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SDSFusion: A Semantic-Aware Infrared and Visible Image Fusion Network for Degraded Scenes

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2025年 PP卷 PP页

作者： Jun Chen Liling Yang Wei Yu Wenping Gong Zhanchuan Cai Jiayi Ma School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Faculty of Engineering China University of Geosciences Wuhan China School of Computer Science and Engineering Macau University of Science and Technology Taipa Macau China Electronic Information School Wuhan University Wuhan China

A single-modal infrared or visible image offers limited representation in scenes with lighting degradation or extreme weather. We propose a multi-modal fusion framework, named SDSFusion, for all-day and all-weather infrared and visible image fusion. SDSFusion exploits the commonality in image processing to achieve enhancement, fusion, and semantic task interaction in a unified framework guided by semantic awareness and multi-scale features and losses. To address the disparity between infrared and visible images in degraded scenes, we differentiate modal features in a unified fusion model. Unlike existing joint fusion methods, we propose an adversarial generative network that refines the reconstruction of low-light images by embedding fused features. It provides feature-level brightness supplementation and image reconstruction to refine brightness and contrast. Extensive experiments in degraded scenes confirm that our approach is superior to state-of-the-art approaches in visual quality and performance, demonstrating the effectiveness of interaction improvement. The code will be posted at: https://***/Liling-yang/SDSFusion.

关键词： Degradation Image fusion Semantics Training Visualization Meteorology Feature extraction Lighting Image segmentation Brightness

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Short-Term Load Forecasting Based on Graph Convolution and Dendritic Deep Learning

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IEEE Transactions on Network science and engineering 2025年

作者： Zhang, Chunyang Yu, Yang Zhang, Tengfei Song, Keyu Wang, Yirui Gao, Shangce Nanjing University of Posts and Telecommunications College of Automation & College of Artificial Intelligence Nanjing210023 China Ningbo University Faculty of Electrical Engineering and Computer Science Zhejiang Key Laboratory of Mobile Network Application Technology Zhejiang315211 China University of Toyama Faculty of Engineering Toyama-shi930-8555 Japan

Short-term load forecasting (STLF) is a significant task to the planning, operation and control of future power systems. The increasing number of devices connected to the system has led to more complex characteristics and forms of load, which has brought considerable difficulties to the relevant methods in achieving higher load prediction accuracy and reliability. In this regard, this study proposes a deep learning model that combines graph convolutional network (GCN), gated recurrent unit (GRU), and dendritic neural model (DNM) to forecast electric load more accurately. Firstly, the sample load data is constructed into graph data with individual time steps as nodes. A GCN is used to extract the hidden features while allowing a full communication between the time steps feature data. A GRU is then used to capture the time-dependent relationship of the data. Finally, a dendritic layer instead of a fully connected layer is used as the output to integrate data features in depth. Experiments are conducted to verify the validity of the proposed model and compared it with several effective deep learning models, including CNN_LSTM, Transformer and Kolmogorov-Arnold Networks (KAN). The results show a significant improvement in prediction compared to the baseline models, with mean absolute percentage error(MAPE) of 1.62% and 3.98%, coefficient of determination(R2) of 0.983 and 0.928 respectively on two load datasets at different levels of aggregation, nationally and regionally. © 2013 IEEE.

关键词： Convolutional neural networks

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Dual-task Mutual Reinforcing Embedded Joint Video Paragraph Retrieval and Grounding

arXiv

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arXiv 2024年

作者： Wang, Mengzhao Li, Huafeng Zhang, Yafei Li, Jinxing Xie, Minghong Tao, Dapeng The Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China The School of Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China FIST LAB School of Information Science and Engineering Yunnan University Kunming650091 China

Video Paragraph Grounding (VPG) aims to precisely locate the most appropriate moments within a video that are relevant to a given textual paragraph query. However, existing methods typically rely on large-scale annotated temporal labels and assume that the correspondence between videos and paragraphs is known. This is impractical in real-world applications, as constructing temporal labels requires significant labor costs, and the correspondence is often unknown. To address this issue, we propose a Dual-task Mutual Reinforcing Embedded Joint Video Paragraph Retrieval and Grounding method (DMR-JRG). In this method, retrieval and grounding tasks are mutually reinforced rather than being treated as separate issues. DMR-JRG mainly consists of two branches: a retrieval branch and a grounding branch. The retrieval branch uses inter-video contrastive learning to roughly align the global features of paragraphs and videos, reducing modality differences and constructing a coarse-grained feature space to break free from the need for correspondence between paragraphs and videos. Additionally, this coarse-grained feature space further facilitates the grounding branch in extracting fine-grained contextual representations. In the grounding branch, we achieve precise cross-modal matching and grounding by exploring the consistency between local, global, and temporal dimensions of video segments and textual paragraphs. By synergizing these dimensions, we construct a fine-grained feature space for video and textual features, greatly reducing the need for large-scale annotated temporal labels. Meanwhile, we design a grounding reinforcement retrieval module (GRRM) that brings the coarse-grained feature space of the retrieval branch closer to the fine-grained feature space of the grounding branch, thereby reinforcing retrieval branch through grounding branch, and finally achieving mutual reinforcement between tasks. Extensive experiments on three challenging datasets demonstrate the effec

