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Interior Attention-Aware Network for Infrared Small Target Detection

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Wang, Kewei Du, Shuaiyuan Liu, Chengxin Cao, Zhiguo 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

Infrared small target detection plays an important role in target warning, ground monitoring, and flight guidance. Existing methods typically utilize local-contrast information of each pixel to detect infrared small targets, neglecting the interior relation between target pixels or background pixels. The mere use of the local information of one pixel, however, is not sufficient for accurate detection, which may lead to missing detection and false alarms. As a harmonious whole, information between pixels are necessary to determine if a pixel belongs to the target or the background. Motivated by the fact that pixels from targets or backgrounds are correlated with each other, we propose a coarse-to-fine interior attention-aware network (IAANet) for infrared small target detection. Specifically, a region proposal network (RPN) is first applied to obtain coarse target regions and filter out backgrounds. Then, we leverage a transformer encoder to model the attention between pixels in coarse target regions, outputting attention-aware features. Finally, predictions are obtained by feeding attention-aware features to a classification head. Extensive experiments show that our approach is capable of detecting targets precisely, of suppressing a variety of false alarm sources, and works effectively in various background environments and target appearances. We show that our IAANet outperforms the state-of-the-art methods by a large margin. Code will be made available at: https://***/kwwcv/iaanet.

关键词： Semantics Object detection Visualization Feature extraction Convolutional neural networks Proposals Clutter Attention-aware coarse-to-fine infrared small target detection

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Time-Weighted Kernel-Sparse-Representation-Based Real-Time Nonlinear Multimode Process Monitoring

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第4期18卷 2411-2421页

作者： Wang, Yang Zheng, Ying Wang, Zhaojing Yang, Weidong Huazhong Univ Sci & Technol Key Lab Educ Minist Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Real-time nonlinear multimode process monitoring of actual industrial systems has attracted increasing attention recently. In this article, the time-weighed kernel sparse representation (TWKSR) method is proposed to partition the mode of the training dataset by introducing the time-series-dependent characteristics into the kernel sparse representation algorithm. The alternating direction method of multipliers is utilized to solve the optimization problem of the proposed TWKSR method. Then, the representative samples from each identified mode are selected to update the dictionary matrix. Based on the updated dictionary matrix, the sparse coefficient is used for online mode identification, and the reconstruction error is utilized for fault detection. Finally, a numerical simulation case and the wastewater treatment process example verify the effectiveness of the proposed method.

关键词： Kernel Dictionaries Sparse matrices Process monitoring Fault detection Adaptation models Optimization Alternating direction method of multipliers (ADMM) fault detection nonlinear multimode processes sparse representation (SR)

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Hierarchical Spatio-temporal Decoupling for Text-to- Video Generation

Hierarchical Spatio-temporal Decoupling for Text-to- Video G...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhiwu Qing Shiwei Zhang Jiayu Wang Xiang Wang Yujie Wei Yingya Zhang Changxin Gao Nong Sang Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Alibaba Group Fudan University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Despite diffusion models having shown powerful abilities to generate photorealistic images, generating videos that are realistic and diverse still remains in its infancy. One of the key reasons is that current methods intertwine spatial content and temporal dynamics together, leading to a notably increased complexity of text-to-video generation (T2V). In this work, we propose HiGen, a diffusion model-based method that improves performance by decoupling the spatial and temporal factors of videos from two perspectives, i.e., structure level and content level. At the structure level, we decompose the T2V task into two steps, including spatial reasoning and temporal reasoning, using a unified denoiser. Specifically, we generate spatially coherent priors using text during spatial reasoning and then generate temporally coherent motions from these priors during temporal reasoning. At the content level, we extract two subtle cues from the content of the input video that can express motion and appearance changes, respectively. These two cues then guide the model's training for generating videos, enabling flexible content variations and enhancing temporal stability. Through the decoupled paradigm, HiGen can effectively reduce the complexity of this task and generate realistic videos with semantics accuracy and motion stability. Extensive experiments demonstrate the superior performance of HiGen over the state-of-the-art T2V methods. We have released our source code and models.

