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RGB-based combustion state classification and stability analysis of SOFC afterburner: effects of fuel utilization and steam-to-carbon ratio

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Fuel 2025年 402卷

作者： Jingjing Wang Shuyu Zhang Jingang Lai Jiashu Jin Chun Zou Jung-Sik Kim Zhonghua Deng Yuanwu Xu Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Wuhan 430074 China State Key Laboratory of Coal Combustion Huazhong University of Science and Technology Wuhan 430074 China Sino-French Engineer School/School of General Engineering Beihang University Beijing 100191 China Wuhan Huamao Automation Co. Ltd. Wuhan 430205 China School of Artificial Intelligence and Automation Wuhan University of Science and Technology Wuhan 430081 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518055 China

Solid oxide fuel cell (SOFC) systems efficiently convert chemical energy into electricity but produce high-temperature exhaust gases containing unreacted fuels. The afterburner combusts these residual fuels to enhance heat recovery and system efficiency, with its performance influenced by stack fuel composition variations. This study investigates how fuel utilization rate ( U f ) and steam-to-carbon ratio (S/C) affect afterburner combustion characteristics in a 1 kW methane-reformed SOFC system. Results showed maximum afterburner temperature decreased from 981.3 K to 835 K as U f increased to 0.6, while S/C minimally impacted temperature distribution. A dimensionless stability index was developed to quantitatively assess combustion performance. An image-based analysis approach extracted five key parameters describing flame characteristics under varying conditions. These parameters supported the development of a three-tier hierarchical classification framework to identify weak, stable, and unstable combustion states, achieving an overall recognition accuracy of 74.5 %. These findings provide insights into SOFC operating parameters, residual stack fuel compositions, and afterburner stability relationships. The proposed assessment methodology offers guidance for optimizing thermal management and improving energy recovery efficiency in SOFC systems.

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DAmageNet: A universal adversarial dataset

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arXiv 2019年

作者： Chen, Sizhe Huang, Xiaolin He, Zhengbao Sun, Chengjin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing 800 Dongchuan Road Shanghai200240 China

It is now well known that deep neural networks (DNNs) are vulnerable to adversarial attack. Adversarial samples are similar to the clean ones, but are able to cheat the attacked DNN to produce incorrect predictions in high confidence. But most of the existing adversarial attacks have high success rate only when the information of the attacked DNN is well-known or could be estimated by massive queries. A promising way is to generate adversarial samples with high transferability. By this way, we generate 96020 transferable adversarial samples from original ones in imageNet. The average difference, measured by root means squared deviation, is only around 3.8 on average. However, the adversarial samples are misclassified by various models with an error rate up to 90%. Since the images are generated independently with the attacked DNNs, this is essentially zero-query adversarial attack. We call the dataset DAmageNet, which is the first universal adversarial dataset that beats many models trained in imageNet. By finding the drawbacks, DAmageNet could serve as a benchmark to study and improve robustness of DNNs. DAmageNet could be downloaded in http://***/Show/56/122 Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

Fault Diagnosis of SOFC Stack Based on Neural Network Algorithm

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Energy Procedia 2019年 158卷 1798-1803页

作者： Tao Xue Xiaolong Wu Yuanwu Xu Suwen Jing Zehua Li Jianhua Jiang Zhonghua Deng Xiaowei Fu Li Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan 430074 China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan 430065 China

To realize the commercialization of SOFC, it must be ensured that it can work efficiently and stably. SOFC fault diagnosis becomes an essential part of the research. Due to the strong coupling of faults in the stack, this paper uses neural network algorithm to detect and diagnose faults. The simulation results verify that through the diagnosis of the test sample, the recognition rate of the test sample by the network is found to be 95%, which explains the neural network fault diagnosis model established in this paper on identifying the normal working state of the stack, the electrode stacking of the stack, and the gas leakage fault of the stack has good effectiveness and accuracy.

关键词： SOFC fault diagnosis neural network

Super Twisting control of Passive Gait Training Exoskeleton Driven by Pneumatic Muscles

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Super Twisting Control of Passive Gait Training Exoskeleton ...

International Symposium on Micromechatronics and Human Science (MHS)

作者： Mengshi Zhang Jian Huang Yu Cao Hai-Tao Zhang Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728126753

ISBN: (纸本)9781728126760

Pneumatic Muscles (PMs) - driven exoskeleton has a promising prospect in the field of rehabilitation and assistance, because of the PMs intrinsic features of compliance and high force-to-weight ratio. However, the precise control of PMs-driven exoskeleton still remains a challenging problem due to the hysteresis, time-varying parameters of PMs and complexity of the mechanism. To solve this problem, this paper presents a super twisting controller (STC), essentially a second-order sliding mode control, to realize the human gait tracking control of lower limb exoskeleton. As a result, the disturbances and uncertainties of the system can be handled, and the chattering caused by the traditional sliding mode control (SMC) could be eliminated. The stability of the closed-loop system is ensured according to the Lyapunov theorem. In addition, we conduct experiments in real exoskeleton system and the results illustrate the validity of the super twisting algorithm..

关键词： Uncertainty Heuristic algorithms Exoskeletons Muscles Stability analysis Closed loop systems Sliding mode control

Metric Learning Based Rolling Bearing Faults Diagnosis with Curvelet Transform

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Metric Learning Based Rolling Bearing Faults Diagnosis with ...

