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39th Chinese control Conference (CCC)

作者： Zhang, Ying Sun, Shu Lian, Cheng Wang, Xiaoping Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Wuhan Univ Technol Sch Automat Wuhan 430074 Peoples R China

ISBN: (纸本)9789881563903

In this paper, a new control system based on the improved PM 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： PID neural network Fuzzy PID controller Landslide control Stability evaluation

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A Comprehensive Review of Thermal Management in Solid Oxide Fuel Cells: Focus on Burners, Heat Exchangers, and Strategies

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ENERGIES 2024年第5期17卷 1005页

作者： Li, Mingfei Wang, Jingjing Chen, Zhengpeng Qian, Xiuyang Sun, Chuanqi Gan, Di Xiong, Kai Rao, Mumin Chen, Chuangting Li, Xi Guangdong Energy Grp Sci & Technol Res Inst Co Ltd Guangzhou 511466 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Minist Wuhan 430074 Peoples R China Guangdong Huizhou Lng Power Co Ltd Huizhou 516081 Peoples R China Guangdong Energy Grp Co Ltd Guangzhou 510630 Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518055 Peoples R China

Solid Oxide Fuel Cells (SOFCs) are emerging as a leading solution in sustainable power generation, boasting high power-to-energy density and minimal emissions. With efficiencies potentially exceeding 60% for electricity generation alone and up to 85% when in cogeneration applications, SOFCs significantly outperform traditional combustion-based technologies, which typically achieve efficiencies of around 35-40%. Operating effectively at elevated temperatures (600 degrees C to 1000 degrees C), SOFCs not only offer superior efficiency but also generate high-grade waste heat, making them ideal for cogeneration applications. However, these high operational temperatures pose significant thermal management challenges, necessitating innovative solutions to maintain system stability and longevity. This review aims to address these challenges by offering an exhaustive analysis of the latest advancements in SOFC thermal management. We begin by contextualizing the significance of thermal management in SOFC performance, focusing on its role in enhancing operational stability and minimizing thermal stresses. The core of this review delves into various thermal management subsystems such as afterburners, heat exchangers, and advanced thermal regulation strategies. A comprehensive examination of the recent literature is presented, highlighting innovations in subsystem design, fuel management, flow channel configuration, heat pipe integration, and efficient waste heat recovery techniques. In conclusion, we provide a forward-looking perspective on the state of research in SOFC thermal management, identifying potential avenues for future advancements and their implications for the broader field of sustainable energy technologies.

关键词： solid oxide fuel cells thermal management combustors heat exchanger thermal management strategy

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Switching the activity of Taq polymerase using clamp-like triplex aptamer structure

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NUCLEIC ACIDS RESEARCH 2020年第15期48卷 8591-8600页

作者： Hu, Yingxin Wang, Zhiyu Chen, Zhekun Pan, Linqiang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Educ Minist China Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China Shijiazhuang Tiedao Univ Coll Informat Sci & Technol Shijiazhuang 050043 Hebei Peoples R China

In nature, allostery is the principal approach for regulating cellular processes and pathways. Inspired by nature, structure-switching aptamer-based nanodevices are widely used in artificial biotechnologies. However, the canonical aptamer structures in the nanodevices usually adopt a duplex form, which limits the flexibility and controllability. Here, a new regulating strategy based on a clamp-like triplex aptamer structure (CLTAS) was proposed for switching DNA polymerase activity via conformational changes. It was demonstrated that the polymerase activity could be regulated by either adjusting structure parameters or dynamic reactions including strand displacement or enzymatic digestion. Compared with the duplex aptamer structure, the CLTAS possesses programmability, excellent affinity and high discrimination efficiency. The CLTAS was successfully applied to distinguish single-base mismatches. The strategy expands the application scope of triplex structures and shows potential in biosensing and programmable nanomachines.

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Zero-shot Text-driven Physically Interpretable Face Editing

arXiv

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arXiv 2023年

作者： Meng, Yapeng Yang, Songru Hu, Xu Zhao, Rui Li, Lincheng Shi, Zhenwei Zou, Zhengxia Image Processing Center School of Astronautics Beihang University Beijing100191 China Beijing Key Laboratory of Digital Media Beihang University Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China Shanghai Artificial Intelligence Laboratory China The Netease Fuxi AI Lab Hangzhou China The Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China

