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5th Asian Conference on Pattern Recognition, ACPR 2019

作者： Mo, Xianjie Wei, Tingting Zhang, Hengmin Huang, Qiong Luo, Wei College of Mathematics and Informatics South China Agricultural University GuangzhouGD510642 China Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai200237 China

ISBN: (纸本)9783030414030

Fine-Grained Visual Categorization (FGVC) is challenging due to the superior similarity among categories and the large within-category variance. Existing work tackles this problem by designing self-localization modules in an end-to-end DCNN to learn semantic part features. However the model efficiency of this strategy decreases significantly with the increasing of the number of categories, because more parts are needed to offset the impact of the increasing of categories. In this paper, we propose a label-smooth learning method that improves models applicability to large categories by maximizing its prediction diversity. Based on the similarity among fine-grained categories, a KL divergence between uniform and prediction distributions is established to reduce model’s confidence on the ground-truth category, while raising its confidence on similar categories. By minimizing it, information from similar categories are exploited for model learning, thus diminishing the effects caused by the increasing of categories. Experiments on five benchmark datasets of mid-scale (CUB-200-2011, Stanford Dogs, Stanford Cars, and FGVC-Aircraft) and large-scale (NABirds) categories show a clear advantage of the proposed label-smooth learning and demonstrate its comparable or state-of-the-art performance. Code is available at https://***/Cedric-Mo/LS-for-FGVC. © Springer Nature Switzerland AG 2020.

关键词： Convolutional neural networks

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Real-time Online UAV Images Mosaic with Robustness to Cumulative Errors 20

Real-time Online UAV Images Mosaic with Robustness to Cumula...

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2020 International Conference on Computing, Networks and Internet of Things, CNIOT 2020

作者： Xia, Xing Wang, Bei Li, Jing Song, Li Gong, Yi Hang Deng, Bao Song Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai China National Innovation Institute of Defense Technology Academy of Military Science Tianjin Artificial Intelligence Innovation Center Tianjing China

ISBN: (纸本)9781450377713

In this paper, a novel real-time online UAV image mosaic algorithm is proposed to meet the real-time, robustness and accuracy requirements of practical application. First, the algorithm adopts GPS and track planning information to locate the adjacent images. Instead of the 8-DOF (degrees of freedom) homography transformation, the 4-DOF similarity transformation is estimated to more robustly register the UAV images and reduce their perspective errors. Second, a new method of local registration is developed to reduce the cumulative errors and calculation costs. Meanwhile, the real-time mosaic process is used to register images and update the panoramic image. Finally, the effectiveness of the proposed approach is evaluated by a group of experiments on 4K images taken by quadrotor drones. According to the comparison of experiments, the proposed algorithm is robust to cumulative errors and obtains high-quality panorama. Additionally, through the analysis on stitching time and practical application, frame-by-frame mosaic synthesis is online updated in real-time. © 2020 ACM.

关键词： Unmanned aerial vehicles (UAV)

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Real-time Updating Technology of Local Area Orthoimages Based on 3D Digital Earth 20

Real-time Updating Technology of Local Area Orthoimages Base...

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3rd International Conference on Computer Science and Software Engineering, CSSE 2020

作者： Song, Li Niu, Yugang Deng, Baosong Li, Jing Xia, Xing Gong, Yihang Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai China National Innovation Institute of Defense Technology Academy of Military Science Beijing China School of Software Tianjin University Tianjin China

ISBN: (纸本)9781450375528

In order to solve the problems of poor real-time performance and single display method in traditional unmanned aerial vehicle(UAV) orthographic image mosaic, this paper proposes a real-time updating technology of local area orthoimages based on 3D digital earth, which can be widely used in the fields of emergency rescue, environmental monitoring and military *** technology is that in the process of UAV aerial photography, the server receives sequence aerial image in real time and synchronously completes orthographic image splicing, then automatically cuts them into regular image tiles and transmits the regular image tiles to the display terminal in an incremental manner. The display terminal software constructs a 3D digital earth based on quadtree LOD algorithm, and updates the terrain texture of the target range with the received high-definition orthographic image tiles, so as to realize real-time coverage monitoring of key areas. In this paper, the above work is verified by real aerial images, and the results prove the feasibility and effectiveness of the work. © 2020 ACM.

