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A review of computer graphics approaches to urban modeling from a machine learning perspective Review Article
Tian Feng, Feiyi Fan, Tomasz Bednarz,t.feng@latrobe.edu.au
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 7, Pages 915-925 doi: 10.1631/FITEE.2000141
Keywords： 城市建模；计算机图形学；机器学习；深度学习
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring Review
Billie F. Spencer Jr.,Vedhus Hoskere,Yasutaka Narazaki
Engineering 2019, Volume 5, Issue 2, Pages 199-222 doi: 10.1016/j.eng.2018.11.030
Computer vision techniques, in conjunction with acquisition through remote cameras and unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure condition assessment. The ultimate goal of such a system is to automatically and robustly convert the image or video data into actionable information. This paper provides an overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment. In particular, relevant research in the fields of computer vision, machine learning, and structural engineering are presented. The work reviewed is classified into two types: inspection applications and monitoring applications. The inspection applications reviewed include identifying context such as structural components, characterizing local and global visible damage, and detecting changes from a reference image. The monitoring applications discussed include static measurement of strain and displacement, as well as dynamic measurement of displacement for modal analysis. Subsequently, some of the key challenges that persist towards the goal of automated vision-based civil infrastructure and monitoring are presented. The paper concludes with ongoing work aimed at addressing some of these stated challenges.
Keywords： Structural inspection and monitoring Artificial intelligence Computer vision Machine learning Optical flow
Oguzhan Dogru, Kirubakaran Velswamy, Biao Huang
Engineering 2021, Volume 7, Issue 9, Pages 1248-1261 doi: 10.1016/j.eng.2021.04.027
This paper synchronizes control theory with computer vision by formalizing object tracking as a sequential decision-making process. A reinforcement learning (RL) agent successfully tracks an interface between two liquids, which is often a critical variable to track in many chemical, petrochemical, metallurgical, and oil industries. This method utilizes less than 100 images for creating an environment, from which the agent generates its own data without the need for expert knowledge. Unlike supervised learning (SL) methods that rely on a huge number of parameters, this approach requires far fewer parameters, which naturally reduces its maintenance cost. Besides its frugal nature, the agent is robust to environmental uncertainties such as occlusion, intensity changes, and excessive noise. From a closed-loop control context, an interface location-based deviation is chosen as the optimization goal during training. The methodology showcases RL for real-time object-tracking applications in the oil sands industry. Along with a presentation of the interface tracking problem, this paper provides a detailed review of one of the most effective RL methodologies: actor–critic policy.
Keywords： Interface tracking Object tracking Occlusion Reinforcement learning Uniform manifold approximation and projection
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
Neuromorphic Computing Advances Deep-Learning Applications
Chris Palmer
Engineering 2020, Volume 6, Issue 8, Pages 854-856 doi: 10.1016/j.eng.2020.06.010
First Supercomputer Breaks Exascale Barrier, with More Expected Soon
Mitch Leslie
Engineering 2023, Volume 23, Issue 4, Pages 10-12 doi: 10.1016/j.eng.2023.02.004
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
New directions for artificial intelligence: human, machine, biological, and quantum intelligence Comment
Li WEIGANG,Liriam Michi ENAMOTO,Denise Leyi LI,Geraldo Pereira ROCHA FILHO
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 6, Pages 984-990 doi: 10.1631/FITEE.2100227
This comment reviews the “once learning” mechanism (OLM) that was proposed byWeigang (1998), the subsequent success of “one-shot learning” in object categories (Li FF et al., 2003), and “you only look once” (YOLO) in objective detection (Redmon et al., 2016). Upon analyzing the current state of research in artificial intelligence (AI), we propose to divide AI into the following basic theory categories: artificial human intelligence (AHI), artificial machine intelligence (AMI), artificial biological intelligence (ABI), and artificial quantum intelligence (AQI). These can also be considered as the main directions of research and development (R&D) within AI, and distinguished by the following classification standards and methods: (1) human-, machine-, biological-, and quantum-oriented AI R&D; (2) information input processed by dimensionality increase or reduction; (3) the use of one/a few or a large number of samples for knowledge learning.
Keywords： 人工智能；机器学习；一次性学习；一瞥学习；量子计算
Computer Simulation of Metal Solidification Microstructures
Zhu Mingfang,Yu Jin,Hong Junzhuo
Strategic Study of CAE 2004, Volume 6, Issue 5, Pages 8-16
Computer simulation has been one of the most important and advanced research fields in the materials science and engineering. It is playing an increasing important role in the studies of microstructural evolution during solidification of metals and alloys. In this paper, the recent progress in computer simulation of solidification microstructures is briefly reviewed. Various models including deterministic and stochastic approaches for the prediction of solidification microstructures are compared and assessed. Then, a modified cellular automaton ( MCA) model developed by the authors is introduced and its predictive capabilities are described by presenting some examples including the modeling of 2D & 3D dendritic growth, non-dendritic or globular microstructure evolution in semi-solid process, eutectic and peritectic microstructure formation, as well as the asymmetric dendritic growth features in the presence of melt convection.