关键词： Training Source coding Semantics Spatial coherence Cognition Stability analysis Complexity theory

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Deep learning with multiple scale attention and direction regularization for asset price prediction

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 186卷 115796-115796页

作者： Xu, Fucui Tan, Shan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Forecasting the stock price is a challenging task due to its complex dynamic behaviors, affected by long-term trends, seasonal changes, cyclical changes, and irregular changes. Although many deep learning techniques have been applied to stock price forecasting, few of them have a deep insight into these complex behaviors. In this work, we propose a four-step hybrid model, named ESTA-Net, to adaptively extract these behavior patterns for stock price forecasting. Firstly, the empirical mode decomposition is applied to decompose a closing price sequence into intrinsic mode functions (IMFs). The goal of this step is to extract multiple quasi-stationary features of different time scales from the historical closing price sequence. Secondly, each IMF is modeled and forecasted by a temporal attention long short term memory (TALSTM) network. The TALSTM network is designed to capture the long term dependency of each IMF. Thirdly, the learned deep representations of IMFs are fed into a scale attention network (SANet), which adaptively selects relevant deep representations of multiple time scale features extracted from the historical price sequence. Finally, these learned deep features are fed into a fully connected layer to predict the future closing price. In addition, to make the proposed model learn the movement direction of the closing price, we propose a novel regularization term, i.e. the direction regularization term, to train the proposed model. This regularization term measures the inconsistency between the predicted movement direction and the actual movement direction of the closing price. Experiments show that the proposed model significantly outperforms benchmark models. Particularly, on seven financial market indices, the proposed model with the direction regularization term achieves the highest POCID (32.04% higher than that of CNN) and the lowest MAPE (37.36% lower than that of DA-RNN).

关键词： Asset price prediction Deep representations Scale attention mechanism Direction priori information

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Research on Fault Diagnosis of Solid Oxide Fuel Cell Based on Improved Random Forest Algorithm 43

Research on Fault Diagnosis of Solid Oxide Fuel Cell Based o...

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43rd Chinese control Conference (CCC)

作者： Zhu, Kanglin Xiang, Hongfu Xiang, Guohui Wu, Han Li, Xi Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Min Wuhan 430074 Hubei Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518055 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

Solid oxide fuel cell (SOFC) is a new energy power supply device that has been highly popular in recent years. It has the advantages of high efficiency, quietness, and no pollution. However, due to the long-term operation of the SOFC system in a high-temperature environment, various faults are inevitable, which will affect the normal operation of the system. Therefore, this paper mainly focuses on the rupture fault of the heat exchanger and the hydrogen shortage fault that occur during the operation of the SOFC. A novel method (PCA-RF) for SOFC system fault diagnosis is established by combining principal component analysis and random forest algorithm. This method not only has a simple process but also has high accuracy. Therefore, this algorithm can well help researchers accurately identify whether there is a fault in the actual use scenario of the SOFC, so as to reduce or recover unnecessary losses.

关键词： SOFC Data-driven PCA RF Fault diagnosis

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Decoupling of nitrogen and oxygen sensors based on particle swarm optimization and fuzzy feedforward compensation 43

Decoupling of nitrogen and oxygen sensors based on particle ...

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43rd Chinese control Conference (CCC)

作者： Sun, Can Wang, Zhen Wang, Jie Qiao, Yu Li, Xi Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Min Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518055 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

A nitrogen oxide sensor that can accurately and quickly detect the content of nitrogen oxides in exhaust gas plays a key role in the treatment of automotive exhaust gas. Due to the characteristics of gas diffusion and chamber structure, nitrogen oxide sensors are difficult to achieve high-precision and fast measurement due to strong coupling. In order to solve this problem, this paper proposes a decoupling control strategy for nitrogen oxide sensors. On the one hand, the strategy uses fuzzy feedforward compensation, and on the other hand, it uses PID control based on particle swarm optimization to achieve control. This not only eliminates the coupling between loops, but also improves the control accuracy. The analysis shows that the design of the decoupling control strategy for nitrogen oxide sensors proposed in this paper is feasible, which allows the nitrogen oxide sensors to work in a stable and reliable state.

关键词： Nitrogen-oxygen sensor Coupling Adaptive PID Fuzzy feedforward decoupling

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Lifetime Extension control for Multi-Stack SOFC System with Inconsistent Stacks 42

Lifetime Extension Control for Multi-Stack SOFC System with ...

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42nd Chinese control Conference, CCC 2023

作者： Li, Zexin Liu, Guoqiang Zhao, Wei Li, Xi School of Artificial Intelligence and Automation Huazhong University of Science & Technology Key Laboratory of Image Processing and Intelligent Control of Education Ministry Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen518055 China

ISBN: (纸本)9789887581543

The multi-stack fuel cell system (MFCS) is an important basis for the application of solid oxide fuel cell (SOFC) technology to large-scale power generation systems. MFCS can provide higher power, longer lifetime and better reliability. Different from the single-stack fuel cell system (SFCS), there may be some inconsistencies among the stacks in the MFCS. If the inconsistencies are ignored, they will significantly affect the lifetime of the MFCS. This study focuses on the lifetime extension of multi-stack SOFC power generation systems with inconsistent stacks. Since the multi-stack system of SOFC generally cannot control the flow rate of each stack respectively, it brings a difficulty to the control of the MFCS with inconsistencies among the stacks. In order to overcome this difficulty, this paper built a multi-stack SOFC system model, which can characterize the degradation and inconsistencies among SOFC stacks. And then designed a power distribution strategy based on fuzzy control to extend the lifetime of the MFCS. The strategy was verified by simulations. The simulation results show that the strategy can effectively control the inconsistencies among the stacks and significantly extend the lifetime of the MFCS. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Fuzzy control