关键词： Antennas

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Active training sample selection and updating strategy for near-infrared model with an industrial application

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Chinese Journal of Chemical Engineering 2019年第11期27卷 2749-2758页

作者： Kaixun He Kai Wang Yayun Yan College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and TechnologyShanghai 200237China

Training sample selection is widely accepted as an important step in developing a near-infrared(NIR) spectroscopic model. For industrial applications, the initial training dataset is usually selected empirically. This process is time-consuming, and updating the structure of the modeling dataset online is difficult. Considering the static structure of the modeling dataset, the performance of the established NIR model could be degraded in the online process. To cope with this issue, an active training sample selection and updating strategy is proposed in this work. The advantage of the proposed approach is that it can select suitable modeling samples automatically according to the process information. Moreover, it can adjust model coefficients in a timely manner and avoid arbitrary updating effectively. The effectiveness of the proposed method is validated by applying the method to an industrial gasoline blending process.

关键词： Near-infrared spectroscopy Chemical processes Process systems Soft sensor Gasoline blending

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Comparison of CNN-Uni-LSTM and CNN-Bi-LSTM based on single-channel EEG for sleep staging

Comparison of CNN-Uni-LSTM and CNN-Bi-LSTM based on single-c...

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International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)

作者： Qianyu Li Bei Wang Jing Jin Xingyu Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai China

ISBN: (数字)9781728180229

ISBN: (纸本)9781728180236

Sleep staging is an effective method for diagnosing sleep disorder and monitoring sleep quality. With the rapid development of machine learning technology, the automatic staging methods of sleep gradually replace the traditional manual interpretation which can improve the efficiency on sleep staging for medical research. LSTM networks can save the historical information as a reference for the current moment, which is undoubtedly a good way to improve sleep staging performance. In this paper, a convolutional neural network (CNN) is constructed to extract the features from a single-channel EEG. The Uni-directional Long Short-Term Memory (Uni-LSTM) network and Bi-directional Long Short-Term Memory (Bi-LSTM) network are combined with CNN to realize automatic sleep staging. The obtained results showed that the two presented network frameworks are effective and feasible on sleep staging. The Bi-LSTM which has more enriched sequence information got better classification performance than the Uni-LSTM.

关键词： Sleep Manuals Machine learning Feature extraction Electroencephalography Monitoring Medical diagnostic imaging

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Representation evaluation block-based teacher-student network for the industrial quality-relevant performance modeling and monitoring

arXiv

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arXiv 2021年

作者： Yang, Dan Peng, Xin Lu, Yusheng Huang, Haojie Zhong, Weimin Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

Quality-relevant fault detection plays an important role in industrial processes, while the current quality-related fault detection methods based on neural networks main concentrate on process-relevant variables and ignore quality-relevant variables, which restrict the application of process monitoring. Therefore, in this paper, a fault detection scheme based on the improved teacher-student network is proposed for quality-relevant fault detection. In the traditional teacher-student network, as the features differences between the teacher network and the student network will cause performance degradation on the student network, representation evaluation block (REB) is proposed to quantify the features differences between the teacher and the student networks, and uncertainty modeling is used to add this difference in modeling process, which are beneficial to reduce the features differences and improve the performance of the student network. Accordingly, REB and uncertainty modeling is applied in the teacher-student network named as uncertainty modeling teacher-student uncertainty autoencoder (TSUAE). Then, the proposed TSUAE is applied to process monitoring, which can effectively detect faults in the process-relevant subspace and quality-relevant subspace simultaneously. The proposed TSUAE-based fault detection method is verified in two simulation experiments illustrating that it has satisfactory fault detection performance compared to other fault detection methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Wastewater treatment

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Distribution Adaptation Local Outlier Factor for Multimode Process monitoring

Distribution Adaptation Local Outlier Factor for Multimode P...

