,Discrete Wavelet Transformation, Intensity-based Features andHierarchical PSO-SVM

Author

Zhao Yiwen, Zheng Huang, Zhao Xingang, Song Guoli, Chengjian Lou, and Hen Cai

Subjects
Scheme (programming language), business.industry, Computer science, Pattern recognition, Gray level, Support vector machine, Co-occurrence matrix, Transformation (function), Wavelet, Histogram, Artificial intelligence, business, computer, Intensity (heat transfer), computer.programming_language
Published
2019

160. Improved departure from nucleate boiling prediction in rod bundles using a physics-informed machine learning-aided framework

Author

Shirvan, Koroush, Zhao, Xingang, and Salko, Robert

Subjects
bepress|Physical Sciences and Mathematics|Physics|Engineering Physics, bepress|Engineering|Nuclear Engineering, engrXiv|Engineering|Engineering Physics, engrXiv|Engineering, bepress|Engineering, bepress|Engineering|Mechanical Engineering, engrXiv|Engineering|Mechanical Engineering, engrXiv|Engineering|Nuclear Engineering, engrXiv|Engineering|Mechanical Engineering|Energy Systems, bepress|Engineering|Mechanical Engineering|Energy Systems
Abstract

The critical heat flux (CHF) corresponding to the departure from nucleate boiling (DNB) crisis is a regulatory limit for the licensing of pressurized water reactors (PWRs) worldwide. Despite the abundance of predictive tools available to the reactor thermal-hydraulics community, the path for an accurate CHF model remains elusive. This work approaches the prediction of DNB through a physics-informed machine learning-aided framework (PIMLAF) with the objective of achieving superior predictive capabilities for a rod bundle. In view of the limitations in existing macro-scale physics-driven tools, an improved mechanistic model is first proposed, leveraging key concepts in the liquid sublayer dryout and bubble crowding mechanisms. The proposed mechanistic model is able to predict DNB in different heater geometries for a broad range of flow conditions without the need for recalibration. This model is then incorporated as the physics-informed component of the hybrid framework PIMLAF, which takes advantage of established understanding in the field (i.e., domain knowledge [DK]) and uses machine learning (ML) to capture undiscovered information from the mismatch between the actual and DK-predicted output. Two bundle-related case studies using the PWR subchannel and bundle tests (PSBT) database are carried out to illustrate the PIMLAF’s improved performance over traditional approaches for both interpolation and extrapolation purposes. In light of the PIMLAF’s promising potential to reduce prediction error, reactor vendors are encouraged to leverage their in-house experimental efforts and apply the hybrid framework to potentially achieve margin reductions in the minimum DNB ratio (MDNBR) for the designs of interest.

Published
2021

161. A weighted multi-output neural network model for the prediction of rigid pavement deterioration.

Author

Guo, Fengdi, Zhao, Xingang, Gregory, Jeremy, and Kirchain, Randolph

Subjects
*ARTIFICIAL neural networks, *PAVEMENT management, *PAVEMENTS, *PREDICTION models
Abstract

A novel weighted multi-output neural network (NN) model is proposed for predicting the deterioration of rigid pavements based on Iowa pavement management system data. This first-of-a-kind model simultaneously predicts four pavement condition metrics concerning rigid pavements, including IRI, faulting, longitudinal crack and transverse crack. It provides an opportunity to efficiently evaluate pavement conditions and to make treatment decisions based on multi-condition metrics, such as the pavement condition index (PCI) for budget allocation models. Compared to traditional single-output NN models, this multi-output model is capable of incorporating correlations among different condition metrics. During model training, each condition metric is assigned a weight to reflect its relative importance. When the weights equal to those in the formula for the multi-condition metric, the prediction performance for PCI is optimal (13% lower MSE than optimal, single-output models). The multi-output model improves on the prediction performance for three of the four individual condition metrics compared to optimal single-output models. Results show that the consideration of correlations could improve the prediction performance for single and multi-condition metrics. Finally, variable weighting is critical for achieving the optimal balance of prediction performance among the various metrics as dictated by the needs of the decisionmaker. [ABSTRACT FROM AUTHOR]

Published
2022
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166. Electron-Catalyzed Dehydrogenation in a Single-Molecule Junction

Author

Chen, Hongliang, primary, Jiang, Feng, additional, Hu, Chen, additional, Jiao, Yang, additional, Chen, Su, additional, Qiu, Yunyan, additional, Zhou, Ping, additional, Zhang, Long, additional, Cai, Kang, additional, Song, Bo, additional, Chen, Xiao-Yang, additional, Zhao, Xingang, additional, Wasielewski, Michael R., additional, Guo, Hong, additional, Hong, Wenjing, additional, and Stoddart, J. Fraser, additional

Published
2021
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177. The 0-1 programming solution for the problem of resource allocation

Author

Luo, Guoliang and Zhao, Xingang

Subjects
Environmental issues
Abstract

In this article, we first translated the problem of disperse resource allocation into the 0-1 programming problem, and put forward the special solution based on the relative difference quotient aiming at the particularity of resource allocation 0-1 programming model. And the example proved that the new algorithm was effective and fast. Keywords: Resource allocation, 0-1 programming, Relative difference quotient, Feasible increasing direction, 1. Introduction of the problem In actual problems, we always need to allocate a sort of or many sorts of resources with certain quantity to many departments or units, and [...]

Published
2009

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