关键词： Wages

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An Integrated Logic Function Decomposition Flow for XOR-Majority Graphs

An Integrated Logic Function Decomposition Flow for XOR-Majo...

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Electronics Design automation (ISEDA), International Symposium of

作者： Chengyu Ma Yong Xiao Yun Shao Zhufei Chu Faculty of Electrical Engineering & Computer Science Ningbo University Ningbo China Giga Design Automation Co. Ltd. Shenzhen China

Logic function decomposition is critical to logic synthesis. In this paper, we propose an integrated logic function decomposition flow, which consists of disjoint support decomposition, exact synthesis, Shannon decomposition, and the recently proposed majority-of-three (MAJ) decomposition. Through this process, the logic function can be decomposed into any directed acyclic graph (DAG). An experimental evaluation shows that our algorithm leads to better XOR-majority graphs (XMGs) compared to state-of-the-art algorithms. Our experiments show that the proposed method achieves an average of 5% and 6% reduction in the lookup tables (LUTs) size/depth product applied to the EPFL arithmetic and random control benchmarks after LUT mapping, respectively.

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Online and Offline Hybrid Teaching Mode Based on Scenario Cases: A Case Study of MySQL Databases 4

Online and Offline Hybrid Teaching Mode Based on Scenario Ca...

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4th International Conference on computer science and Educational Informatization, CSEI 2022

作者： You, Jinguo Wang, Ziwen Zhao, Xiaoxia Kou, Weili Zhang, Yunwei Zhang, Ting Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming China Computer Center Kunming University of Science and Technology Yunnan Kunming China College of Big Data and Intelligent Engineering Southwest Forestry University Yunnan Kunming China School of Information Engineering Jiaozuo University Henan Jiaozuo China

ISBN: (数字)9781839537882

ISBN: (纸本)9781839537882

Teaching mode design is a process to achieve the learning goal. Under the background of information technology, online and offline hybrid teaching mode has become the new form of education, and the closely related teaching mode design plays an essential role in the success of teaching implementation. Scenario case teaching can stimulate students' interest in learning and improve their ability to analyze and solve problems. Taking the MySQL database course as an example, this paper introduces the design and implementation process of online and offline hybrid teaching modes based on scenario cases. The practical results demonstrate that the hybrid teaching mode far outperforms the traditional teaching effect in terms of the aspects of students' basic knowledge of databases, practical operation level, and self-learning ability. Furthermore, this teaching mode has gained popularity in the studying of both our university and league universities. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Design

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Asymmetric Modulated Predictive Current Control for Dual Three-Phase PMSM With Improved Performance

Asymmetric Modulated Predictive Current Control for Dual Thr...

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International Conference on Power Electronics and Motion Control (IPEMC)

作者： Ze Li Xingyu Zheng Jinhui Xia Hangan Liu Xiaonan Gao Jose Rodriguez Liling Wang Zhaoyan Zhang Zhiheng Liu College of Electronic and Information Engineering Hebei University China School of Automation Southeast University China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Faculty of Engineering Universidad San Sebastian Chile

ISBN: (数字)9798350351330

ISBN: (纸本)9798350351347

Dual three-phase PMSM (DTP-PMSM) has gained significant attention benefiting from their exceptional reliability and high torque density. However, DTP-PMSM exhibits poor steady-state performance and large calculation amount with the conventional finite-control-set predictive current control. To address these concerns, an asymmetric modulated predictive current control (AM-PCC) for DTP-PMSM is presented. Therein, the three-phase decomposition and deadbeat control scheme are combined to eliminate the weighting factor and optimize the execution efficiency. An asymmetric modulation strategy is presented and integrated into PCC to reduce the current harmonics and switching frequency. Extensive simulation tests are conducted to demonstrate the effectiveness of the proposed AM-PCC scheme.

