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One-Variable Attack on The Industrial Fault Classification System and Its Defense Article

Yue Zhuo, Yuri A.W. Shardt, Zhiqiang Ge

Engineering 2022, Volume 19, Issue 12,   Pages 240-251 doi: 10.1016/j.eng.2021.07.033

Abstract: the samples can cause the models to provide incorrect fault predictions.samples.samples.Moreover, to hide the adversarial samples in the visualization space, a Jacobian matrix is used to guidethe perturbed variable selection, making the adversarial samples in the dimensional reduction space

Keywords： Adversarial samples     Black-box attack     Industrial data security     Fault classification system    

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Adversarial Attacks and Defenses in Deep Learning Feature Article

Kui Ren, Tianhang Zheng, Zhan Qin, Xue Liu

Engineering 2020, Volume 6, Issue 3,   Pages 346-360 doi: 10.1016/j.eng.2019.12.012

Abstract: Recently, the security vulnerability of
DL algorithms to adversarial samples has been widely recognizedThe fabricated samples can lead to various
misbehaviors of the DL models while being perceivedSuccessful implementations
of adversarial attacks in real physical-world scenarios further demonstrate
Hence, adversarial attack and defense techniques have attracted increasing attention from bothIn this paper,
we first introduce the theoretical foundations, algorithms, and applications of adversarial

Keywords： Machine learning     Deep neural network Adversarial example     Adversarial attack     Adversarial defense    

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flavonol-based colorimetric and turn-on fluorescent probe for rapid determination of hydrazine in real water samples

Frontiers of Chemical Science and Engineering 2023, Volume 17, Issue 1,   Pages 24-33 doi: 10.1007/s11705-022-2171-1

Abstract: This probe was employed for determining trace hydrazine in different environmental water samples.

Keywords： cinnamaldehyde     3-hydroxychromone derivative     hydrazine     fluorescent probe    

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Analysis of physical properties of gas hydrate-bearing unconsolidated sediment samples from the ultra-deepwater

Frontiers in Energy 2022, Volume 16, Issue 3,   Pages 509-520 doi: 10.1007/s11708-021-0786-4

Abstract: study presents comprehensive measurement results and analysis of drilled hydrate-bearing sediments samplesThe results show that the gas hydrate in the core samples is mainly methane hydrate with a methane contentThe saturation of the samples fluctuates from 2%–60%, the porosity is approximately 38%–43%, and theIn addition, the median diameter of the sediment samples is mainly distributed in the range of 15 to

Keywords： natural gas hydrates     physical properties analysis     hydrate-bearing sediments    

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Determination of aniline derivatives in water samples after preconcentration with oxidized multiwalled

Hideyuki KATSUMATA, Yuta ODA, Satoshi KANECO, Tohru SUZUKI, Kiyohisa OHTA

Frontiers of Chemical Science and Engineering 2012, Volume 6, Issue 3,   Pages 270-275 doi: 10.1007/s11705-012-1298-x

Abstract: derivatives, such as 2-nitroaniline (2-NA), 4-nitroaniline (4-NA), and 2,4-dichloroaniline (2,4-DCA) in water samples

Keywords： aniline determination     solid-phase extraction     oxidized multiwalled carbon nanotubes     water sample     HPLC-UV    

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Generative adversarial network based novelty detection usingminimized reconstruction error Article

Huan-gang WANG, Xin LI, Tao ZHANG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1,   Pages 116-125 doi: 10.1631/FITEE.1700786

Abstract: Generative adversarial network (GAN) is the most exciting machine learning breakthrough in recent yearsGAN is composed of a generator and a discriminator, both trained with the adversarial learning mechanism

Keywords： Generative adversarial network (GAN)     Novelty detection     Tennessee Eastman (TE) process    

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Cantonese porcelain classification and image synthesis by ensemble learning and generative adversarial Special Feature on Intelligent Design

Steven Szu-Chi CHEN, Hui CUI, Ming-han DU, Tie-ming FU, Xiao-hong SUN, Yi JI, Henry DUH

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 12,   Pages 1632-1643 doi: 10.1631/FITEE.1900399

