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A Real-Time Load Prediction control for Fuel Cell Hybrid Vehicle

ENERGIES 2022年第10期15卷 3700页

作者： Fu, Jun Zeng, Linghong Lei, Jingzhi Deng, Zhonghua Fu, Xiaowei Li, Xi Wang, Yan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Minist Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Shenzhen Res Inst Shenzhen 518055 Peoples R China Jingchu Univ Technol Sch Elect & Informat Engn Jingmen 448000 Peoples R China

The development of hydrogen energy is an effective solution to the energy and environmental crisis. Hydrogen fuel cells and energy storage cells as hybrid power have broad application prospects in the field of vehicle power. Energy management strategies are key technologies for fuel cell hybrid systems. The traditional optimization strategy is generally based on optimization under the global operating conditions. The purpose of this project is to develop a power allocation optimization method based on real-time load forecasting for fuel cell/lithium battery hybrid electric vehicles, which does not depend on specific working conditions or causal control methods. This paper presents an energy-management algorithm based on real-time load forecasting using GRU neural networks to predict load requirements in the short time domain, and then the local optimization problem for each predictive domain is solved using a method based on Pontryagin's minimum principle (PMP). The algorithm adopts the idea of model prediction control (MPC) to transform the global optimization problem into a series of local optimization problems. The simulation results show that the proposed strategy can achieve a good fuel-saving control effect. Compared with the rule-based strategy and equivalent hydrogen consumption strategy (ECMS), the fuel consumption is lower under two typical urban conditions. In the 1800 s driving cycle, under WTCL conditions, the fuel consumption under the MPC-PMP strategy is 22.4% lower than that based on the ECMS strategy, and 10.3% lower than the rules-based strategy. Under CTLT conditions, the fuel consumption of the MPC-PMP strategy is 13.12% lower than that of the rule-based strategy, and 3.01% lower than the ECMS strategy.

关键词： hybrid power velocity forecasting GRU neural network MPC PMP

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Landslide evolution state prediction and down-level control based on multi-task learning

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KNOWLEDGE-BASED SYSTEMS 2022年第0期238卷 107884-107884页

作者： Sun, Shu Wang, Xiaoping Li, Junnan Lian, Cheng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat 1037 Luoyu Rd Wuhan Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control 1037 Luoyu Rd Wuhan Peoples R China Wuhan Univ Sci & Technol Sch Automat 122 Luoshi Rd Wuhan Peoples R China

In this paper, multi-task learning is introduced into the study of landslide evolution state prediction and control. Firstly, we define two landslide evolution states and propose a method of landslide evolution state level classification prediction. Specifically, we use Gaussian mixture model (GMM) to reconstruct labeled data sets and establish a landslide evolution state level prediction model based on Multi-Task Learning-Stacked Long-Short Term Memory (MTL-SLSTM), and then use task weight rules to design multi-task losses for network training. The parallel multi-step prediction of the evolution state is achieved by the above works. Secondly, considering the spatial-temporal correlation of different monitoring points on the same landslide, we analyze spatial-temporal data to construct spatial-temporal features, and design a multi-task correlation learning mechanism combined multi-task weight learning and multi-task relationship learning methods to construct spatial-temporal relation. The landslide multi-point prediction model based on Multi-Task Correlation Learning-Stacked Long Short Term Memory (MTCL-SLSTM) achieves single-step prediction of the evolution state of multiple monitoring points with high accuracy. Finally, according to the idea of neural direct inverse model control, we propose down-level control method based on the prediction of the landslide evolution state. We build an interval prediction network based on bootstrap method and model selection strategies, and then a safe rainfall interval predictor is trained offline. Moreover, we design an online landslide down-level control process combined the landslide evolution state level predictor, which realizes single-step control of single-point of the dangerous landslide. Furthermore, the effectiveness of the proposed method is verified on Baishuihe landslide.& nbsp;(c) 2021 Elsevier B.V. All rights reserved.

关键词： Landslide Evolution state prediction Spatial-temporal prediction Multi-task learning Landslide control Down-level control

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Knowledge Distillation for Fast and Accurate Monocular Depth Estimation on Mobile Devices

Knowledge Distillation for Fast and Accurate Monocular Depth...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wang, Yiran Li, Xingyi Shi, Min Xian, Ke Cao, Zhiguo Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China

ISBN: (纸本)9781665448994

Fast and accurate monocular depth estimation on mobile devices is a challenging task as one should always trade off the accuracy against the inference time. Most monocular depth methods adopt models with large computation overhead, which are not applicable on mobile devices. However, directly training a light-weight neural network to estimate depth can yield poor performance. To remedy this, we utilize knowledge distillation, transferring the knowledge and representation ability of a stronger teacher network to a light-weight student network. Experiments on Mobile AI 2021 (MAI2021) dataset demonstrate that our solution helps increase the fidelity of the output depth map and maintain fast inference speed. Specifically, with 94.7% less parameters than teacher network, the si-RMSE of student network only decrease by 10%. Moreover, our method ranks second in the MAI2021 Monocular Depth Estimation Challenge, with a si-RMSE of 0.2602, a RMSE of 3.25, and the inference time is 1197 ms tested on the Raspberry Pi 4.

