Search

Your search keyword '"Meng, Yanmei"' showing total 169 results
Start Over Author "Meng, Yanmei"
169 results on '"Meng, Yanmei"'

3. 3D Adversarial Attacks Beyond Point Cloud

Author

Zhang, Jinlai, Chen, Lyujie, Liu, Binbin, Ouyang, Bo, Xie, Qizhi, Zhu, Jihong, Li, Weiming, and Meng, Yanmei

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recently, 3D deep learning models have been shown to be susceptible to adversarial attacks like their 2D counterparts. Most of the state-of-the-art (SOTA) 3D adversarial attacks perform perturbation to 3D point clouds. To reproduce these attacks in the physical scenario, a generated adversarial 3D point cloud need to be reconstructed to mesh, which leads to a significant drop in its adversarial effect. In this paper, we propose a strong 3D adversarial attack named Mesh Attack to address this problem by directly performing perturbation on mesh of a 3D object. In order to take advantage of the most effective gradient-based attack, a differentiable sample module that back-propagate the gradient of point cloud to mesh is introduced. To further ensure the adversarial mesh examples without outlier and 3D printable, three mesh losses are adopted. Extensive experiments demonstrate that the proposed scheme outperforms SOTA 3D attacks by a significant margin. We also achieved SOTA performance under various defenses. Our code is available at: https://github.com/cuge1995/Mesh-Attack., Comment: 8 pages, 6 figs

Published
2021

4. The art of defense: letting networks fool the attacker

Author

Zhang, Jinlai, Dong, Yinpeng, Liu, Binbin, Ouyang, Bo, Zhu, Jihong, Kuang, Minchi, Wang, Houqing, and Meng, Yanmei

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Robust environment perception is critical for autonomous cars, and adversarial defenses are the most effective and widely studied ways to improve the robustness of environment perception. However, all of previous defense methods decrease the natural accuracy, and the nature of the DNNs itself has been overlooked. To this end, in this paper, we propose a novel adversarial defense for 3D point cloud classifier that makes full use of the nature of the DNNs. Due to the disorder of point cloud, all point cloud classifiers have the property of permutation invariant to the input point cloud. Based on this nature, we design invariant transformations defense (IT-Defense). We show that, even after accounting for obfuscated gradients, our IT-Defense is a resilient defense against state-of-the-art (SOTA) 3D attacks. Moreover, IT-Defense do not hurt clean accuracy compared to previous SOTA 3D defenses. Our code is available at: {\footnotesize{\url{https://github.com/cuge1995/IT-Defense}}}.

Published
2021

5. PointCutMix: Regularization Strategy for Point Cloud Classification

Author

Zhang, Jinlai, Chen, Lyujie, Ouyang, Bo, Liu, Binbin, Zhu, Jihong, Chen, Yujing, Meng, Yanmei, and Wu, Danfeng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

As 3D point cloud analysis has received increasing attention, the insufficient scale of point cloud datasets and the weak generalization ability of networks become prominent. In this paper, we propose a simple and effective augmentation method for the point cloud data, named PointCutMix, to alleviate those problems. It finds the optimal assignment between two point clouds and generates new training data by replacing the points in one sample with their optimal assigned pairs. Two replacement strategies are proposed to adapt to the accuracy or robustness requirement for different tasks, one of which is to randomly select all replacing points while the other one is to select k nearest neighbors of a single random point. Both strategies consistently and significantly improve the performance of various models on point cloud classification problems. By introducing the saliency maps to guide the selection of replacing points, the performance further improves. Moreover, PointCutMix is validated to enhance the model robustness against the point attack. It is worth noting that when using as a defense method, our method outperforms the state-of-the-art defense algorithms. The code is available at:https://github.com/cuge1995/PointCutMix, Comment: 8 pages,5 figures

Published
2021

25. Performance prediction and parameter optimization of alumina-titanium carbide ceramic micro-EDM hole machining process based on XGBoost.

Author

Chen, Yujin, Wu, Yaoguang, Cheng, Mengmeng, Zhu, Jihong, Meng, Yanmei, and Mu, Xiele

Abstract

Aiming at the difficulties in setting process parameters and the low accuracy of process performance prediction in electrical discharge machining (EDM) hole machining of alumina ceramics, a novel EDM process performance prediction method based on eXtreme Gradient Boosting (XGBoost) is proposed in this study. The independent variables selected include processing polarity (P), interelectrode voltage (V), peak current (I), pulse frequency (F), pulse width (T), and tool electrode and workpiece electrode gap (W), while the dependent variables are tool electrode length loss (ELW), tool electrode volume loss (EVW), and material removal rate (MRR). An L18 (21 × 35) orthogonal test is designed to obtain training samples for model development. The accuracy of the prediction results for ELW, EVW, and MRR by the XGBoost model is found to be 1.7%, 2.5%, and 9.4%, respectively. These results show a significant improvement compared to the linear regression model, with an improvement of 41.7%, 62.5%, and 19.4%, respectively. Furthermore, when compared to the support vector regression (SVR) model, the XGBoost model also shows improvement of 23.6%, 5.9%, and 12.0% for ELW, EVW, and MRR prediction accuracy, respectively. These findings suggest that the proposed XGBoost-based method is effective and accurate in predicting the performance of alumina ceramic EDM processes. In addition, the third generation nondominated sorting genetic algorithm (NSGA-III) is utilized to optimize the process parameters with both single objective and multi-objective approaches. The optimal parameters for ELW, EVW, and MRR, including the selection of machining polarity, interelectrode voltage, peak current, pulse frequency, pulse width, and tool electrode and workpiece electrode gap are obtained through the Pareto frontier of the three comprehensive optima. The average relative errors between the experimental results and the optimized results are found to be 6.46%, 10.45%, and 9.58% for ELW, EVW, and MRR, respectively, indicating the accuracy and effectiveness of the optimized results. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

