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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yichong Li Xiaojian Liu Fan Cheng Xiaoyong Pan Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions.

关键词： Proteins Protein engineering Accuracy RNA Machine learning Predictive models Data models Gene expression Bioinformatics Diseases

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Real-Time Maritime Obstacle Detection Based on YOLOv5 for Autonomous Berthing 16th

Real-Time Maritime Obstacle Detection Based on YOLOv5 for Au...

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16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Chen, Guotong Qi, Jiangtao Dai, Zeyu Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China China Ship Scientific Research Center No. 222 Shanshui East Road Binhu District Wuxi214082 China

ISBN: (纸本)9789811912528

The shipping industry is developing towards intelligence, in which autonomous berthing and docking systems play an important role. Real-time maritime obstacle detection is essential for autonomous berthing, which can protect ships from collisions. In the field of real-time objection detection, YOLOv5s is one of the most effective networks. To solve the real-time maritime obstacle detection for autonomous berthing, we propose an improved network, named YOLOv5s-CBAM, which employs the convolutional block attention module to boost the representation power of YOLOv5s. In addition, we propose another network, named YOLOv5-SE, in which a squeeze-and-excitation block is introduced to recalibrate the channel-wise features adaptively. The experimental results demonstrate that YOLOv5s-CBAM outperforms YOLOv5s in the detection of large obstacles. Both YOLOv5s-CBAM and YOLOv5s-SE achieve state-of-the-art performance in various metrics compared with previous related work for maritime obstacle detection. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Ships

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Robust Remote Sensing Scene Classification with Multi-view Voting and Entropy Ranking 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wang, Jinyang Wang, Tao Gan, Min Hadjichristofi, George Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering and International Digital Economy College Minjiang University Fuzhou350108 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science and Engineering European University Cyprus Nicosia1516 Cyprus

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Deep convolutional neural networks have been widely used in scene classification of remotely sensed images. In this work, we propose a robust learning method for the task that is secure against partially incorrect categorization of images. Specifically, we remove and correct errors in the labels progressively by iterative multi-view voting and entropy ranking. At each time step, we first divide the training data into disjoint parts for separate training and voting. The unanimity in the voting reveals the correctness of the labels, so that we can train a strong model with only the images with unanimous votes. In addition, we adopt entropy as an effective measure for prediction uncertainty, in order to partially recover labeling errors by ranking and selection. We empirically demonstrate the superiority of the proposed method on the WHU-RS19 dataset and the AID dataset. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Entropy

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Temperature Gradient Analyses of a Tubular Solid Oxide Fuel Cell Fueled by Methanol

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Transactions of Tianjin University 2023年第1期29卷 14-30页

作者： Qidong Xu Meiting Guo Lingchao Xia Zheng Li Qijiao He Dongqi Zhao Keqing Zheng Meng Ni Department of Building and Real Estate Research Institute for Sustainable Urban Development(RISUD)&Research Institute for Smart Energy(RISE)The Hong Kong Polytechnic UniversityHong Kong 999077China School of Artifi cial Intelligence and Automation Key Laboratory of Image Processing and Intelligent ControlHuazhong University of Science and TechnologyWuhan 430074China School of Low-Carbon Energy and Power Engineering China University of Mining and TechnologyXuzhou 221116China

Thermal management in solid oxide fuel cells(SOFC)is a critical issue due to non-uniform electrochemical reactions and convective fl ows within the ***,a 2D mathematical model is established herein to investigate the thermal responses of a tubular methanol-fueled *** show that unlike the low-temperature condition of 873 K,where the peak temperature gradient occurs at the cell center,it appears near the fuel inlet at 1073 K because of the rapid temperature rise induced by the elevated current *** the large heat convection capacity,excessive air could not eff ectively eliminate the harmful temperature gradient caused by the large current ***,optimal control of the current density by properly selecting the operating potential could generate a local thermal neutral ***,the maximum axial temperature gradient could be reduced by about 18%at 973 K and 20%at 1073 K when the air with a 5 K higher temperature is ***,despite the higher electrochemical performance observed,the cell with a counter-fl ow arrange-ment featured by a larger hot area and higher maximum temperature gradients is not preferable for a ceramic SOFC system considering thermal ***,this study could provide insightful thermal information for the operating condition selection,structure design,and stability assessment of realistic SOFCs combined with their internal reforming process.

