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Reconstructing QRS Complex From PPG by Transformed Attentional Neural Networks

IEEE SENSORS JOURNAL 2020年第20期20卷 12374-12383页

作者： Chiu, Hong-Yu Shuai, Hong-Han Chao, Paul C. -P. Chiao Tung Univ NCTU BASIC Lab Dept Elect & Comp Engn Hsinchu 30010 Taiwan Natl Chiao Tung Univ NCTU Dept Elect & Comp Engn Sensors IC Lab Hsinchu 30010 Taiwan

Technology that translates photoplethysmogram (PPG) into the QRS complex of electrocardiogram (ECG) would be transformative for people who require continuously monitoring. However, directly decoding the QRS complex of ECG from PPG is challenging because PPG signals usually have different offsets due to 1) different devices, and 2) personal differences, which makes the alignment difficult. In this paper, we make the first attempt to reconstruct the QRS complex of ECG only from the recording of PPG by an end-to-end deep learning-based approach. Specifically, we propose a novel encoder-decoder architecture containing three components: 1) a sequence transformer network which automatically calibrates the offset, 2) an attention network, which dynamically identifies regions of interest, and 3) a new QRS complex-enhanced loss for better reconstruction. The experiment results on a real dataset demonstrate the effectiveness of the proposed method: 3.67% R peak failure rate of the reconstructed ECG and high correlation of pulse transit time between the reconstructed QRS complex and the groundtruth QRS complex (rho = 0.844), which creates a new opportunity for low-cost clinical studies via the waveform-level reconstruction of the QRS complex of ECG from PPG.

关键词： Electrocardiography Monitoring Electrodes Biomedical monitoring Skin Standards Sensors Convolutional neural network electrocardiography encoder-decoder photoplethysmography transform network

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Tracking Performance Limitations of MIMO Networked Control Systems With Multiple Communication Constraints

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IEEE TRANSACTIONS ON CYBERNETICS 2020年第7期50卷 2982-2995页

作者： Chen, Chao-Yang Gui, Weihua Wu, Lianghong Liu, Zhaohua Yan, Huaicheng Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Peoples R China Boston Univ Ctr Polymer Studies Boston MA 02215 USA Boston Univ Dept Phys Boston MA 02215 USA Cent South Univ Sch Informat Sci & Engn Changsha 410012 Peoples R China East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Hubei Normal Univ Coll Mechatron & Control Engn Huangshi 435002 Hubei Peoples R China

In this paper, the tracking performance limitation of networked control systems (NCSs) is studied. The NCSs are considered as continuous-time linear multi-input multioutput (MIMO) systems with random reference noises. The controlled plants include unstable poles and nonminimum phase (NMP) zeros. The output feedback path is affected by multiple communication constraints. We focus on some basic communication constraints, including additive white noise (AWN), quantization noise, bandwidth, as well as encoder-decoder. The system performance is evaluated with the tracking error energy, and used a two-degree-of-freedom (2DOF) controller. The explicit representation of the tracking performance is given in this paper. The results indicate the tracking performance limitations rely to internal characteristics of the plant (unstable poles and NMP zeros), reference noises [the reference noise power distribution (RNPD) and its directions], and the characteristics of communication constraints. The characteristics of communication constraints include communication noise power distribution (CNPD);quantization noise power distribution (QNPD), and their distribution directions;transform bandwidth allocation (TBA);transform encoder-decoder allocation (TEA), and their allocation directions;and NMP zeros and MP part of bandwidth. Moreover, the tracking performance limitations are also affected by the angles between the each transform NMP zero direction and RNPD direction, and these angles between each transform unstable poles direction and the direction of communication constraint distribution/allocation. In addition, for MIMO NCSs, bandwidth (there are not identical two channels) can always affect the direction of unstable poles, and the channel allocation of bandwidth and encode-decode may be used for a feasible method for the performance allocation of each channel. Finally, an instance is given for verifying the effectiveness of the theoretical outcomes.