Abstract: The synthesis module is developed based on a conditional generative adversarial network, which enables

Keywords： Cantonese porcelain     Classification     Generative adversarial network     Creative arts    

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immune checkpoint Siglec10 in cancers: mining of comprehensive omics data and validation in patient samples

Frontiers of Medicine 2022, Volume 16, Issue 4,   Pages 596-609 doi: 10.1007/s11684-021-0868-z

Abstract: Sialic acid binding Ig-like lectin 10 (Siglec10) is a member of innate immune checkpoints that inhibits the activation of immune cells through the interaction with its ligand CD24 on tumor cells. Here, by analyzing public databases containing 64 517 patients of 33 cancer types, we found that the expression of Siglec10 was altered in 18 types of cancers and was associated with the clinical outcomes of 11 cancer types. In particular, Siglec10 was upregulated in patients with kidney renal clear cell carcinoma (KIRC) and was inversely associated with the prognosis of the patients. In 131 KIRC patients of our settings, Siglec10 was elevated in the tumor tissues of 83 (63.4%) patients compared with that in their counterpart normal kidney tissues. Moreover, higher level of Siglec10 was associated with advanced disease (stages III and IV) and worse prognosis. Silencing of CD24 in KIRC cells significantly increased the number of Siglec10-expressing macrophages phagocytosing KIRC cells. In addition, luciferase activity assays suggested that Siglec10 was a potential target of the transcription factors c-FOS and GATA1, which were identified by data mining. These results demonstrate that Siglec10 may have important oncogenic functions in KIRC, and represents a novel target for the development of immunotherapies.

Keywords： innate immune checkpoint     Siglec10     kidney renal clear cell carcinoma    

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Topology-independent end-to-end learning model for improving the voltage profile in microgrids-integrated power distribution networks

Frontiers in Energy 2023, Volume 17, Issue 2,   Pages 211-227 doi: 10.1007/s11708-022-0847-3

Abstract: proposed model consists of four modules, Pre-training Network and modified interior point methods with adversarialnetworks (Modified IPMAN) as core modules, and discriminator generative adversarial network (Dis-GAN

Keywords： end-to-end learning     microgrids     voltage profile improvement     generative adversarial network    

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Toward Trustworthy Decision-Making for Autonomous Vehicles: A Robust Reinforcement Learning Approach with Safety Guarantees

Xiangkun He,Wenhui Huang,Chen Lv,

Engineering doi: 10.1016/j.eng.2023.10.005

Abstract: adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarialIn addition, an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust

Keywords： Autonomous vehicle     Decision-making     Reinforcement learning     Adversarial attack     Safety guarantee    

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Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples

Xin ZHANG, Tao HUANG, Bo WU, Youmin HU, Shuai HUANG, Quan ZHOU, Xi ZHANG

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 2,   Pages 340-352 doi: 10.1007/s11465-021-0629-3

Abstract: fully extract fault information to recognize mechanical health states when processing high-dimensional samplesneural network (DCNN) is proposed in this study to accomplish fault recognition of high-dimensional samplestrained through dimension reduction learning to obtain different fault features from high-dimensional samples

Keywords： fault intelligent diagnosis     deep learning     deep convolutional neural network     high-dimensional samples    

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A Geometric Understanding of Deep Learning Article

Na Lei, Dongsheng An, Yang Guo, Kehua Su, Shixia Liu, Zhongxuan Luo, Shing-Tung Yau, Xianfeng Gu

Engineering 2020, Volume 6, Issue 3,   Pages 361-374 doi: 10.1016/j.eng.2019.09.010

Abstract: Times New Roman",serif">This work introduces an optimal transportation (OT) view of generative adversarial

Keywords： Generative     Adversarial     Deep learning     Optimal transportation     Mode collapse    

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Standard method design considerations for semi-quantification of total naphthenic acids in oil sands process affected water by mass spectrometry: A review

Kevin A. Kovalchik, Matthew S. MacLennan, Kerry M. Peru, John V. Headley, David D.Y. Chen

Frontiers of Chemical Science and Engineering 2017, Volume 11, Issue 3,   Pages 497-507 doi: 10.1007/s11705-017-1652-0