关键词： Knowledge engineering Performance evaluation Training Visualization Conferences Neural networks Estimation

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Dynamic Color Transform for Wheat Head Detection 18

Dynamic Color Transform for Wheat Head Detection

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18th IEEE/CVF International Conference on Computer Vision (ICCV)

作者： Liu, Chengxin Wang, Kewei Lu, Hao Cao, Zhiguo Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China

ISBN: (纸本)9781665401913

Developing accurate algorithms for wheat head detection is challenging due to the variability of observation circumstances and the uncertainty of wheat head appearances. In this work, we propose a simple but effective idea-dynamic color transform (DCT)-for accurate wheat head detection. This idea is based on an observation that modifying the color channel of an input image can significantly alleviate false negatives and therefore improve detection results. DCT follows a linear color transform and can be easily implemented as a dynamic network. A key property of DCT is that the transform parameters are data-dependent such that illumination variations can be corrected adaptively. The DCT network can be incorporated into any existing object detectors. For example, DCT plays an important role in our solution participating in the Global Wheat Head Detection (GWHD) Challenge 2021, where our solution ranks the first on the initial public leaderboard, with an Average Domain Accuracy (ADA) of 0.821, and obtains the runner-up reward on the final complete testing set, with an ADA of 0.695.

关键词： Head Uncertainty image color analysis Conferences Lighting Transforms Detectors

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Global Convergence Guarantees of (A)GIST for a Family of Nonconvex Sparse Learning Problems

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IEEE Transactions on Cybernetics 2022年第5期52卷 3276-3288页

作者： Zhang, Hengmin Qian, Feng Shang, Fanhua Du, Wenli Qian, Jianjun Yang, Jian East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and Engineering Shanghai200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xi'an710071 China Peng Cheng Laboratory Shenzhen518066 China Nanjing University of Science and Technology Pca Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China

In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the \ell _{0} -norm usually achieve better performance than the convex case (e.g., \ell _{1} -norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Lojasiewica (KL) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones. © 2013 IEEE.

关键词： Optimization

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Coordinated Navigation control of Cross-Domain Unmanned Systems via Guiding Vector Fields

arXiv

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arXiv 2023年

作者： Hu, Bin-Bin Zhang, Hai-Tao Liu, Bin Ding, Jianing Xu, Yifan Luo, Chuanshang Cao, Haosen The School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China Dongguan University of Technology Dongguan523106 China

This paper proposes a distributed guiding-vector-field (DGVF) controller for cross-domain unmanned systems (CDUSs) consisting of heterogeneous unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs), to achieve coordinated navigation whereas maneuvering along their prescribed paths. In particular, the DGVF controller provides a hierarchical architecture of an upper-level heterogeneous guidance velocity controller and a lower-level signal tracking regulator. Therein, the upper-level controller is to govern multiple heterogeneous USVs and UAVs to approach and maneuver along the prescribed paths and coordinate the formation simultaneously, whereas the low-level regulator is to track the corresponding desired guidance signals provided by the upper-level module. Significantly, the heterogeneous coordination among neighboring UAVs and USVs is achieved merely by the lightweight communication of a scalar (i.e., the additional virtual coordinate), which substantially decreases the communication and computational costs. Sufficient conditions assuring asymptotical convergence of the closed-loop system are derived in presence of the exponentially vanishing tracking errors. Finally, real-lake experiments are conducted on a self-established cross-domain heterogeneous platform consisting of three M-100 UAVs, two HUSTER-16 USVs, a HUSTER-12C USV, and a WiFi 5G wireless communication station to verify the effectiveness of the present DGVF controller. Copyright © 2023, The Authors. All rights reserved.

关键词： Air navigation

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Sparse Bayesian Learning Based on Collaborative Neurodynamic Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第12期52卷 13669-13683页

作者： Zhou, Wei Zhang, Hai-Tao Wang, Jun Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China

Regression in a sparse Bayesian learning (SBL) framework is usually formulated as a global optimization problem with a nonconvex objective function and solved in a majorization-minimization framework where the solution quality and consistency depend heavily on the initial values of the used algorithm. In view of the shortcomings, this article presents an SBL algorithm based on collaborative neurodynamic optimization (CNO) for searching global optimal solutions to the global optimization problem. The CNO system consists of a population of recurrent neural networks (RNNs) where each RNN is convergent to a local optimum to the global optimization problem. Reinitialized repetitively via particle swarm optimization with exchanged local optima information, the RNNs iteratively improve their searching performance until reaching global convergence. The proposed CNO-based SBL algorithm is almost surely convergent to a global optimal solution to the formulated global optimization problem. Two applications with experimental results on sparse signal reconstruction and partial differential equation identification are elaborated to substantiate the superiority and efficacy of the proposed method in terms of solution optimality and consistency.

