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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 adversarialnetwork 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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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: is needed to ensure voltage security against ever-changing operating conditions, especially when the network(SCAN), which depends on operational data instead of network topology details in the context of powerMore specifically, the proposed model consists of four modules, Pre-training Network and modified interiorpoint methods with adversarial networks (Modified IPMAN) as core modules, and discriminator generativeadversarial network (Dis-GAN) and Volt convolutional neural network (Volt-CNN) as ancillary modules.

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

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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 recognizedSuccessful 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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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: In this paper, we propose an approach based on a generative adversarial network (GAN) combined with adeep neural network (DNN).

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

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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: font-family:"Times New Roman",serif">This work introduces an optimal transportation (OT) view of generativeadversarial networks (GANs).We also propose a novel generative model, which uses an autoencoder (AE) for manifold learning and OT

Keywords： Generative     Adversarial     Deep learning     Optimal transportation     Mode collapse    

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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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Surprising Advances in Generative Artificial Intelligence Prompt Amazement—and Worries

Dana Mackenzie

Engineering 2023, Volume 25, Issue 6,   Pages 9-11 doi: 10.1016/j.eng.2023.04.004

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Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models Review

Changde Du, Jinpeng Li, Lijie Huang, Huiguang He

Engineering 2019, Volume 5, Issue 5,   Pages 948-953 doi: 10.1016/j.eng.2019.03.010

Abstract: networks and human visual streams in terms of the architecture and computational rules Furthermore, deep generativemodels (e.g., variational autoencoders (VAEs) and generative adversarial networks (GANs)) have produced

Keywords： Brain encoding and decoding     Functional magnetic resonance imaging     Deep neural networks     Deep generative    

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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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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: However, these data-driven models are vulnerable to adversarial attacks; thus, small perturbations onrecent studies have demonstrated the vulnerability of machine learning methods and the existence of adversarialMoreover, 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 spaceBased on the attack method, a corresponding adversarial training defense method is also proposed, which

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

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Novel interpretable mechanism of neural networks based on network decoupling method

Frontiers of Engineering Management 2021, Volume 8, Issue 4,   Pages 572-581 doi: 10.1007/s42524-021-0169-x

Abstract: The lack of interpretability of the neural network algorithm has become the bottleneck of its wide applicationdimension reduction method of high-dimensional system and reveal the calculation mechanism of the neural networkWe apply our framework to some network models and a real system of the whole neuron map of CaenorhabditisResult shows that a simple linear mapping relationship exists between network structure and network behaviorin the neural network with high-dimensional and nonlinear characteristics.

Keywords： neural networks     interpretability     dynamical behavior     network decouple    

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A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

Frontiers of Mechanical Engineering 2023, Volume 18, Issue 2, doi: 10.1007/s11465-022-0736-9

Abstract: Recently, advanced sensing techniques ensure a large number of multivariate sensing data for intelligent fault diagnosis of machines. Given the advantage of obtaining accurate diagnosis results, multi-sensor fusion has long been studied in the fault diagnosis field. However, existing studies suffer from two weaknesses. First, the relations of multiple sensors are either neglected or calculated only to improve the diagnostic accuracy of fault types. Second, the localization for multi-source faults is seldom investigated, although locating the anomaly variable over multivariate sensing data for certain types of faults is desirable. This article attempts to overcome the above weaknesses by proposing a global method to recognize fault types and localize fault sources with the help of multi-sensor relations (MSRs). First, an MSR model is developed to learn MSRs automatically and further obtain fault recognition results. Second, centrality measures are employed to analyze the MSR graphs learned by the MSR model, and fault sources are therefore determined. The proposed method is demonstrated by experiments on an induction motor and a centrifugal pump. Results show the proposed method’s validity in diagnosing fault types and sources.

Keywords： fault recognition     fault localization     multi-sensor relations     network analysis     graph neural network    

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Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6,   Pages 1285-1298 doi: 10.1007/s11709-020-0691-7

Abstract: This article intends to model the multiscale constitution using feedforward neural network (FNN) andrecurrent neural network (RNN), and appropriate set of loading paths are selected to effectively predict

Keywords： multiscale method     constitutive model     feedforward neural network     recurrent neural network    

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Heat, mass, and work exchange networks

Zhiyou CHEN, Jingtao WANG

Frontiers of Chemical Science and Engineering 2012, Volume 6, Issue 4,   Pages 484-502 doi: 10.1007/s11705-012-1221-5

Abstract: This review presents the main works related to each network.

Keywords： process system engineering     integration methods     heat exchange network     mass exchange network     work exchangenetwork    

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Title Author Date Type Operation

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 adversarialnetwork

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

Journal Article

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

Journal Article

Adversarial Attacks and Defenses in Deep Learning

Kui Ren, Tianhang Zheng, Zhan Qin, Xue Liu

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

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

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

Surprising Advances in Generative Artificial Intelligence Prompt Amazement—and Worries

Dana Mackenzie

Journal Article

Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models

Changde Du, Jinpeng Li, Lijie Huang, Huiguang He

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

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

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

Journal Article

Novel interpretable mechanism of neural networks based on network decoupling method

Journal Article

A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

Journal Article

Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

Journal Article

Heat, mass, and work exchange networks

Zhiyou CHEN, Jingtao WANG

Journal Article