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Real-time energy-efficient optimal control of high-speed electric train

control ENGINEERING PRACTICE 2021年 112卷

作者： Zhou, Hao Wan, Yiming Ye, Hao Jiang, Ming Wang, Jia Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRis Dept Automat Beijing 100084 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China CRSC Res & Design Inst Grp Co Ltd Beijing 100070 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

This paper studies the real-time energy-efficient optimal control of high-speed electric trains within a given trip time. In particular, we focus on the re-optimization of the driving strategy, in the case that the actual train speed deviates from the planned optimal speed profile due to uncertainties. The proposed optimization algorithm has a double-loop structure with simultaneous action implementation. It results in reduced computation complexity and shortened control period, compared to the conventional double-loop algorithm with delayed action implementation. Existence and uniqueness of the solution to the re-optimization problem are discussed, and the convergence of the proposed simultaneous action method is proved. Moreover, it is shown by perturbation analysis that the resulting energy consumption during speed switching process is reduced. The effectiveness of the proposed method is demonstrated by the simulation study on two tracks of Dongguan-Huizhou intercity railway in China.

关键词： Optimal control Pontryagin's maximum principle Energy efficient operation Real-time control High-speed electric train

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keypoint-Based Feature Matching For Partial Person Re-Identification

Keypoint-Based Feature Matching For Partial Person Re-Identi...

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IEEE International Conference on image processing

作者： Chuchu Han Changxin Gao Nong Sang Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

As a derivative of person re-identification (re-ID), partial re- ID aims to retrieve a partial pedestrian across holistic person images captured by non-overlapping cameras. This task is more challenging and closer to real-world applications. Since we cannot locate the part of the partial image, the misaligned region compromises the performance greatly when directly (a) compare a partial pedestrian with a holistic one. To alleviate this issue, we propose a keypoint-Based Feature Matching (KBFM) network, which constructs a simple and effective framework for partial re-ID. Specifically, our architecture explicitly leverages the keypoints generated by pose estimation. Based on the visible keypoints, coordinates of the corresponding visible region can be computed. And the keypoint-based feature embeddings can be generated by bilinear sampling. When matching two images, we extract their features on the basis of shared visible keypoints, avoiding the misalignment and disturbance. Moreover, considering the triplet loss cannot be flexibly built in the partial re-ID pipeline, we improve the original sampling method and achieve significant performance. Extensive experimental results on two widely used benchmarks demonstrate significant performance improvements of our method over most state-of-the-art methods.

关键词： Feature extraction Cameras Task analysis Pose estimation Training Pipelines Generative adversarial networks

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Battery Parameter Identification Using Recursive Least Squares with Variable Directional Forgetting

Battery Parameter Identification Using Recursive Least Squar...

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International Conference on control and Automation (ICCA)

作者： Kun Zhu Yiming Wan Chenou Li Xin Luo Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

An accurate battery model is important to perform various tasks of battery management. Battery model parameters change with working conditions, thus requiring continuous update for parameter estimation. The recursive least-squares algorithm with a forgetting strategy is widely used to estimate time-varying parameters online. However, the online data vary with battery operations, so the data may fail to contain sufficient excitation. When the online data is not persistently exciting, most existing forgetting strategies in battery literature suffer from covariance blowup, which means the algorithm cannot identify parameters reliably. In order to cope with non-persistent excitation, a recursive least-squares algorithm with variable direction forgetting is presented in this paper for battery parameter estimation. Instead of discounting all old information, it performs forgetting only in the direction of sufficient excitation. The forgetting factor can adaptively adjust based on excitation, which enhances trade-off between stability and tracking capability of the algorithm. A battery simulation model with time-varying parameters is used to illustrate the effectiveness of our proposed algorithm.

关键词： Batteries State of charge Electronic countermeasures Computational modeling Parameter estimation Load modeling Integrated circuit modeling

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Fault detection of lithium-ion batteries subject to probabilistic parametric uncertainties 11

Fault detection of lithium-ion batteries subject to probabil...

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11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2019

作者： Liu, Yuhao Wan, Yiming Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China

ISBN: (纸本)9781728106816

The fault detection task in lithium-ion battery management system (BMS) is critical to the safety and reliability of rechargeable and hybrid electric vehicles. To explicitly account for inevitable errors of battery model parameters, a robust fault detection method is proposed in this paper. The residual generator is established by exploiting an equivalent circuit model. The effect of uncertainties on the residual is parametrized as polynomial dependence on probabilistic uncertain parameters and noises. Then Gaussian mixtures are adopted to derive the residual distribution. To account for residual uncertainties in the detection decision, the weighted sum of distances from the generated residual to each Gaussian component of the residual distribution is proposed. Simulation results illustrate that our proposed method outperforms the conventional approach that does not consider parametric uncertainties. © 2019 IEEE.