关键词： Solid oxide fuel cell Modeling Methanol fuel Temperature gradient Internal reforming

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WDNet: A Water Depth Perception Network with Global information

WDNet: A Water Depth Perception Network with Global Informat...

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第42届中国控制会议

作者： Zhihui Huang Yifeng Tang Chuancong Tang Yifan Xu ChuanShang Luo Bin Liu Hai-Tao Zhang School of Artificial Intelligence and Automation the Engineering Research Center of Autonomous Intelligent Unmanned Systems the Key Laboratory of Image Processing and Intelligent Control and the State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology

Deep perception of the unmanned surface vehicle's surroundings is an inaccessible part of its fully autonomous navigation mission. The existing methods, whether based on traditional stereo matching or deep learning, do not fully consider the characteristics of water environment, resulting in severe error depths in weak textures(sky, calm lake) and water reflections regions, that increases the risk of running aground or collision. What is worse that there is not a public dataset for depth estimation in the water environment. Therefore, this work proposes a self-supervised model for depth estimation named Water Depth Perception Network(WDNet) to address these problems. The decoder of this network has a wider receptive field and can effectively handle the depth error in the weak texture region. Besides, the WDNet is trained with a novel and effective loss function which assist the network to reduce errors in sky and water region, and some indexes are proposed to evaluate the model's performances in sky and water region. Finally, our proposed WDNet achieves a 0.1056 absolute relative error in ranging, the average number of error pixels in the sky area drops from 15803.87 to 580.91, which only accounted for 0.29% of the image,and the error in water region drops from 51.04 to 6.75, all of them are superior to the performance of baseline model.

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Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning

arXiv

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arXiv 2024年

作者： He, Fan He, Mingzhen Shi, Lei Huang, Xiaolin Suykens, Johan A.K. STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium MOE Key Laboratory of System Control and Information Processing Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory for Contemporary Applied Mathematics School of Mathematical Sciences Fudan University Shanghai200433 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China MOE Key Laboratory of System Control and Information Processing Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

Ridgeless regression has garnered attention among researchers, particularly in light of the "Benign Overfitting" phenomenon, where models interpolating noisy samples demonstrate robust generalization. However, kernel ridgeless regression does not always perform well due to the lack of flexibility. This paper enhances kernel ridgeless regression with Locally-Adaptive-Bandwidths (LAB) RBF kernels, incorporating kernel learning techniques to improve performance in both experiments and theory. For the first time, we demonstrate that functions learned from LAB RBF kernels belong to an integral space of Reproducible Kernel Hilbert Spaces (RKHSs). Despite the absence of explicit regularization in the proposed model, its optimization is equivalent to solving an 0-regularized problem in the integral space of RKHSs, elucidating the origin of its generalization ability. Taking an approximation analysis viewpoint, we introduce an lq-norm analysis technique (with 0 © 2024, CC BY.

关键词： Vector spaces

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LSU-NET: Lightweight Automatic Organs Segmentation Network for Medical images

LSU-NET: Lightweight Automatic Organs Segmentation Network f...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yujie Ding Shenghua Teng Zuoyong Li Xiao Chen College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures.

关键词： Training image segmentation Convolution Dynamic scheduling Stability analysis Resource management Speech processing Standards Monitoring Biomedical imaging

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A Parallel Read-Write Circuit With Fast Amplitude-Adaptive Matching Scheme to Memristor Crossbar Array

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IEEE Transactions on Circuits and Systems I: Regular Papers 2025年

作者： Jiang, Ningye Jiang, Mingxuan Huang, Haoen Yang, Le Huang, Yi Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430074 China Wuhan Institute of Technology School of Electrical and Information Engineering Wuhan430205 China University of Massachusetts Amherst Department of Electrical and Computer Engineering AmherstMA01003 United States