Abstract: analytical methods have been developed for the semi-quantification of total naphthenic acids in water samples

Keywords： total naphthenic acids     environmental samples     oil sands process affected water     polar organics     mass spectrometry    

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SmartPaint: a co-creative drawing system based on generative adversarial networks Special Feature on Intelligent Design

Lingyun SUN, Pei CHEN, Wei XIANG, Peng CHEN, Wei-yue GAO, Ke-jun ZHANG

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 12,   Pages 1644-1656 doi: 10.1631/FITEE.1900386

Abstract: To bridge this gap, we have developed SmartPaint, a co-creative drawing system based on generative adversarial

Keywords： Co-creative drawing     Deep learning     Image generation    

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Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology Article

Yufei Liu, Yuan Zhou, Xin Liu, Fang Dong, Chang Wang, Zihong Wang

Engineering 2019, Volume 5, Issue 1,   Pages 156-163 doi: 10.1016/j.eng.2018.11.018

Abstract: Effective utilization of deep learning relies considerably on the number of labeled samples, which restrictsIn this paper, we propose an approach based on a generative adversarial network (GAN) combined with aFirst, the original samples were divided into a training set and a test set.Next, the DNN classifier was trained with the synthetic samples.attempt to transform the classical statistical machine-learning classification method based on original samples

Keywords： Artificial intelligence     Generative adversarial network     Deep neural network     Small sample size     Cancer    

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Title Author Date Type Operation

One-Variable Attack on The Industrial Fault Classification System and Its Defense

Yue Zhuo, Yuri A.W. Shardt, Zhiqiang Ge

Journal Article

Adversarial Attacks and Defenses in Deep Learning

Kui Ren, Tianhang Zheng, Zhan Qin, Xue Liu

Journal Article

flavonol-based colorimetric and turn-on fluorescent probe for rapid determination of hydrazine in real water samples

Journal Article

Analysis of physical properties of gas hydrate-bearing unconsolidated sediment samples from the ultra-deepwater

Journal Article

Determination of aniline derivatives in water samples after preconcentration with oxidized multiwalled

Hideyuki KATSUMATA, Yuta ODA, Satoshi KANECO, Tohru SUZUKI, Kiyohisa OHTA

Journal Article

Generative adversarial network based novelty detection usingminimized reconstruction error

Huan-gang WANG, Xin LI, Tao ZHANG

Journal Article

Cantonese porcelain classification and image synthesis by ensemble learning and generative adversarial

Steven Szu-Chi CHEN, Hui CUI, Ming-han DU, Tie-ming FU, Xiao-hong SUN, Yi JI, Henry DUH

Journal Article

immune checkpoint Siglec10 in cancers: mining of comprehensive omics data and validation in patient samples

Journal Article

Topology-independent end-to-end learning model for improving the voltage profile in microgrids-integrated power distribution networks

Journal Article

Toward Trustworthy Decision-Making for Autonomous Vehicles: A Robust Reinforcement Learning Approach with Safety Guarantees

Xiangkun He,Wenhui Huang,Chen Lv,

Journal Article

Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples

Xin ZHANG, Tao HUANG, Bo WU, Youmin HU, Shuai HUANG, Quan ZHOU, Xi ZHANG

Journal Article

A Geometric Understanding of Deep Learning

Na Lei, Dongsheng An, Yang Guo, Kehua Su, Shixia Liu, Zhongxuan Luo, Shing-Tung Yau, Xianfeng Gu

Journal Article

Standard method design considerations for semi-quantification of total naphthenic acids in oil sands process affected water by mass spectrometry: A review

Kevin A. Kovalchik, Matthew S. MacLennan, Kerry M. Peru, John V. Headley, David D.Y. Chen

Journal Article

SmartPaint: a co-creative drawing system based on generative adversarial networks

Lingyun SUN, Pei CHEN, Wei XIANG, Peng CHEN, Wei-yue GAO, Ke-jun ZHANG

Journal Article

Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology

Yufei Liu, Yuan Zhou, Xin Liu, Fang Dong, Chang Wang, Zihong Wang

Journal Article