2019 3rd International Conference on Machine Vision and Information Technology (CMVIT 2019)

作者： Ziming Lu Jiang-Wen Xiao Zhengyi Huang School of Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control(Huazhong University of Science and Technology) Ministry of Education

Rolling bearing faults are among the primary causes of breakdown in mechanical equipment. Aiming at the vibration signals of rolling bearing which are non-stationary and easy to be disturbed by noise, a novel fault diagnosis method based on curvelet transform and metric learning is proposed. This method consists of 3 parts. The first one is feature engineering which includes reshaping the original timing features of rolling bearings, employing curvelet transform to transform reshaped features and making its coefficients as the new features. Curvelet transform can analyse the original signal from many angles. The second one is employing metric learning to map these new features into special embedding space. The last one is applying KNN classifier to detect the rolling bearing faults. Metric learning can effectively improve the performance of KNN by learning a mapping matrix to modify the distribution of samples. The proposed method overcomes the problems such as the subjectivity and blindness of manual feature extraction, poor coupling in each stage and sensitive to the effect of noise. Extensive simulations based on several data-sets show that the our method has better performance on bearing fault diagnosis than traditional methods.

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Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach

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arXiv 2019年

作者： He, He Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

A brain-computer interface (BCI) system usually needs a long calibration session for each new subject/task to adjust its parameters, which impedes its transition from the laboratory to real-world applications. Domain adaptation, which leverages labeled data from auxiliary subjects/tasks (source domains), has demonstrated its effectiveness in reducing such calibration effort. Currently, most domain adaptation approaches require the source domains to have the same feature space and label space as the target domain, which limits their applications, as the auxiliary data may have different feature spaces and/or different label spaces. This paper considers different set domain adaptation for BCIs, i.e., the source and target domains have different label spaces. We introduce a practical setting of different label sets for BCIs, and propose a novel label alignment (LA) approach to align the source label space with the target label space. It has three desirable properties: 1) LA only needs as few as one labeled sample from each class of the target subject;2) LA can be used as a preprocessing step before different feature extraction and classification algorithms;and, 3) LA can be integrated with other domain adaptation approaches to achieve even better performance. Experiments on two motor imagery datasets demonstrated the effectiveness of LA. Copyright © 2019, The Authors. All rights reserved.

关键词： Brain computer interface

Optimize TSK fuzzy systems for regression problems: Mini-batch gradient descent with regularization, droprule, and adabound (MBGD-RDA)

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arXiv 2019年

作者： Wu, Dongrui Yuan, Ye Huang, Jian Tan, Yihua Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Takagi-Sugeno-Kang (TSK) fuzzy systems are very useful machine learning models for regression problems. However, to our knowledge, there has not existed an efficient and effective training algorithm that ensures their generalization performance, and also enables them to deal with big data. Inspired by the connections between TSK fuzzy systems and neural networks, we extend three powerful neural network optimization techniques, i.e., mini-batch gradient descent (MBGD), regularization, and AdaBound, to TSK fuzzy systems, and also propose three novel techniques (DropRule, DropMF, and Drop-Membership) specifically for training TSK fuzzy systems. Our final algorithm, MBGD with regularization, DropRule and AdaBound (MBGD-RDA), can achieve fast convergence in training TSK fuzzy systems, and also superior generalization performance in testing. It can be used for training TSK fuzzy systems on datasets of any size;however, it is particularly useful for big datasets, on which currently no other efficient training algorithms exist. Copyright © 2019, The Authors. All rights reserved.

关键词： Gradient methods

Optimize TSK fuzzy systems for big data classification problems: Bag of tricks

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arXiv 2019年

作者： Cui, Yuqi Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Takagi-Sugeno-Kang (TSK) fuzzy systems are flexible and interpretable machine learning models;however, they may not be easily applicable to big data problems, especially when the size and the dimensionality of the data are both large. This paper proposes a mini-batch gradient descent (MBGD) based algorithm to efficiently and effectively train TSK fuzzy systems for big data classification problems. It integrates three novel techniques: 1) uniform regularization (UR), which is a regularization term added to the loss function to make sure the rules have similar average firing levels, and hence better generalization performance;2) random percentile initialization (RPI), which initializes the membership function parameters efficiently and reliably;and, 3) batch normalization (BN), which extends BN from deep neural networks to TSK fuzzy systems to speedup the convergence and improve generalization. Experiments on nine datasets from various application domains, with varying size and feature dimensionality, demonstrated that each of UR, RPI and BN has its own unique advantages, and integrating all three together can achieve the best classification performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Gradient methods

A Model Reference Adaptive controller With Feedforward Decoupling for Active Magnetic Bearing Rotor System

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A Model Reference Adaptive Controller With Feedforward Decou...

IEEE Advanced Information Technology, Electronic and Automation control Conference (IAEAC)

作者： Jian Yi Gui-Ping Ren Hai-Tao Zhang Yue Wu School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan P.R. China

Active magnetic bearing (AMB) rotor system is widely applied in the industry for its remarkable advantages. However, it is a typical open-loop unstable mechatronics system suffering from nonlinear and couple characteristic. In this paper, the feedforward decoupling method is used to deal with the couple relationship of different axes. And the model reference adaptive control (MRAC) method is applied to improve the control performance and robustness and overcome the uncertainties. This control method makes the system output reach to the reference model by the designed adaptive law. Simulation results illustrate the effectiveness of the proposed scheme.

关键词： Rotors Couplings Adaptation models Magnetic levitation Feedforward systems Adaptive systems Uncertainty