This paper proposes a novel and physically interpretable method for face editing based on arbitrary text prompts. Different from previous GAN-inversion-based face editing methods that manipulate the latent space of GANs, or diffusion-based methods that model image manipulation as a reverse diffusion process, we regard the face editing process as imposing vector flow fields on face images, representing the offset of spatial coordinates and color for each image pixel. Under the above-proposed paradigm, we represent the vector flow field in two ways: 1) explicitly represent the flow vectors with rasterized tensors, and 2) implicitly parameterize the flow vectors as continuous, smooth, and resolution-agnostic neural fields, by leveraging the recent advances of implicit neural representations. The flow vectors are iteratively optimized under the guidance of the pre-trained Contrastive Language-image Pretraining (CLIP) model by maximizing the correlation between the edited image and the text prompt. We also propose a learning-based one-shot face editing framework, which is fast and adaptable to any text prompt input. Our method can also be flexibly extended to real-time video face editing. Compared with state-of-the-art text-driven face editing methods, our method can generate physically interpretable face editing results with high identity consistency and image quality. Our code will be made publicly available. © 2023, CC BY-NC-SA.

关键词： Iterative methods

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image Regularizations Based on the Sparsity of Corner Points

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IEEE TRANSACTIONS ON image processing 2019年第1期28卷 72-87页

作者： Liu, Haosen Tan, Shan Huazhong Univ Sci & Technol Sch Automat Minist Educ China Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

Many analysis-based regularizations proposed so far employ a common prior information, i. e., edges in an image are sparse. However, in local edge regions and texture regions, this prior may not hold. As a result, the performance of regularizations based on the edge sparsity may be unsatisfactory in such regions for image-related inverse problems. These regularizations tend to smooth out the edges while eliminating the noise. In other words, these regularizations' abilities of preserving edges are limited. In this paper, a new prior that the corner points in a natural image are sparse was proposed to construct regularizations. Intuitively, even in local edge regions and texture regions, the sparsity of corner points may still exist, and hence, the regularizations based on it can achieve better performance than those based on the edge sparsity. As an example, by utilizing the sparsity of corner points, we proposed a new regularization based on Noble's corner measure function. Our experiments demonstrated the excellent performance of the proposed regularization for both image denoising and deblurring problems, especially in local edge regions and texture regions.

关键词： Corner detection image restoration nonlocal method regularization structure tensor

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Flocking with nonlinear systems based on distributed observers

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NEUROCOMPUTING 2025年 619卷

作者： Chi, Jun An, Qing Wang, Xiaoling Su, Housheng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Luoyu Rd 1037 Wuhan 430074 Hubei Peoples R China Wuchang Univ Technol Articial Intelligence Sch 16 Jiangxia Ave Wuhan 430223 Hubei Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Wenyuan Rd 9 Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Wenyuan Rd 9 Nanjing 210023 Jiangsu Peoples R China

This paper investigates the output feedback-based flocking control problem for nonlinear dynamical systems (NDSs). To address this, distributed observers and consensus-based leader-following control algorithms are incorporated into each sensor in the network. By combining state estimation with flocking control algorithms, the observed state information of the NDS serves as the virtual leader for each sensor. To handle scenarios involving obstacles, we propose enclosing obstacles of various shapes within circles and developing a flocking control algorithm using artificial potential functions. Simulation results validate the effectiveness of the proposed algorithm.

关键词： Nonlinear observers Flocking Mobile sensor network Output feedback

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Causal Invariant Hierarchical Molecular Representation for Out-of-distribution Molecular Property Prediction

Causal Invariant Hierarchical Molecular Representation for O...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinlong Wen Yifei Guo Shuoying Wei Wenhan Long Lida Zhu Rongbo Zhu Huazhong Agricultural University Wuhan China College of Informatics Huazhong Agricultural University Wuhan China Shenzhen Institute of Nutrition and Health Huazhong Agricultural University Shenzhen China Shenzhen Branch Guangdong Laboratory for Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture and Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan China Huazhong University of Science and Technology Wuhan China Key Laboratory of 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)9798350386226

ISBN: (纸本)9798350386233

Molecular representation learning is widely used in the field of drug discovery, due to its ability to accurately capture the complex features of compounds in high-dimensional space. However, existing molecular representation learning models are prone to be influenced by spurious parts during distribution shifts (also known as out-of-distribution, or OOD), which results in models mistakenly treating these spurious parts as crucial features of molecules, thereby limiting the generalization capability of the models. To tackle this issue, a novel invariant molecular representation learning model, called Causal Invariant Hierarchical Molecular Representation Graph Neural Networks (CHiMoGNN), is proposed for OOD molecular property prediction. In CHiMoGNN, a Feature Enhancement (FE) module is designed to leverage the multi-level molecular parts to enhance the expression of invariant features, thereby enhancing the model’s capability to capture key molecular information. In addition, a Cartesian Product based Environmental Impact (EI) module is adopted to generate counterfactual samples with environmental diversity. Consequently, these samples are utilized to train a classifier that maintains consistent performance across various environments. Extensive experiments on seven real-world datasets demonstrate that CHiMoGNN outperforms 9 state-of-the-art models, achieving a 5.73% increase in average ROC-AUC, and the results also show that CHiMoGNN can effectively maintain generalization in various distribution shifts. Code and datasets are available at https://***/Chertuion/CHiMoGNN.

