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Mingzhi Zhao, Huiliang Wei, Yiming Mao, Changdong Zhang, Tingting Liu, Wenhe Liao
Engineering 2023, Volume 23, Issue 4, Pages 181-195 doi: 10.1016/j.eng.2022.09.015
Molten pool characteristics have a significant effect on printing quality in laser powder bed fusion (PBF), and quantitative predictions of printing parameters and molten pool dimensions are critical to the intelligent control of the complex processes in PBF. Thus far, bidirectional predictions of printing parameters and molten pool dimensions have been challenging due to the highly nonlinear correlations involved. To
address this issue, we integrate an experiment on molten pool characteristics, a mechanistic model, and deep learning to achieve both forward and inverse predictions of key parameters and molten pool characteristics during laser PBF. The experiment provides fundamental data, the mechanistic model significantly augments the dataset, and the multilayer perceptron (MLP) deep learning model predicts the molten pool dimensions and process parameters based on the dataset built from the experiment and the mechanistic model. The results show that bidirectional predictions of the molten pool dimensions and process parameters can be realized, with the highest prediction accuracies approaching 99.9% and mean prediction accuracies of over 90.0%. Moreover, the prediction accuracy of the MLP model is closely related to the characteristics of the dataset—that is, the learnability of the dataset has a crucial impact on the prediction accuracy. The highest prediction accuracy is 97.3% with enhancement of the dataset via the mechanistic model, while the highest prediction accuracy is 68.3% when using only the experimental dataset. The prediction accuracy of the MLP model largely depends on the quality of the dataset as well. The research results demonstrate that bidirectional predictions of complex correlations using MLP are feasible for laser PBF, and offer a novel and useful framework for the determination of process conditions and outcomes for intelligent additive manufacturing.
Keywords： Additive manufacturing Molten pool Model Deep learning Learnability
Visual interpretability for deep learning: a survey Review
Quan-shi ZHANG, Song-chun ZHU
Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1, Pages 27-39 doi: 10.1631/FITEE.1700808
Keywords： Artificial intelligence Deep learning Interpretable model
Tandem hiddenMarkovmodels using deep belief networks for offline handwriting recognition Article
Partha Pratim ROY, Guoqiang ZHONG, Mohamed CHERIET
Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7, Pages 978-988 doi: 10.1631/FITEE.1600996
Keywords： Handwriting recognition Hidden Markov models Deep learning Deep belief networks Tandem approach
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
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
Progress in Neural NLP: Modeling, Learning, and Reasoning Review
Ming Zhou, Nan Duan, Shujie Liu, Heung-Yeung Shum
Engineering 2020, Volume 6, Issue 3, Pages 275-290 doi: 10.1016/j.eng.2019.12.014
Natural language processing (NLP) is a subfield of artificial intelligence (AI) that focuses on enabling computers to understand and process human languages. In the last five years, we have witnessed the rapid development of NLP in tasks such as machine translation, question-answering, and machine reading comprehension based on deep learning and an enormous volume of annotated and unannotated data. In this paper, we will review the latest progress in the neural network-based NLP framework (neural NLP) from three perspectives: modeling, learning, and reasoning. In the modeling section, we will describe several fundamental neural network-based modeling paradigms, such as word embedding, sentence embedding, and sequence-to-sequence modeling, which are widely used in modern NLP engines. In the learning section, we will introduce widely used learning methods for NLP models, including supervised, semi-supervised, and unsupervised learning; multitask learning; transfer learning; and active learning. We view reasoning as a new and exciting direction for neural NLP, but it has yet to be well addressed. In the reasoning section, we will review reasoning mechanisms, including the knowledge, existing non-neural inference methods, and new neural inference methods. We emphasize the importance of reasoning in this paper because it is important for building interpretable and knowledge-driven neural NLP models to handle complex tasks. At the end of this paper, we will briefly outline our thoughts on the future directions of neural NLP.