Keywords： solidification microstructure computer simulation Cellular Automaton Model
Meteorological Telecommunication Network &Computer Systems Engineering
Li Huang,Wang Chunhu
Strategic Study of CAE 2000, Volume 2, Issue 9, Pages 101-106
As the information infrastructure for the meteorological services in China, meteorological telecommunication networks and computer systems have made great progress in terms of networking and computer applications. For the 21st century, the meteorological services in China will continue to be improved with leaps and bounds, the meteorological information infrastructure will also be further strengthened and along developed the expansion and advance of the national information infrastructure. This article introduces the developments regarding both meteorological telecommunication networks and computer systems, and presents the status of the construction in modernized system projects of China Meteorological Administration as well as it´s engineering applications .
Keywords： telecommunication network computer systems engineering
Visual knowledge guided intelligent generation of Chinese seal carving Research Article
Kejun ZHANG, Rui ZHANG, Yehang YIN, Yifei LI, Wenqi WU, Lingyun SUN, Fei WU, Huanghuang DENG, Yunhe PAN
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 10, Pages 1479-1493 doi: 10.1631/FITEE.2100094
We digitally reproduce the process of resource collaboration, design creation, and visual presentation of Chinese art. We develop an intelligent art-generation system (Zhejiang University Intelligent System, http://www.next.zju.edu.cn/seal/; the website of the search and layout system is http://www.next.zju.edu.cn/seal/search_app/) to deal with the difficulty in using a visual knowledge guided approach. The knowledge base in this study is the Qiushi Database, which consists of open datasets of images of seal characters and seal stamps. We propose a seal character generation method based on visual knowledge, guided by the database and expertise. Furthermore, to create the layout of the seal, we propose a deformation algorithm to adjust the seal characters and calculate layout parameters from the database and knowledge to achieve an intelligent structure. Experimental results show that this method and system can effectively deal with the difficulties in the generation of seal carving. Our work provides theoretical and applied references for the rebirth and innovation of art.
Keywords： Seal-carving Intelligent generation Deep learning Parametric modeling Computational art
Data-driven soft sensors in blast furnace ironmaking: a survey Review Article
Yueyang LUO, Xinmin ZHANG, Manabu KANO, Long DENG, Chunjie YANG, Zhihuan SONG
Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 3, Pages 327-354 doi: 10.1631/FITEE.2200366
Keywords： Soft sensors Data-driven modeling Machine learning Deep learning Blast furnace Ironmaking process
Jane Palmer
Engineering 2019, Volume 5, Issue 3, Pages 357-358 doi: 10.1016/j.eng.2019.04.007
The World’s Biggest Computer Chip
Marcus Woo
Engineering 2020, Volume 6, Issue 1, Pages 6-7 doi: 10.1016/j.eng.2019.11.001
Title Author Date Type Operation
A review of computer graphics approaches to urban modeling from a machine learning perspective
Tian Feng, Feiyi Fan, Tomasz Bednarz,t.feng@latrobe.edu.au
Journal Article
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring
Billie F. Spencer Jr.,Vedhus Hoskere,Yasutaka Narazaki
Journal Article
2020年计算机视觉、图像与深度学习国际学术会议（CVIDL 2020）
15 May 2020
Conference Information
Actor–Critic Reinforcement Learning and Application in Developing Computer-Vision-Based Interface Tracking
Oguzhan Dogru, Kirubakaran Velswamy, Biao Huang
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
Adversarial Attacks and Defenses in Deep Learning
Kui Ren, Tianhang Zheng, Zhan Qin, Xue Liu
Journal Article
New directions for artificial intelligence: human, machine, biological, and quantum intelligence
Li WEIGANG,Liriam Michi ENAMOTO,Denise Leyi LI,Geraldo Pereira ROCHA FILHO
Journal Article
Computer Simulation of Metal Solidification Microstructures
Zhu Mingfang,Yu Jin,Hong Junzhuo
Journal Article
Meteorological Telecommunication Network &Computer Systems Engineering
Li Huang,Wang Chunhu
Journal Article
Visual knowledge guided intelligent generation of Chinese seal carving
Kejun ZHANG, Rui ZHANG, Yehang YIN, Yifei LI, Wenqi WU, Lingyun SUN, Fei WU, Huanghuang DENG, Yunhe PAN
Journal Article
Data-driven soft sensors in blast furnace ironmaking: a survey
Yueyang LUO, Xinmin ZHANG, Manabu KANO, Long DENG, Chunjie YANG, Zhihuan SONG
Journal Article
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