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Viewpoint Transform Matching model for person re-identification

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NEUROCOMPUTING 2021年 433卷 19-27页

作者： Zheng, Ruochen Gao, Changxin Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Person re-identification is a challenging problem that aims at matching persons across multiple non overlapping cameras. Previous works on person re-identification mainly focus on solving the problems caused by pose variations, backgrounds, and occlusion while ignoring the viewpoint variations. To address the viewpoint problem, we propose a Viewpoint Transform Matching (VTM) model, which reduces the inference of viewpoints by feature-level viewpoint transformation. Overall, we design a framework by using viewpoint specific branches to separately extract the representation of different viewpoints. Specifically, we first select the branch for an input image, according to its viewpoint information. In the branch, we transform the feature to other viewpoints by establishing a mutually transformable connection between the features of different viewpoints. To suppress the interference among viewpoints, we propose a viewpoint transform classifier module to independently train a classifier for each viewpoint. To improve the effectiveness of the transform, we propose a viewpoint transform loss to guarantee the consistency of the original features and the transformed features. Experiments conducted on Market-1501, DukeMTMC-reID and CUHK03 show the effectiveness of the proposed method. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Person re-identification Viewpoint Transform Matching Deep learning

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Latent Distribution-Based 3D Hand Pose Estimation From Monocular RGB images

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第12期31卷 4883-4894页

作者： Li, Moran Wang, Jialong Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

In this article, we propose a novel compressed latent distribution representation for 3D hand pose estimation from monocular RGB images to alleviate the channel correspondence problem. The channel correspondence problem occurs when the 2D and depth coordinates are estimated from independent feature maps, which means the 2D and depth channel sequences may not match during the cross-dataset inference. In contrast, we propose a compressed latent distribution representation that the 2D and depth feature maps for each joint are interconnected and inter-constrained more directly, effectively alleviating the channel correspondence problem and improving cross-dataset performance. Moreover, we design an efficient encoder-decoder network that can maintain the resolution of feature maps to enable better hand feature extraction from monocular RGB images. In this work, the overall pipeline contains two branches: one is the 2D hand pose estimation branch based on a latent heatmap representation (LHR);the other is the 3D hand pose estimation branch based on our proposed latent distribution representation (LDR). In this way, the 2D estimation branch serves as guidance for the 3D branch, which simplifies the optimization of the overall network and results in a more rapid convergence during training. The results on several benchmark datasets (including STB, RHD, and the most recently released InterHand2.6M) demonstrate that our proposed method achieves state-of-the-art (SOTA) performance.

关键词： Three-dimensional displays Pose estimation Two dimensional displays Heating systems Cameras Neural networks Feature extraction 3D hand pose estimation latent representation

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Sampled-Data Output Feedback control for Nonlinear Uncertain Systems Using Predictor-Based Continuous-Discrete Observer

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第11期34卷 9223-9233页

作者： Sun, Jiankun Yang, Jun Zeng, Zhigang Wang, Huiming Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Loughborough Univ Dept Aeronaut & Automot Engn Loughborough LE11 3TU Leics England Chongqing Univ Pasts & Telecommun Chongqing Key Lab Complex Syst & Bion Control Chongqing 400065 Peoples R China

In this article, we investigate the problem of sampled-data robust output feedback control for a class of nonlinear uncertain systems with time-varying disturbance and measurement delay based on continuous-discrete observer. An augmented system that includes the nonlinear uncertain system and disturbance model is first found, and by using the delayed sampled-data output, we then propose a novel predictor-based continuous-discrete observer to estimate the unknown state and disturbance information. After that, in order to attenuate the undesirable influences of nonlinear uncertainties and disturbance, a sampled-data robust output feedback controller is developed based on disturbance/uncertainty estimation and attenuation technique. It shows that under the proposed control method, the states of overall hybrid nonlinear system can converge to a bounded region centered at the origin. The main benefit of the proposed control method is that in the presence of measurement delay, the influences of time-varying disturbance and nonlinear uncertainties can be effectively attenuated with the help of feedback domination method and prediction technique. Finally, the effectiveness of the proposed control method is demonstrated via the simulation results of a numerical example and a practical example.

关键词： Nonlinear systems Delays Uncertainty Observers control systems Output feedback Time-varying systems Disturbance uncertainty estimation and attenuation (DUEA) measurement delay nonlinear uncertain system predictor-based continuous-discrete observer

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