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第三十九届中国控制会议

作者： Yutang Xiao Yang Tao Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes of the Ministry of Education East China University of Science and Technology

In modern industrial processes, the production process includes multiple operating modes, due to changes in production goals and conditions. And the data generated in this process is a mixture of Gaussian and non-Gaussian distributions. Therefore, the data distribution of multimode processes is uncertain and complex. It is difficult to monitor multimode processes accurately by using traditional Multivariate Statistical Process Monitoring（MSPM） methods. In this case, the absence of labeled data is considered more valuable. This paper proposes a new unsupervised domain adaptive method called the Distribution Adaptation Local Outlier Factor（DALOF）. This method can extract useful information from multimode data through domain adaptation, which can reduce the distribution difference of training data and improve the accuracy of modeling. In the DALOF model, data from different modes in the training set are defined as the source domain and the target domain. Weight the training data according to their correlation, and then project these data into a low-dimensional subspace. In this space, the distribution distance between the source and target domain is minimum. To solve the nonlinear characteristics of multimode data, this paper uses LOF to build a model and formed a density-based monitoring index. Numerical examples and Tennessee Eastman（TE） process simulation demonstrate the effectiveness of DALOF.

关键词： Distribution Adaptation Local Outlier Factor Multimode Process Process monitoring

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Modeling of oil near-infrared spectroscopy based on similarity and transfer learning algorithm

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Frontiers of Chemical Science and Engineering 2019年第3期13卷 599-607页

作者： Yifei Wang Kai Wang Zhao Zhou Wenli Du Key Laboratory of Advanced Control and Optimization for Chemical Processes (Ministry of Education) East China University of Science and TechnologyShanghai 200237China School of information science and engineering East China University of Science and TechnologyShanghai 200237China

Near-infrared spectroscopy mainly reflects the frequency-doubled and total-frequency absorption information of hydrogen-containing groups (0 -H, C-H, N-H, S-H) in organic molecules for near-infrared lights with different wavelengths, so it is applicable to testing of most raw materials and products in the field of petrochemicals. However, the modeling process needs to collect a large number of laboratory analysis data. There are many oil sources in China, and oil properties change frequently. Modeling of each raw material is not only unfeasible but also will affect its engineering application efficiency. In order to achieve rapid modeling of near-infrared spectroscopy and based on historical data of different crude oils under different detection conditions, this paper discusses about the feasibility of the application of transfer learning algorithm and makes it possible that transfer learning can assist in rapid modeling using certain historical data under similar distributions under a small quantity of new data. In consideration of the requirement of transfer learning for certain similarity of different datasets, a transfer learning method based on local similarity feature selection is proposed. The simulation verification of spectral data of 13 crude oils measured by three different probe detection methods is performed. The effectiveness and application scope of the transfer modeling method under different similarity conditions are analyzed.

关键词： near-infrared spectroscopy transfer learning similarity modeling

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Zoning search using a hyper-heuristic algorithm

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Science China(Information Sciences) 2019年第9期62卷 193-195页

作者： Qinqin FAN Ning LI Yilian ZHANG Xuefeng YAN Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai Jiao Tong University Logistics Research Center Shanghai Maritime University Key Laboratory of Marine Technology and Control Engineering Ministry of CommunicationsShanghai Maritime University Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

Dear editor,With the development of high-performance computing techniques, run time of meta-heuristic algorithms can be reduced effectively. However, improved computation power has not been indirectly converted into search capability in most of previous studies [1]. Moreover, the solution precision of an optimization problem is directly determined

关键词： pro CEC Zoning search using a hyper-heuristic algorithm

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Automatic detection of awake state during sleep based on the approximate entropy of EMG

Automatic detection of awake state during sleep based on the...

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International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)

作者： Qihui Ai Bei Wang Jing Jin Xingyu Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Ministry of Education Shanghai China

ISBN: (数字)9781728180229

ISBN: (纸本)9781728180236

Sleep is important to human beings. A good sleep can bring sufficient energy and keep healthy. Sleep process contains several sleep stages. There are also several short-duration awake states appeared in sleep process. The amount of those awake states during sleep would be related to evaluate the sleep quality. In this study, the automatic detection method of awake state is investigated on the sleep recording of patient with sleep disorder. Two nonlinear features of ApEN and Mc-ApEN are calculated based on the EMG signal recorded during one's overnight sleep process. The detection performance is analyzed and compared. The obtained results showed that the presented method is feasible for awake state detection during sleep, where Mc-ApEN is slightly better than ApEN. It can be an assistant tool for sleep evaluation.

关键词： Tools Electromyography Entropy Informatics

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