26. Experimental study on micro electrical discharge machining of alumina–titanium carbide ceramic materials.

Author

Chen, Yujin, Wu, Yaoguang, Cheng, Mengmeng, Lai, Debin, Zhu, Jihong, and Meng, Yanmei

Abstract

When electric discharge machining (EDM) is used to process Al2O3-TiC ceramics, it is difficult to obtain an optimal production efficiency and tool wear rate. As the target demand of processing largely relies on the process parameters, it is pertinent to conduct an in-depth study on the process parameters. In this study, the effects of certain technological parameters on the properties of EDM Al2O3-TiC ceramic materials were assessed and a mixed 2–3 horizontal orthogonal test method was designed. The rules of machining polarity, inter-electrode voltage, peak current, gain, pulse frequency, pulse width, inter-electrode gap and electrode length loss, electrode profile loss, material removal rate were studied. The effect of process parameters on process performance was determined by range analysis. In addition, the analysis of variance revealed that machining polarity has a significant effect on the electrode loss while the electrode voltage and peak current significantly influenced the material removal rate. The optimal parameter combination was determined as negative machining polarity, 130 V inter-electrode voltage, 50 A peak current, 45 gain, 150 Hz pulse frequency, 1.0 μs pulse width, and 40 μs inter-electrode gap. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

36. Capacity estimation of Li-ion battery based on transformer-adversarial discriminative domain adaptation.

Author

Liu, Xin, Yang, Changbo, Meng, Yanmei, Zhu, Jihong, and Duan, Yijian

Subjects
ELECTRIC vehicle batteries, LITHIUM-ion batteries, ELECTRONIC equipment, TIME series analysis, ELECTRIC vehicles
Abstract

Lithium-ion batteries are widely used in various electronic devices as well as electric vehicles, and accurate estimation of the battery capacity is important to ensure safe and reliable operation of the system. However, in practice, the complex working conditions and the limitation of the number of charge/discharge cycles lead to insufficient historical data and inaccurate capacity estimation. In order to improve the adaptability as well as accuracy under different operating conditions, this paper proposes a lithium-ion battery capacity estimation model based on Transformer-Adversarial Discriminative Domain Adaptation (T-ADDA). The model takes charging voltage, charging current, and charging temperature as inputs and uses a transformer network to extract the time series features from the data. Then, adversarial domain adaptation is trained on the source and target domain data by the domain discrimination network of the ADDA model so as to find the domain invariant features between the source and target domains. Finally, the regression network of ADDA is used to achieve cross-domain prediction for the target domain data. The experimental results show that the T-ADDA model can accurately achieve cross-domain prediction and that the average error of prediction under different operating conditions is only 3.9225%. Therefore, the T-ADDA model has good adaptability and accuracy, and it can significantly improve the performance of lithium-ion battery capacity estimation. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

43. The Quality Monitoring Technology in the Process of the Pulping Papermaking Alkaline Steam Boiling Based on Neural Network

Author

Su, Jianjun, Meng, Yanmei, Chen, Chaolin, Lu, Funing, Yan, Sijie, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sun, Fuchun, editor, Zhang, Jianwei, editor, Tan, Ying, editor, Cao, Jinde, editor, and Yu, Wen, editor

Published
2008
Full Text
View/download PDF

44. Data Fusion Based on Neural Networks and Particle Swarm Algorithm and Its Application in Sugar Boiling

Author

Meng, Yanmei, Yan, Sijie, Tang, Zhihong, Chen, Yuanling, Liu, Jingneng, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sun, Fuchun, editor, Zhang, Jianwei, editor, Tan, Ying, editor, Cao, Jinde, editor, and Yu, Wen, editor

Published
2008
Full Text
View/download PDF

50. Data‐driven modeling based on kernel extreme learning machine for sugarcane juice clarification

Author

Meng Yanmei, Yanpeng Xie, Shuangshuang Yu, Johnny Qin, and Hui Wang

Subjects
Computer science, color value, lcsh:TX341-641, 02 engineering and technology, 01 natural sciences, Data-driven, extreme learning machine, 0202 electrical engineering, electronic engineering, information engineering, Process engineering, Reliability (statistics), Original Research, Extreme learning machine, gravity purity, particle swarm optimization, Basis (linear algebra), Artificial neural network, business.industry, 010401 analytical chemistry, sugarcane juice clarification, Particle swarm optimization, 0104 chemical sciences, Support vector machine, Gravity (alcoholic beverage), 020201 artificial intelligence & image processing, business, lcsh:Nutrition. Foods and food supply, Food Science
Abstract

Clarification of sugarcane juice is an important operation in the production process of sugar industry. The gravity purity and the color value of juice are the two most important evaluation indexes in the cane sugar production using the sulphitation clarification method. However, in the actual operation, the measurement of these two indexes is usually obtained by offline experimental titration, which makes it impossible to timely adjust the system indicators. A data‐driven modeling based on kernel extreme learning machine is proposed to predict the gravity purity of juice and the color value of clear juice. The model parameters are optimized by particle swarm optimization. Experiments are conducted to verify the effectiveness and superiority of the modeling method. Compared with BP neural network, radial basis neural network, and support vector machine, the model has a good performance, which proves the reliability of the model.

Published
2019

Catalog

Books, media, physical & digital resources
See catalog results