关键词： MIMO communication Control systems Bandwidth Poles and zeros Power distribution Resource management Linear systems Bandwidth communication noise encoder-decoder performance limitation power distribution quantization noise reference noise

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Multi-energy load forecasting for regional integrated energy systems considering temporal dynamic and coupling characteristics

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ENERGY 2020年第0期195卷 116964-000页

作者： Wang, Shaomin Wang, Shouxiang Chen, Haiwen Gu, Qiang Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin 300072 Peoples R China Tianjin Xianghe Elect Co Ltd Tianjin 300072 Peoples R China State Grid Tianjin Elect Power Res Inst Tianjin 300022 Peoples R China

Accurate multi-energy load forecasting (MELF) is the key to realize the balance between supply and demand in regional integrated energy systems (RIES). To this end, a hybrid MELF method for RIES considering temporal dynamic and coupling characteristics (MELF_TDCC) is proposed. The novelty of MELF_TDCC lies in the following three aspects: 1) considering the high-dimensional temporal dynamic characteristic, an encoder-decoder model based on long-short term memory network (LSTMED) is proposed, which can extract the high dimensional potential feature, and reflect the temporal dynamic characteristics of historical load sequence effectively;2) considering the cross-coupling characteristic, a coupling feature matrix of multi-energy load is constructed, which reflects the cross-influence of electricity, cooling and heating loads;3) with the feature fusion layer of the hybrid model being built by gradient boosting decision tree (GBDT), the extended feature matrix for each class of load is constructed considering the intra-class inherent characteristics and inter-class coupling characteristic of loads, and the GBDT model is trained on the extended feature matrix, which provides multi-dimensional perspective for researching load essential characteristics. MELF_TDCC is verified on the ultra-short-term and short-term MELF scenarios based on an actual dataset. The simulation result shows that the proposed MELF_TDCC outperforms the current advanced methods. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： encoder-decoder Long short-term memory Multi-energy load forecasting Regional integrated energy systems Rolling forecasting

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Large-scale 3D point-cloud semantic segmentation of urban and rural scenes using data volume decomposition coupled with pipeline parallelism

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AUTOMATION IN CONSTRUCTION 2022年第0期133卷 103995-103995页

作者： Chew, Alvin Wei Ze Ji, Ankang Zhang, Limao Bentley Syst Res Off 1 Harbourfront PlHarbourFront Tower One Singapore 098633 Singapore Harbin Inst Technol Sch Management Harbin 150001 Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore 639798 Singapore

This study proposes a generic approach which performs a series of systematic analyses by first introducing a data volume decomposition method to generate useful data features for performing semantic segmentation analysis involving 3D point-cloud data. Pipeline parallelism protocol is then implemented to accelerate the deep learning model's training phase. Our proposed approach is verified by decomposing around 2.0 billion point-cloud data points, as extracted from an open-source Semantic3D dataset, into many 3D regular structures with defined numbers of voxels. Each derived 3D structure has imposed normality in their data distribution of the respective label classes. Using the optimal hyperparameters for model training, the resulting trained model achieves average overall accuracy (mOA) and average intersection over union (mIOU) values of 0.984 and 0.752, respectively, on a testing dataset having close to 800 million point-cloud data points. The results are comparable with that of other state-of-the-art models in the literature.

关键词： Deep learning Image segmentation analysis encoder-decoder Pipeline parallelism 3D point-cloud data

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Spatial interpolation using conditional generative adversarial neural networks

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INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2020年第4期34卷 735-758页

作者： Zhu, Di Cheng, Ximeng Zhang, Fan Yao, Xin Gao, Yong Liu, Yu Peking Univ Sch Earth & Space Sci Inst Remote Sensing & Geog Informat Syst Beijing Peoples R China Peking Univ Beijing Key Lab Spatial Informat Integrat & Its A Beijing Peoples R China UCL SpaceTimeLab Dept Civil Environm & Geomat Engn London England MIT Senseable City Lab 77 Massachusetts Ave Cambridge MA 02139 USA

