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Deep 3D reconstruction: methods, data, and challenges Review Article
Caixia Liu, Dehui Kong, Shaofan Wang, Zhiyong Wang, Jinghua Li, Baocai Yin,lcxxib@emails.bjut.edu.cn,wangshaofan@bjut.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5, Pages 615-766 doi: 10.1631/FITEE.2000068
Keywords: 深度学习模型;三维重建;循环神经网络;深度自编码器;生成对抗网络;卷积神经网络
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
With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques, it is critical
to ensure the security and robustness of the deployed algorithms. Recently, the security vulnerability of
DL algorithms to adversarial samples has been widely recognized. The fabricated samples can lead to various
misbehaviors of the DL models while being perceived as benign by humans. Successful implementations
of adversarial attacks in real physical-world scenarios further demonstrate their practicality.
Hence, adversarial attack and defense techniques have attracted increasing attention from both machine
learning and security communities and have become a hot research topic in recent years. In this paper,
we first introduce the theoretical foundations, algorithms, and applications of adversarial attack techniques.
We then describe a few research efforts on the defense techniques, which cover the broad frontier
in the field. Several open problems and challenges are subsequently discussed, which we hope will provoke
further research efforts in this critical area.
Keywords: Machine learning Deep neural network Adversarial example Adversarial attack Adversarial defense
Diffractive Deep Neural Networks at Visible Wavelengths Article
Hang Chen, Jianan Feng, Minwei Jiang, Yiqun Wang, Jie Lin, Jiubin Tan, Peng Jin
Engineering 2021, Volume 7, Issue 10, Pages 1485-1493 doi: 10.1016/j.eng.2020.07.032
Optical deep learning based on diffractive optical elements offers unique advantages for parallel processing, computational speed, and power efficiency. One landmark method is the diffractive deep neural network (D2NN) based on three-dimensional printing technology operated in the terahertz spectral range. Since the terahertz bandwidth involves limited interparticle coupling and material losses, this paper
extends D2NN to visible wavelengths. A general theory including a revised formula is proposed to solve any contradictions between wavelength, neuron size, and fabrication limitations. A novel visible light D2NN classifier is used to recognize unchanged targets (handwritten digits ranging from 0 to 9) and targets that have been changed (i.e., targets that have been covered or altered) at a visible wavelength of 632.8 nm. The obtained experimental classification accuracy (84%) and numerical classification accuracy (91.57%) quantify the match between the theoretical design and fabricated system performance. The presented framework can be used to apply a D2NN to various practical applications and design other new applications.
Keywords: Optical computation Optical neural networks Deep learning Optical machine learning Diffractive deep neural networks
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
Brain encoding and decoding via functional magnetic resonance imaging (fMRI) are two important aspects of visual perception neuroscience. Although previous researchers have made significant advances in brain encoding and decoding models, existing methods still require improvement using advanced machine learning techniques. For example, traditional methods usually build the encoding and decoding models separately, and are prone to overfitting on a small dataset. In fact, effectively unifying the encoding and decoding procedures may allow for more accurate predictions. In this paper, we first review the existing encoding and decoding methods and discuss
the potential advantages of a "bidirectional" modeling strategy. Next, we show that there are correspondences between deep neural networks and human visual streams in terms of the architecture and computational rules Furthermore, deep generative models (e.g., variational autoencoders (VAEs) and generative adversarial networks (GANs)) have produced promising results in studies on brain encoding and decoding. Finally, we propose that the dual learning method, which was originally designed for machine translation tasks, could help to improve the performance of encoding and decoding models by leveraging large-scale unpaired data.
Keywords: Brain encoding and decoding Functional magnetic resonance imaging Deep neural networks Deep generative models Dual learning
Recent advances in efficient computation of deep convolutional neural networks Review
Jian CHENG, Pei-song WANG, Gang LI, Qing-hao HU, Han-qing LU
Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1, Pages 64-77 doi: 10.1631/FITEE.1700789
Keywords: Deep neural networks Acceleration Compression Hardware accelerator
A Survey of Accelerator Architectures for Deep Neural Networks Review
Yiran Chen, uan Xie, Linghao Song, Fan Chen, Tianqi Tang
Engineering 2020, Volume 6, Issue 3, Pages 264-274 doi: 10.1016/j.eng.2020.01.007
Recently, due to the availability of big data and the rapid growth of computing power, artificial intelligence (AI) has regained tremendous attention and investment. Machine learning (ML) approaches have been successfully applied to solve many problems in academia and in industry. Although the explosion of big data applications is driving the development of ML, it also imposes severe challenges of data processing speed and scalability on conventional computer systems. Computing platforms that are dedicatedly designed for AI applications have been considered, ranging from a complement to von Neumann platforms to a “must-have” and standalone technical solution. These platforms, which belong to a larger category named “domain-specific computing,” focus on specific customization for AI. In this article, we focus on summarizing the recent advances in accelerator designs for deep neural networks (DNNs)—that is, DNN accelerators. We discuss various architectures that support DNN executions in terms of computing units, dataflow optimization, targeted network topologies, architectures on emerging technologies, and accelerators for emerging applications. We also provide our visions on the future trend of AI chip designs.