关键词： Current control Torque Switching frequency Dynamics Modulation Harmonic analysis Power electronics

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A New Configuration for Waveguide Filters With Parallel Coupling Paths

A New Configuration for Waveguide Filters With Parallel Coup...

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Weijun Wu Zai-Cheng Guo Lei Zhu School of Electrical and Automation Engineering Nanjing Normal University Nanjing China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau SAR China

A new configuration is proposed for filters with parallel coupling paths between the source and load in this paper. The proposed fourth-order waveguide bandpass filter (BPF) with two parallel coupled paths is formally designed through this structure. The filter is working at 6.5 GHz and with 300 MHz bandwidth. Due to the two parallel coupling paths, a pair of transmission zeros (TZs) are generated out of passband. Compared with previous waveguide filters, the coupling paths are connected to the source and load in different sides of input/output waveguide. In this way, the waveguide cavity of the resonator can be increased and the insertion loss of the filter can be greatly reduced. The effectiveness of the fourth-order waveguide bandpass filter is verified by simulation results.

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Zero-sum game-based adaptive optimal tracking control for nonlinear systems with uncertain dynamics and external disturbance

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International Journal of Dynamics and Control 2025年第6期13卷 1-17页

作者： Yan, Lei Lai, Guanyu Liu, Zhi Chen, C. L. Philip School of Intelligent Manufacturing Nanyang Institute of Technology Henan Nanyang473004 China School of Automation Guangdong University of Technology Guangdong Guangzhou510006 China Faculty of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510006 China

This paper addresses the issue of adaptive optimal tracking control of nonlinear systems with uncertain dynamics and external disturbances in a zero-sum game setting. The presence of these uncertainties makes it challenging to develop a controller that can achieve good tracking and optimal performance. To tackle this problem, an adaptive backstepping technique is employed to design the forward controller. This technique transforms the error dynamics into an affine system by utilizing a second-order command filter. Furthermore, within the framework of the zero-sum game, the model identifier is utilized to approximate the unknown dynamics while simultaneously considering the external disturbances. By playing the game, the action network generates an optimal controller that is robust against the worst disturbance. The stability of the closed-loop system is proven using the Lyapunov stability theory. It is demonstrated that all closed-loop signals remain bounded, and the weights of all networks are uniformly ultimately bounded. Moreover, the system is capable of tracking reference signals using the optimized controller. The effectiveness of the proposed approach is confirmed through demonstration examples. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Model reference adaptive control

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Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method

Texture Analysis of Breast US Images Using Morphological Tra...

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Mediterranean Conference on Control and automation (MED)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati The Modelling & Simulation Laboratory Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer.

关键词： Ultrasonic imaging Transforms Feature extraction Time measurement Fractals Ensemble learning Image preprocessing

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Strong Stabilization of the Acrobot at the Down–Up Equilibrium Point ⋆

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IFAC-PapersOnLine 2023年第2期56卷 9167-9172页

作者： Ziyu Wang Xin Xin Yannian Liu School of Automation Southeast University and Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing 210096 China Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama 719-1197 Japan

In this paper, the strong stabilization of the Acrobot at the Down-Up equilibrium point is addressed, with the first link being downward and the second link being upright. By converting the strong stabilization of the Acrobot at the Down–Up equilibrium point equivalently to the existence and design of a stable stabilizing controller for a fourth-order single-input single-output linear plant with adjustable zeros, a pair of poles on the imaginary axis, and a pair of real poles located symmetrically with respect to the origin, this paper has three main contributions. Firstly, the existence of a stable stabilizing controller for any Acrobot which is linearly controllable at the Down–Up equilibrium point is proved by showing the range of the adjustable zero via adjusting a parameter of the output signal. Secondly, a necessary and sufficient condition on the mechanical parameters of the Acrobot is provided to guarantee the existence of a second-order stable stabilizing controller for the Acrobot around its Down– Up equilibrium point. Thirdly, a direct method is presented to design a second-order stable controller, whose transfer function is preset with three parameters. By utilizing the Liénard– Chipart criterion for a fourth-order polynomial, the necessary and sufficient conditions on these parameters for achieving the strong stabilization are obtained, which are expressed in a cascade form for obtaining these parameters conveniently. A numerical example is presented to validate the effectiveness of the proposed method.

关键词： Robust control strong stabilization underactuated mechanical systems reduced-order controller linear systems

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