关键词： Optimization Bayes methods Neurodynamics Recurrent neural networks Mathematical model Linear programming Inference algorithms Collaborative neurodynamic optimization (CNO) global optimization regression sparse Bayesian learning (SBL)

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A Direct-Decoupling Closed-Loop control Method for Roll-to-Roll Web Printing Systems

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2021年第3期18卷 1367-1379页

作者： Zhang, Tao Zheng, Ying Chen, Zhihua Deng, Zhonghua Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Guangzhou Univ Inst Comp Sci & Technol Guangzhou 510006 Peoples R China

The roll-to-roll (R2R) web printing system is a complex coupling system, in which tension fluctuation is caused by upstream register control, thus results in downstream register errors. Therefore, it is indispensable to compensate for the couplings in order to improve the register precision in R2R printing systems. However, existing control methods do not realize complete decoupling due to their indirect calculation of compensations for the register errors. In this article, a mechanical model is set up to represent the direct relationship between the downstream register errors and all their upstream register controls. According to the model, compensation is calculated on the basis of the Lyapunov stability theorem to converge the register errors to zero. Then, a direct-decoupling closed-loop control method with first-order compensation terms, i.e., the direct-decoupling proportional derivative control (DDPD), is proposed to completely compensate for the couplings between all upstream register controls and downstream register errors. In addition, the first-order expression of the compensation makes it easy to implement in industrial applications. An industrial example indicates that the proposed control method eliminates the couplings and maintains the range of register errors within +/- 0.06 mm. Note to Practitioners-This article proposes a control strategy for the roll-to-roll (R2R) printing system, particularly for printing systems with electronic line shafts. Few existing studies have been done in detailedly analyzing the upstream register controls and the downstream register errors. This article establishes a mechanical model of printing registration and gives a systematic analysis of the complete relationship between upstream register controls and downstream register errors and then proposes a control strategy based on the Lyapunov stability analysis. The proposed method can be extended to other similar R2R systems. Simulation and industrial examples show that

关键词： Registers Printing Mathematical model Couplings Computational modeling Lyapunov methods Printing machinery Direct decoupling overcompensation roll-to-roll (R2R) printing system tension fluctuation

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High-resolution magnetic nanoparticle temperature measurement method based on dual-frequency magnetic field excitation

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MEASUREMENT SCIENCE AND TECHNOLOGY 2021年第7期32卷

作者： Wang, Shuai Zhang, Pu Liu, Wenzhong Guo, Silin Zhang, Yapeng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

A new remote method for measuring temperature using magnetic nanoparticles (MNPs) is presented in this paper. The method involves exciting MNPs in a dual-frequency AC magnetic field, and using tunnel magnetoresistance to detect the mixing-frequency magnetization response signals. The effects of the excitation magnetic field amplitude and frequency on the temperature measurement were analyzed. Based on the relationship between the MNPs' magnetization harmonic amplitudes and their temperature, two-harmonic and four-harmonic temperature measurement models were established. The estimated temperature was obtained by the optimized parameter estimation algorithm. The temperature error was found to be less than 0.02 K using the two-harmonic model and less than 0.015 K using the four-harmonic model.

关键词： magnetic nanoparticles dual-frequency AC excitation tunnel magnetoresistance

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Decoupled Two-Stage Crowd Counting and Beyond

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IEEE TRANSACTIONS ON image processing 2021年 30卷 2862-2875页

作者： Cheng, Jian Xiong, Haipeng Cao, Zhiguo Lu, Hao Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

One of appealing approaches to counting dense objects, such as crowd, is density map estimation. Density maps, however, present ambiguous appearance cues in congested scenes, rendering infeasibility in identifying individuals and difficulties in diagnosing errors. Inspired by an observation that counting can be interpreted as a two-stage process, i.e., identifying possible object regions and counting exact object numbers, we introduce a probabilistic intermediate representation termed the probability map that depicts the probability of each pixel being an object. This representation allows us to decouple counting into probability map regression (PMR) and count map regression (CMR). We therefore propose a novel decoupled two-stage counting (D2C) framework that sequentially regresses the probability map and learns a counter conditioned on the probability map. Given the probability map and the count map, a peak point detection algorithm is derived to localize each object with a point under the guidance of local counts. An advantage of D2C is that the counter can be learned reliably with additional synthesized probability maps. This addresses important data deficiency and sample imbalanced problems in counting. Our framework also enables easy diagnoses and analyses of error patterns. For instance, we find that, the counter per se is sufficiently accurate, while the bottleneck appears to be PMR. We further instantiate a network D2CNet in our framework and report state-of-the-art counting and localization performance across 6 crowd counting benchmarks. Since the probability map is a representation independent of visual appearance, D2CNet also exhibits remarkable cross-dataset transferability. Code and pretrained models are made available at: https://***/d2cnet

关键词： Location awareness Probabilistic logic Training Reliability Object recognition Kernel Detection algorithms Crowd counting crowd analysis convolutional neural networks local count models

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