Spatial interpolation is a traditional geostatistical operation that aims at predicting the attribute values of unobserved locations given a sample of data defined on point supports. However, the continuity and heterogeneity underlying spatial data are too complex to be approximated by classic statistical models. Deep learning models, especially the idea of conditional generative adversarial networks (CGANs), provide us with a perspective for formalizing spatial interpolation as a conditional generative task. In this article, we design a novel deep learning architecture named conditional encoder-decoder generative adversarial neural networks (CEDGANs) for spatial interpolation, therein combining the encoder-decoder structure with adversarial learning to capture deep representations of sampled spatial data and their interactions with local structural patterns. A case study on elevations in China demonstrates the ability of our model to achieve outstanding interpolation results compared to benchmark methods. Further experiments uncover the learned spatial knowledge in the model's hidden layers and test the potential to generalize our adversarial interpolation idea across domains. This work is an endeavor to investigate deep spatial knowledge using artificial intelligence. The proposed model can benefit practical scenarios and enlighten future research in various geographical applications related to spatial prediction.

关键词： Spatial interpolation generative adversarial networks deep learning encoder-decoder spatial prediction

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Model-Based Underwater Image Simulation and Learning-Based Underwater Image Enhancement Method

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INFORMATION 2022年第4期13卷 187-187页

作者： Liu, Yidan Xu, Huiping Zhang, Bing Sun, Kelin Yang, Jingchuan Li, Bo Li, Chen Quan, Xiangqian Chinese Acad Sci Inst Deep Sea Sci & Engn Sanya 572000 Peoples R China

Due to the absorption and scattering effects of light in water bodies and the non-uniformity and insufficiency of artificial illumination, underwater images often present various degradation problems, impacting their utility in underwater applications. In this paper, we propose a model-based underwater image simulation and learning-based underwater image enhancement method for coping with various degradation problems in underwater images. We first derive a simplified model for describing various degradation problems in underwater images, then propose a model-based image simulation method that can generate images with a wide range of parameter values. The proposed image simulation method also comes with an image-selection part, which helps to prune the simulation dataset so that it can serve as a training set for learning to enhance the targeted underwater images. Afterwards, we propose a convolutional neural network based on the encoder-decoder backbone to learn to enhance various underwater images from the simulated images. Experiments on simulated and real underwater images with different degradation problems demonstrate the effectiveness of the proposed underwater image simulation and enhancement method, and reveal the advantages of the proposed method in comparison with many state-of-the-art methods.

关键词： underwater image simulation underwater image enhancement modeling of underwater image degradation encoder-decoder deep learning

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Very-Short-Term Probabilistic Forecasting for a Risk-Aware Participation in the Single Price Imbalance Settlement

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IEEE TRANSACTIONS ON POWER SYSTEMS 2020年第2期35卷 1218-1230页

作者： Bottieau, Jeremie Hubert, Louis De Greve, Zacharie Vallee, Francois Toubeau, Jean-Francois Univ Mons Elect Power Engn Unit B-7000 Mons Belgium

The single imbalance pricing is an emerging mechanism in European electricity markets where all positive and negative imbalances are settled at a unique price. This real-time scheme thereby stimulates market participants to deviate from their schedule to restore the power system balance. However, exploiting this market opportunity is very risky due to the extreme volatility of the real-time power system conditions. In order to address this issue, we implement a new tailored deep-learning model, named encoder-decoder, to generate improved probabilistic forecasts of the imbalance signal, by efficiently capturing its complex spatio-temporal dynamics. The predicted distributions are then used to quantify and optimize the risk associated with the real-time participation of market players, acting as price-makers, in the imbalance settlement. This leads to an integrated forecast-driven strategy, modeled as a robust bi-level optimization. Results show that our probabilistic forecaster achieves better performance than other state of the art tools, and that the subsequent risk-aware robust dispatch tool allows finding a tradeoff between conservative and risk-seeking policies, leading to improved economic benefits. Moreover, we show that the model is computationally efficient and can thus be incorporated in the very-short-term dispatch of market players with flexible resources.