关键词： Fault detection

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Temporal context aggregation network for temporal action proposal refinement

arXiv

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arXiv 2021年

作者： Qing, Zhiwu Su, Haisheng Gan, Weihao Wang, Dongliang Wu, Wei Wang, Xiang Qiao, Yu Yan, Junjie Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China SenseTime Research Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Shanghai AI Laboratory Shanghai China

Temporal action proposal generation aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet important task in the video understanding field. The proposals generated by current methods still suffer from inaccurate temporal boundaries and inferior confidence used for retrieval owing to the lack of efficient temporal modeling and effective boundary context utilization. In this paper, we propose Temporal Context Aggregation Network (TCANet) to generate high-quality action proposals through "local and global" temporal context aggregation and complementary as well as progressive boundary refinement. Specifically, we first design a Local-Global Temporal Encoder (LGTE), which adopts the channel grouping strategy to efficiently encode both "local and global" temporal inter-dependencies. Furthermore, both the boundary and internal context of proposals are adopted for frame-level and segment-level boundary regressions, respectively. Temporal Boundary Regressor (TBR) is designed to combine these two regression granularities in an end-to-end fashion, which achieves the precise boundaries and reliable confidence of proposals through progressive refinement. Extensive experiments are conducted on three challenging datasets: HACS, ActivityNet-v1.3, and THUMOS-14, where TCANet can generate proposals with high precision and recall. By combining with the existing action classifier, TCANet can obtain remarkable temporal action detection performance compared with other methods. Not surprisingly, the proposed TCANet won the 1st place in the CVPR 2020 - HACS challenge leaderboard on temporal action localization task. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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A New control System Based on An Improved PID Neural Network controller for Landslide System control

A New Control System Based on An Improved PID Neural Network...

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Chinese control Conference (CCC)

作者： Ying Zhang Shu Sun Cheng Lian Xiaoping Wang School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

In this paper, a new control system based on the improved PID neural network controller (PIDNNC) is designed to control the landslide displacement. In the control system, the fuzzy comprehensive judgement model of landslide system is established to judge whether the system needs to be controlled and give the expected displacement. To solve the problem of random initialization in PIDNNC, This paper proposed an improved PIDNNC based on fuzzy PID controller (F-PIDNNC). The fuzzy PID controller is used to obtain the proportional, integral and differential coefficients k p , k i and k d . Then the obtained parameters are taken as the initial weights of PIDNN. The effectiveness of the proposed control system is simulated on Baishuihe and Shiliushubao landslides. The results show that the proposed control system based on F-PIDNNC is much accurate and faster than the traditional one-dimensional fuzzy PID controller and PIDNNC.

关键词： Terrain factors control systems Neural networks Neurons Predictive models Stability criteria

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Multi-to-binary network (MTBNet) for automated multi-organ segmentation on multi-sequence abdominal MRI images

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PHYSICS IN MEDICINE AND BIOLOGY 2020年第16期65卷 165013页

作者： Zhao, Xiangming Huang, Minxin Li, Laquan Qi, X. Sharon Tan, Shan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Chongqing Univ Posts & Telecommun Sch Sci Chongqing 400065 Peoples R China Univ Calif Los Angeles Dept Radiat Oncol Los Angeles CA 90024 USA

Fully convolutional neural network (FCN) has achieved great success in semantic segmentation. However, the performance of the FCN is generally compromised for multi-object segmentation. Multi-organ segmentation is very common while challenging in the field of medical image analysis, where organs largely vary with scales. Different organs are often treated equally in most segmentation networks, which is not quite optimal. In this work, we propose to divide a multi-organ segmentation task into multiple binary segmentation tasks by constructing a multi-to-binary network (MTBNet). The proposed MTBNet is based on the FCN for pixel-wise prediction. Moreover, we build a plug-and-play multi-to-binary block (MTB block) to adjust the influence of the feature maps from the backbone. This is achieved by parallelizing multiple branches with different convolutional layers and a probability gate (ProbGate). The ProbGate is set up for predicting whether the class exists, which is supervised clearly via an auxiliary loss without using any other inputs. More reasonable features are achieved by summing branches' features multiplied by the probability from the accompanying ProbGate and fed into a decoder module for prediction. The proposed method is validated on a challenging task dataset of multi-organ segmentation in abdominal MRI. Compared to classic medical and other multi-scale segmentation methods, the proposed MTBNet improves the segmentation accuracy of small organs by adjusting features from different organs and reducing the chance of missing or misidentifying these organs.