Memristor crossbar array (MCA) is a computing-in-memory (CIM) module for computational acceleration. However, conventional read-write (R-W) circuits for MCA rely heavily on external components and have shortcoming in long writing times. To address these issues, we propose a parallel R-W circuit with a fast amplitude-adaptive matching (AAM) scheme. The fast AAM scheme is designed to accelerate time of writing memristors in MCA by adaptively adjusting the writing voltage to match an optimal amplitude. The architecture of parallel R-W circuit are outlined with the implementation of R-W processes. The design principles of parallel R-W unit, fast AAM unit, and associated control logic are described for analyzing their functions within the circuit. By integrating the parallel R-W circuit with MCA, a computing block is leveraged in a neural network for classification tasks. The experimental results demonstrate that the neural network with multiple computing blocks maintains high accuracy across various datasets (over 80%). Furthermore, the proposed AAM scheme achieves an average 54% reduction in writing time compared to that one without AAM scheme. Compared to the pulse-width scheme, the parallel R-W circuit exhibits an average 3.05× speedup in adjusting a 5×5 MCA, and the speedup efficiency is higher as the increases of MCA size. © 2025 IEEE.

关键词： Memristors

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Vision-Based Autonomous Landing of a Quadrotor UAV on a Double-Layered Nested Landing Marker

Vision-Based Autonomous Landing of a Quadrotor UAV on a Doub...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Mu, Lingxia Zhang, Yuan Xin, Jing Zhang, Youmin School of Automation and Information Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Department of Mechanical and Industrial Engineering Concordia University MontrealH3G 1M8 Canada

ISBN: (纸本)9789811966125

This paper focuses on the vision-based autonomous landing mission of a quadrotor unmanned aerial vehicle (UAV). A double-layered nested Aruco landing marker is designed which can adapt to the situation that the field of sight of airborne camera changes as the UAV’s altitude decreases during the landing. Then, considering that the landing marker might be obstructed in certain situation, traditional Aruco detection method cannot effectively recognize marker. A YOLOv4 deep learning algorithm is used for marker detection. In addition, to address the problem of false detection in some test results, normalized Wasserstein distance is used to replace the non-maximum suppression to deal with redundant frames. The UAV’s relative location to the marker is then calculated and landing mission is finally achieved by the image-based visual servoing strategy. The simulation on Gazebo demonstrates that the designed marker is effective for the UAV landing mission and the revised YOLOv4 detection algorithm can increase detection results accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Antennas

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Disentangling Spatial and Temporal Learning for Efficient image-to-Video Transfer Learning

Disentangling Spatial and Temporal Learning for Efficient Im...

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International Conference on Computer Vision (ICCV)

作者： Zhiwu Qing Shiwei Zhang Ziyuan Huang Yingya Zhang Changxin Gao Deli Zhao Nong Sang Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Alibaba Group ARC National University of Singapore

Recently, large-scale pre-trained language-image models like CLIP have shown extraordinary capabilities for understanding spatial contents, but naively transferring such models to video recognition still suffers from unsatisfactory temporal modeling capabilities. Existing methods insert tunable structures into or in parallel with the pre-trained model, which either requires back-propagation through the whole pre-trained model and is thus resource-demanding, or is limited by the temporal reasoning capability of the pre-trained structure. In this work, we present DiST, which disentangles the learning of spatial and temporal aspects of videos. Specifically, DiST uses a dual-encoder structure, where a pre-trained foundation model acts as the spatial encoder, and a lightweight network is introduced as the temporal encoder. An integration branch is inserted between the encoders to fuse spatio-temporal information. The disentangled spatial and temporal learning in DiST is highly efficient because it avoids the back-propagation of massive pre-trained parameters. Meanwhile, we empirically show that disentangled learning with an extra network for integration benefits both spatial and temporal understanding. Extensive experiments on five benchmarks show that DiST delivers better performance than existing state-of-the-art methods by convincing gaps. When pre-training on the large-scale Kinetics-710, we achieve 89.7% on Kinetics-400 with a frozen ViT-L model, which verifies the scalability of DiST. Codes and models can be found in https://***/alibaba-mmai-research/DiST.

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