Keywords： Natural language processing Deep learning Modeling learning and Reasoning
A deep Q-learning network based active object detection model with a novel training algorithm for service robots Research Article
Shaopeng LIU, Guohui TIAN, Yongcheng CUI, Xuyang SHAO
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 11, Pages 1673-1683 doi: 10.1631/FITEE.2200109
This paper focuses on the problem of (AOD). AOD is important for to complete tasks in the family environment, and leads robots to approach the target object by taking appropriate moving actions. Most of the current AOD methods are based on reinforcement learning with low training efficiency and testing accuracy. Therefore, an AOD model based on a (DQN) with a novel training algorithm is proposed in this paper. The DQN model is designed to fit the Q-values of various actions, and includes state space, feature extraction, and a multilayer perceptron. In contrast to existing research, a novel training algorithm based on memory is designed for the proposed DQN model to improve training efficiency and testing accuracy. In addition, a method of generating the end state is presented to judge when to stop the AOD task during the training process. Sufficient comparison experiments and ablation studies are performed based on an AOD dataset, proving that the presented method has better performance than the comparable methods and that the proposed training algorithm is more effective than the raw training algorithm.
Keywords： Active object detection Deep Q-learning network Training method Service robots
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
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
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
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
Robust object tracking with RGBD-based sparse learning Article
Zi-ang MA, Zhi-yu XIANG
Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7, Pages 989-1001 doi: 10.1631/FITEE.1601338
Keywords： Object tracking Sparse learning Depth view Occlusion templates Occlusion detection
Disambiguating named entitieswith deep supervised learning via crowd labels Article
Le-kui ZHOU,Si-liang TANG,Jun XIAO,Fei WU,Yue-ting ZHUANG
Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 1, Pages 97-106 doi: 10.1631/FITEE.1601835
Keywords： Named entity disambiguation Crowdsourcing Deep learning
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
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
This work introduces an optimal transportation (OT) view of generative adversarial networks (GANs). Natural datasets have intrinsic patterns, which can be summarized as the manifold distribution principle: the distribution of a class of data is close to a low-dimensional manifold. GANs mainly accomplish two tasks: manifold learning and probability distribution transformation. The latter can be carried out using the classical OT method. From the OT perspective, the generator computes the OT map, while the discriminator computes the Wasserstein distance between the generated data distribution and the real data distribution; both can be reduced to a convex geometric optimization process. Furthermore, OT theory discovers the intrinsic collaborative—instead of competitive—relation between the generator and the discriminator, and the fundamental reason for mode collapse. We also propose a novel generative model, which uses an autoencoder (AE) for manifold learning and OT map for probability distribution transformation. This AE–OT model improves the theoretical rigor and transparency, as well as the computational stability and efficiency; in particular, it eliminates the mode collapse. The experimental results validate our hypothesis, and demonstrate the advantages of our proposed model.
Keywords： Generative Adversarial Deep learning Optimal transportation Mode collapse
Title Author Date Type Operation
Predictions of Additive Manufacturing Process Parameters and Molten Pool Dimensions with a Physics-Informed Deep Learning Model
Mingzhi Zhao, Huiliang Wei, Yiming Mao, Changdong Zhang, Tingting Liu, Wenhe Liao
Journal Article
Tandem hiddenMarkovmodels using deep belief networks for offline handwriting recognition
Partha Pratim ROY, Guoqiang ZHONG, Mohamed CHERIET
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
Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models
Changde Du, Jinpeng Li, Lijie Huang, Huiguang He
Journal Article
Progress in Neural NLP: Modeling, Learning, and Reasoning
Ming Zhou, Nan Duan, Shujie Liu, Heung-Yeung Shum
Journal Article
A deep Q-learning network based active object detection model with a novel training algorithm for service robots
Shaopeng LIU, Guohui TIAN, Yongcheng CUI, Xuyang SHAO
Journal Article
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
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
Adversarial Attacks and Defenses in Deep Learning
Kui Ren, Tianhang Zheng, Zhan Qin, Xue Liu
Journal Article
Disambiguating named entitieswith deep supervised learning via crowd labels
Le-kui ZHOU,Si-liang TANG,Jun XIAO,Fei WU,Yue-ting ZHUANG
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
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