关键词： Representation learning Limiting Codes Predictive models Feature extraction Iron Graph neural networks Drug discovery Data mining Compounds

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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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第三十九届中国控制会议

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

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 kp, ki and kd. Then the obtained parameters are taken as the initial weights of *** 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： PID neural network Fuzzy PID controller Landslide control Stability evaluation

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Deep neural learning techniques with long short-term memory for gesture recognition

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NEURAL COMPUTING & APPLICATIONS 2020年第20期32卷 16073-16089页

作者： Jain, Deepak Kumar Mahanti, Aniket Shamsolmoali, Pourya Manikandan, Ramachandran Chongqing Univ Posts & Telecommun Key Lab Intelligent Air Ground Cooperat Control U Coll Automat Chongqing Peoples R China Univ Auckland Auckland New Zealand Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai Peoples R China SASTRA Deemed Univ Sch Comp Thanjavur India

Gesture recognition is a kind of biometric which has assumed great significance in the field of computer vision for communicating information through human activities. To recognize the various gestures and achieve efficient classification, an efficient computational machine learning technique is required. The Shift Invariant Convolutional Deep Structured Neural Learning with Long Short-Term Memory (SICDSNL-LSTM) and Bivariate Fully Recurrent Deep Neural Network with Long Short-Term Memory (BFRDNN-LSTM) have been introduced for classifying human activities with efficient accuracy and minimal time complexity. The SICDSNL-LSTM technique collects gesture data (a kind of biometric) from the dataset and gives it to the input layers of Shift Invariant Convolutional Deep Structured Neural Learning. The SICDSNL-LSTM technique uses two hidden layers for performing regression and classification. In the regression process, dice similarity is used for measuring the relationship between data and output classes. In the second process, the input data are classified into dissimilar classes for each time step using LSTM unit with soft-step activation function. The soft-step activation function uses 'forget gate' for removing the less significant data from the cell state. This is also used to make a decision to display the output at a particular time step and to remove other class results. Then, LSTM output is given to the output layers, and convolutional deep neural learning is performed to classify the gesture. Based on the classification results, human activity and gesture are recognized with high accuracy. The BFRDNN-LSTM technique also performs regression in the first hidden layers using bivariate correlation to find relationship between data and classes. The LSTM unit in BFRDNN-LSTM technique uses Gaussian activation function in the second hidden layers for categorizing incoming data into various classes at each time step. In this proposed BFRDNN-LSTM method, fully recurrent dee

关键词： Gesture recognition Shift invariant Convolutional deep structured Neural learning Long short-term memory Bivariate fully recurrent deep neural network

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Construction path tracking and pose estimation of unmanned bulldozer

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AUTOMATION IN CONSTRUCTION 2023年第1期154卷

作者： Peng, Gang Duan, Hangqi Tan, Zejie Zhou, Yicheng Li, Jianfeng Hu, Bin Zhou, Cheng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Hubei Peoples R China Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Shantui Construction Machinery Co Ltd Jining Shandong Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Natl Ctr Technol Innovat Digital Construction Wuhan Hubei Peoples R China

The vigorous development of automatic control technology and AI has generated considerable interest in driverless systems. Presently, research on driverless systems is mainly focused on the automotive field, but it also has immense scope in engineering machinery. In the field of unmanned bulldozers, path tracking and pose estimation are the foundation of construction quality and safety. This article focuses on this field. A construction path tracking method was designed, wherein motion control commands were generated according to the course angle deviation combined with verification of steering gain and saturation characteristics. The proposed method can track the construction path of an unmanned bulldozer with high accuracy. The improved ORB-SLAM3 pose estimation method fused with GNSS and the local map reloading pose estimation method are used to estimate the bulldozer pose to ensure construction safety. The two methods are superior to the general SLAM system in terms of precision and dynamic performance.

关键词： Construction path tracking Pose estimation Slam Multi-sensor fusion Local map reloading

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