Keywords: Deep neural network Domain-specific architecture Accelerator
Estimating Rainfall Intensity Using an Image-Based Deep Learning Model Article
Hang Yin, Feifei Zheng, Huan-Feng Duan, Dragan Savic, Zoran Kapelan
Engineering 2023, Volume 21, Issue 2, Pages 162-174 doi: 10.1016/j.eng.2021.11.021
Urban flooding is a major issue worldwide, causing huge economic losses and serious threats to public safety. One promising way to mitigate its impacts is to develop a real-time flood risk management system; however, building such a system is often challenging due to the lack of high spatiotemporal rainfall data. While some approaches (i.e., ground rainfall stations or radar and satellite techniques) are available to measure and/or predict rainfall intensity, it is difficult to obtain accurate rainfall data with a desirable spatiotemporal resolution using these methods. This paper proposes an image-based deep learning model to estimate urban rainfall intensity with high spatial and temporal resolution. More specifically, a convolutional neural network (CNN) model called the image-based rainfall CNN (irCNN) model is developed using rainfall images collected from existing dense sensors (i.e., smart phones or transportation cameras) and their corresponding measured rainfall intensity values. The trained irCNN model is subsequently employed to efficiently estimate rainfall intensity based on the sensors' rainfall images. Synthetic rainfall data and real rainfall images are respectively utilized to explore the irCNN's accuracy in theoretically and practically simulating rainfall intensity. The results show that the irCNN model provides rainfall estimates with a mean absolute percentage error ranging between 13.5% and 21.9%, which exceeds the performance of other state-of-the-art modeling techniques in the literature. More importantly, the main feature of the proposed irCNN is its low cost in efficiently acquiring high spatiotemporal urban rainfall data. The irCNN model provides a promising alternative for estimating urban rainfall intensity, which can greatly facilitate the development of urban flood risk management in a real-time manner.
Keywords: Urban flooding Rainfall images Deep learning model Convolutional neural networks (CNNs) Rainfall intensity
DAN: a deep association neural network approach for personalization recommendation Research Articles
Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn
Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7, Pages 963-980 doi: 10.1631/FITEE.1900236
Keywords: Neural network Deep learning Deep association neural network (DAN) Recommendation
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
Keywords: Co-creative drawing Deep learning Image generation
Representation learning via a semi-supervised stacked distance autoencoder for image classification Research Articles
Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7, Pages 963-1118 doi: 10.1631/FITEE.1900116
Keywords: 自动编码器;图像分类;半监督学习;神经网络
Penetration Depth of Projectiles Into Concrete Using Artificial Neural Network
Li Jianguang,Li Yongchi,Wang Yulan
Strategic Study of CAE 2007, Volume 9, Issue 8, Pages 77-81
In this article, nonlinear mapping relation between input of 13 variables of lp and σyt/σyp etc. , and output of penetration depth is established by dimensional analysis and theory of artificial neural networks for problem of penetration depth of projectiles into concrete. Moreover, a satisfied output about penetration depth from RBF neural network is gotten by a group of input sets and corresponding output sets, which comes from M. J. Forrestal 's document.