关键词： Deep learning electricity markets encoder-decoder robust optimization single imbalance pricing

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Deep successor feature learning for text generation

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NEUROCOMPUTING 2020年 396卷 495-500页

作者： Xu, Cong Li, Qing Zhang, Dezheng Xie, Yonghong Li, Xisheng Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Beijing Key Lab Knowledge Engn Mat Sci Beijing 100083 Peoples R China

In this paper we present an approach to training neural network to generate sequences using successor feature learning from reinforcement learning. The model can be thought as two components, an MLE-based token generator and an estimator that predicts the future value of whole sentence. As we know, reinforcement learning has been applied to dealing with the exposure bias problem of generating sequences. Compared with other RL algorithm, successor feature(SF) can learn robust value function provided observations and reward by decomposing the value function into two components - a reward predictor and a successor map. The encoder-decoder framework with SF enables the decoder to generate outputs that receive more future reward, which means that the model pays attention on not only the current word but also the rest words. We demonstrate that the approach improves performance on two translation tasks. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Reinforcement learning Successor feature encoder-decoder Neural machine translation Deep learning

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Uneven Image Dehazing by Heterogeneous Twin Network

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IEEE ACCESS 2020年 8卷 118485-118496页

作者： Wang, Keping Yang, Yi Li, Bingfeng Li, Xinwei Cui, Lizhi Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo 454003 Henan Peoples R China

Removing the haze in an image is a huge challenge due to the difficulty of accurate hazy image modeling. Although the atmospheric scattering model (ASM) is widely used to describe the formation of hazy images, it is hard to deal with the uneven haze image, once the ASM is restricted with the assumption that the atmosphere distributed homogeneously. This paper analyzes the haze imaging mechanism, then proposes an image-to-image architecture to handle the uneven image dehazing, in which a heterogeneous twin network (HT-Net) with two parallel sub-networks are constructed to establish the high dimensional nonlinear mapping model between the hazy and clean images. Consequently, the inhomogeneous haze is removed by the symmetric U-shape network with encoder-decoder structure, meanwhile, the other enhancement network extracts the high-frequency feature from the hazy image to compensate the edge and texture of the object. The effectiveness is validated by the experiments based on three real haze image datasets which depict the same visual content recorded in haze-free and hazy conditions, under the same illumination parameters. One professional uneven haze image dataset is found in the real environment and covers 190 types of scene and 21975 uneven images accordingly. This dataset includes thin, heavy, and uneven haze images. The other two benchmark datasets are I-HAZE and O-HAZE, respectively including 35 pairs of indoor real haze and haze-free (ground-truth) images and 45 different outdoor scenes. Extensive experimental results demonstrate that the proposed method in this paper can remove the haze and achieve superior performance over the other mentioned methods.

关键词： Uneven image dehazing restoration atmospheric scattering model (ASM) deep learning encoder-decoder

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Dense feature pyramid network for cartoon dog parsing

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VISUAL COMPUTER 2020年第10-12期36卷 2471-2483页

作者： Wan, Jerome Mougeot, Guillaume Yang, Xubo Shanghai Jiao Tong Univ Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Software Shanghai Peoples R China

While traditional cartoon character drawings are simple for humans to create, it remains a highly challenging task for machines to interpret. Parsing is a way to alleviate the issue with fine-grained semantic segmentation of images. Although well studied on naturalistic images, research toward cartoon parsing is very sparse. Due to the lack of available dataset and the diversity of artwork styles, the difficulty of the cartoon character parsing task is greater than the well-known human parsing task. In this paper, we study one type of cartoon instance: cartoon dogs. We introduce a novel dataset toward cartoon dog parsing and create a new deep convolutional neural network (DCNN) to tackle the problem. Our dataset contains 965 precisely annotated cartoon dog images with seven semantic part labels. Our new model, called dense feature pyramid network (DFPnet), makes use of recent popular techniques on semantic segmentation to efficiently handle cartoon dog parsing. We achieve a mIoU of 68.39%, a Mean Accuracy of 79.4% and a Pixel Accuracy of 93.5% on our cartoon dog validation set. Our method outperforms state-of-the-art models of similar tasks trained on our dataset: CE2P for single human parsing and Mask R-CNN for instance segmentation. We hope this work can be used as a starting point for future research toward digital artwork understanding with DCNN. Our DFPnet and dataset will be publicly available.

关键词： Cartoon character parsing Semantic part segmentation Pyramid network encoder-decoder Deep learning for vision

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