关键词： multi-organ segmentation fully convolutional neural network plug-and-play abdominal MRI

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Deep learning for variational multimodality tumor segmentation in PET/CT

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NEUROCOMPUTING 2020年 392卷 277-295页

作者： Li, Laquan Zhao, Xiangming Lu, Wei Tan, Shan Huazhong Univ Sci & Technol Sch Automat Key Lab Image Proc & Intelligent Control Minist Educ China Wuhan 430074 Peoples R China Chongqing Univ Posts & Telecommun Coll Sci Chongqing 400065 Peoples R China Mem Sloan Kettering Canc Ctr Dept Med Phys New York NY 10065 USA

Positron emission tomography/computed tomography (PET/CT) imaging can simultaneously acquire functional metabolic information and anatomical information of the human body. How to rationally fuse the complementary information in PET/CT for accurate tumor segmentation is challenging. In this study, a novel deep learning based variational method was proposed to automatically fuse multimodality information for tumor segmentation in PET/CT. A 3D fully convolutional network (FCN) was first designed and trained to produce a probability map from the CT image. The learnt probability map describes the probability of each CT voxel belonging to the tumor or the background, and roughly distinguishes the tumor from its surrounding soft tissues. A fuzzy variational model was then proposed to incorporate the probability map and the PET intensity image for an accurate multimodality tumor segmentation, where the probability map acted as a membership degree prior. A split Bregman algorithm was used to minimize the variational model. The proposed method was validated on a non-small cell lung cancer dataset with 84 PET/CT images. Experimental results demonstrated that: (1) Only a few training samples were needed for training the designed network to produce the probability map;(2) The proposed method can be applied to small datasets, normally seen in clinic research;(3) The proposed method successfully fused the complementary information in PET/CT, and outperformed two existing deep learning-based multimodality segmentation methods and other multimodality segmentation methods using traditional fusion strategies (without deep learning);(4) The proposed method had a good performance for tumor segmentation, even for those with Fluorodeoxyglucose (FDG) uptake inhomogeneity and blurred tumor edges (two major challenges in PET single modality segmentation) and complex surrounding soft tissues (one major challenge in CT single modality segmentation), and achieved an average dice similarity inde

关键词： Tumor segmentation PET/CT images Variational method Deep learning Information fusion

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Quasi-Synchronization of Heterogeneous Networks With a Generalized Markovian Topology and Event-Triggered Communication

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IEEE TRANSACTIONS ON CYBERNETICS 2020年第10期50卷 4200-4213页

作者： Liu, Xinghua Tay, Wee Peng Liu, Zhi-Wei Xiao, Gaoxi Xian Univ Technol Dept Elect Engn Xian 710048 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China Minist China Key Lab Image Proc & Intelligent Control Educ Wuhan Peoples R China

We consider the quasi-synchronization problem of a continuous time generalized Markovian switching heterogeneous network with time-varying connectivity, using pinned nodes that are event-triggered to reduce the frequency of controller updates and internode communications. We propose a pinning strategy algorithm to determine how many and which nodes should be pinned in the network. Based on the assumption that a network has limited control efficiency, we derive a criterion for stability, which relates the pinning feedback gains, the coupling strength, and the inner coupling matrix. By utilizing the stochastic Lyapunov stability analysis, we obtain sufficient conditions for exponential quasi-synchronization under our stochastic event-triggering mechanism, and a bound for the quasi-synchronization error. Numerical simulations are conducted to verify the effectiveness of the proposed control strategy.

关键词： Switches Synchronization Heterogeneous networks Network topology Topology Couplings Stochastic processes Event-triggered control generalized Markovian topology heterogeneous network quasi-synchronization

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Lifecycle operational resilience assessment of urban water distribution networks

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2020年 198卷 106859-000页

作者： Liu, Wei Song, Zhaoyang Ouyang, Min Tongji Univ State Key Lab Disaster Reduct Civil Engn Shanghai 200092 Peoples R China Tongji Univ Dept Struct Engn Shanghai 200092 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 611731 Hubei Peoples R China Huazhong Univ Sci & Technol Key Lab Image Informat Proc & Intelligent Control Minist Educ Wuhan 611731 Hubei Peoples R China

This paper proposes a lifecycle operational resilience assessment framework of urban water distribution networks (WDNs), taking accidents, pipe deterioration, and restoration into account. First, the accident occurrence rates for three common types of pipes, namely, cast iron pipes (CIPs), ductile iron pipes (DIPs), and steel pipes (SPs), are fitted using the maintenance data provided by a water administrative sector. Second, for the two most common accidents, i.e., leakages and bursts, the accident pipes in the former case are simulated by reduced flow, while the accident pipes as well as the influenced ones in the latter case are isolated completely by closing valves, which are determined by a depth-first search method. Third, two restoration strategies are considered, including plugging and replacement, and the corresponding recovery time by days are given based on the maintenance data. Meanwhile, the aging induced pipe deterioration is modeled in the operation process. Finally, the operational resilience of the WDN in Mianzhu city is evaluated by above framework and six resilience strategies are investigated and compared. Results show that replacing accident pipes with new DIPs once burst occurs improve the operational resilience of WDNs most obviously. In addition, replacing CIPs with DIPs ahead of the design working life or adding valves also help to improve the resilience level. Based on the findings, some feasible and practicable resilience enhancement suggestions are concluded to provide guidance for local decision makers.

关键词： Lifecycle Operational resilience Leakages Bursts Water distribution networks Resilience enhancement

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