Keywords: neural networks dimensional analysis penetration depth of projectiles into concrete nonlinear mapping relation RBF neural networks
Associative affinity network learning for multi-object tracking Research Articles
Liang Ma, Qiaoyong Zhong, Yingying Zhang, Di Xie, Shiliang Pu,maliang6@hikvision.com,zhongqiaoyong@hikvision.com,zhangyingying7@hikvision.com,xiedi@hikvision.com,pushiliang.hri@hikvision.com
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 9, Pages 1194-1206 doi: 10.1631/FITEE.2000272
Keywords: 多目标跟踪;深度神经网络;相似度学习
Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images Research Article
Xiaolong CHEN, Xiaoqian MU, Jian GUAN, Ningbo LIU, Wei ZHOU,cxlcxl1209@163.com,guanjian_68@163.com
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 4, Pages 630-643 doi: 10.1631/FITEE.2000611
Keywords: Marine target detection Navigation radar Plane position indicator (PPI) images Convolutional neural network (CNN) Faster R-CNN (region convolutional neural network) method
Flexibility Prediction of Aggregated Electric Vehicles and Domestic Hot Water Systems in Smart Grids Article
Junjie Hu, Huayanran Zhou, Yihong Zhou, Haijing Zhang, Lars Nordströmd, Guangya Yang
Engineering 2021, Volume 7, Issue 8, Pages 1101-1114 doi: 10.1016/j.eng.2021.06.008
With the growth of intermittent renewable energy generation in power grids, there is an increasing demand for controllable resources to be deployed to guarantee power quality and frequency stability. The flexibility of demand response (DR) resources has become a valuable solution to this problem. However, existing research indicates that problems on flexibility prediction of DR resources have not been investigated. This study applied the temporal convolution network (TCN)-combined transformer, a deep learning technique to predict the aggregated flexibility of two types of DR resources, that is, electric vehicles (EVs) and domestic hot water system (DHWS). The prediction uses historical power consumption data of these DR resources and DR signals (DS) to facilitate prediction. The prediction can generate the size and maintenance time of the aggregated flexibility. The accuracy of the flexibility prediction results was verified through simulations of case studies. The simulation results show that under different maintenance times, the size of the flexibility changed. The proposed DR resource flexibility prediction method demonstrates its application in unlocking the demand-side flexibility to provide a reserve to grids.
Keywords: Load flexibility Electric vehicles Domestic hot water system Temporal convolution network-combined transformer Deep learning
Two-level hierarchical feature learning for image classification Article
Guang-hui SONG,Xiao-gang JIN,Gen-lang CHEN,Yan NIE
Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 9, Pages 897-906 doi: 10.1631/FITEE.1500346
Keywords: Transfer learning Feature learning Deep convolutional neural network Hierarchical classification Spectral clustering
Title Author Date Type Operation
Deep 3D reconstruction: methods, data, and challenges
Caixia Liu, Dehui Kong, Shaofan Wang, Zhiyong Wang, Jinghua Li, Baocai Yin,lcxxib@emails.bjut.edu.cn,wangshaofan@bjut.edu.cn
Journal Article
Adversarial Attacks and Defenses in Deep Learning
Kui Ren, Tianhang Zheng, Zhan Qin, Xue Liu
Journal Article
Diffractive Deep Neural Networks at Visible Wavelengths
Hang Chen, Jianan Feng, Minwei Jiang, Yiqun Wang, Jie Lin, Jiubin Tan, Peng Jin
Journal Article
Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models
Changde Du, Jinpeng Li, Lijie Huang, Huiguang He
Journal Article
Recent advances in efficient computation of deep convolutional neural networks
Jian CHENG, Pei-song WANG, Gang LI, Qing-hao HU, Han-qing LU
Journal Article
A Survey of Accelerator Architectures for Deep Neural Networks
Yiran Chen, uan Xie, Linghao Song, Fan Chen, Tianqi Tang
Journal Article
Estimating Rainfall Intensity Using an Image-Based Deep Learning Model
Hang Yin, Feifei Zheng, Huan-Feng Duan, Dragan Savic, Zoran Kapelan
Journal Article
DAN: a deep association neural network approach for personalization recommendation
Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn
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
Representation learning via a semi-supervised stacked distance autoencoder for image classification
Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn
Journal Article
Penetration Depth of Projectiles Into Concrete Using Artificial Neural Network
Li Jianguang,Li Yongchi,Wang Yulan
Journal Article
Associative affinity network learning for multi-object tracking
Liang Ma, Qiaoyong Zhong, Yingying Zhang, Di Xie, Shiliang Pu,maliang6@hikvision.com,zhongqiaoyong@hikvision.com,zhangyingying7@hikvision.com,xiedi@hikvision.com,pushiliang.hri@hikvision.com
Journal Article
Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images
Xiaolong CHEN, Xiaoqian MU, Jian GUAN, Ningbo LIU, Wei ZHOU,cxlcxl1209@163.com,guanjian_68@163.com
Journal Article
Flexibility Prediction of Aggregated Electric Vehicles and Domestic Hot Water Systems in Smart Grids
Junjie Hu, Huayanran Zhou, Yihong Zhou, Haijing Zhang, Lars Nordströmd, Guangya Yang
Journal Article