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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Tong, Zhan Wu, Zhan Yang, Yang Mao, Weilong Wang, Shijie Li, Yinsheng Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Chinese Academy of Sciences Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Shenzhen518055 China School of Computer Science and Engineering Key Lab. of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology Nanjing210096 China

ISBN: (纸本)9798350337488

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly available dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics. © 2023 IEEE.

关键词： computed tomography deep unsupervised learning denoising diffusion probabilistic model Metal artifact reduction physics-based consistency constraint

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Extended State Observer Based Fault-Tolerant Predictive Control for a Four-Mecanum Wheels Mobile Robot 14

Extended State Observer Based Fault-Tolerant Predictive Cont...

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14th IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, CYBER 2024

作者： Yang, Binghao Wang, Dongliang Wen, Ziling Wei, Wu Li, Wenji Fan, Zhun Shantou University Key Lab of Digital Signal and Image Processing of Guangdong Province Guangdong 515063 China Shantou University School of Department of Electronic and Information Engineering Guangdong 515063 China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangdong Guangzhou510640 China School of Automation Science and Engineering South China University of Technology Guangdong Guangzhou510641 China

ISBN: (纸本)9798331506056

The application of unmanned vehicles in industrial cargo transportation is becoming increasingly widespread, particularly playing a significant role in transporting heavy-duty goods. However, when performing such tasks, whether in single-vehicle transportation or multi-vehicle collaborative transportation, the occurrence of wheel failures can potentially lead to serious safety accidents. Therefore, this paper proposes a fault-tolerant control scheme, establishing a kinematic model considering disturbances and failures, and adopts model predictive control for trajectory tracking. Additionally, an extended state observer is introduced to observe system states and counteract disturbances. To address potential failure issues, a controller reconstruction strategy is proposed. Finally, the effectiveness and superiority of the controller is validated through numerical simulation and comparative experiment. © 2024 IEEE.

关键词： Mobile robots

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Knowledge Augmented Attention Gating Embedding for Link Prediction

Knowledge Augmented Attention Gating Embedding for Link Pred...

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Asia-Pacific Signal and Information processing Association Annual Summit and Conference (APSIPA)

作者： Zewei Chen Shuhong Chen Chen Li Xianwei Zheng Minfan He Xutao Li Department of Electronic Engineering Key Lab of Digital Signal and Image Processing of Guangdong Province Shantou University Shantou China School of Mathematics Foshan University Foshan China

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

Knowledge graphs (KGs) use graphical representations to illustrate relations between entities, forming a structured knowledge base. However, KGs often suffer from incompleteness, lacking some entities and their interactions. Current link prediction technologies based on KG embeddings primarily focus on learning entity features and relation patterns from existing KGs to address the incompleteness issue. Yet, these methods encounter the inherent sparsity issue in KGs—uneven distribution of entity connections and scarcity of direct links. This complexity challenges models in identifying intricate, potential associations, thereby compromising link prediction accuracy and overall model performance. In this paper, we introduce a innovative Knowledge Augmented Attention-based Gating Embedding (KAAGE) framework for link prediction. Our model augments knowledge by reversing relations and triples, then integrates both original and augmented structural data through gating units. Extensive experiments on three established benchmark datasets demonstrate the superiority of KAAGE over current models. Our implementation code is available at https://***/22zwChen/KAAGE

关键词： Codes Fuses Semantics Knowledge based systems Knowledge graphs Information processing Predictive models Benchmark testing Data models Complexity theory

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Semi-supervised emotional classification of color images by learning from cloud

Semi-supervised emotional classification of color images by ...

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International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII

作者： Na Li Yong Xia Yuwei Xia Shaanxi Key Lab of Speech & Image Information Processing (SAIIP) Northwestern Polytechnical University Xi'an China Computer Science College Xi'an Polytechnic University Xi'an China

Classification of images based on the feelings generated by each image in its reviewers is becoming more and more popular. Due to the difficulty of gathering training data, this task is intrinsically a small-sample learning problem. Hence, the results produced by most existing solutions are less accurate. In this paper, we propose the semi-supervised hierarchical classification (SSHC) algorithm for emotional classification of color images. We extract three groups of features for each classification task and use those features in a two-level classification model that is based on the support vector machine (SVM) and Adaboost technique. To enlarge the training dataset, we employ each training image to retrieve similar images from the Internet cloud and jointly use the manually labeled small dataset and retrieved large but unlabeled dataset to train a classifier via semi-supervised learning. We have evaluated the proposed algorithm against the fuzzy similarity-based emotional classification (FSBEC) algorithm and another supervised hierarchical classification algorithm that does not learn from online images in three bi-class classification tasks, including “warm vs. cool”, “light vs. heavy” and “static vs. dynamic”. Our pilot results suggest that, by learning from the similar images archived in the Internet cloud, the proposed SSHC algorithm can produce more accurate emotional classification of color images.

关键词： image color analysis Feature extraction Training Support vector machines image edge detection Histograms Color

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A directional-progressive search method for infrared small target detection 3

A directional-progressive search method for infrared small t...

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3rd Optoelectronics Global Conference, OGC 2018

作者： Zhang, Xiangyue Ding, Qinghai Luo, Haibo Hui, Bin Chang, Zheng Zhang, Junchao Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Opto-Electronic Information Processing CAS Shenyang110016 China Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China Space Star Technology Co. Ltd Beijing100086 China

ISBN: (纸本)9781538673973

Infrared small target detection plays a key role in infrared precision guidance and infrared early-warning system. It has been a difficult problem for researchers to study on how to detect targets accurately at a long distance as early as possible. Most of the existing algorithms can detect small targets in simple backgrounds, but they would fail on the detection when the background clutters are chaotic and the signal to clutter ratio (SCR) is low. Therefore, we propose a new infrared small target detection method which called a directional-progressive search (DPS) method. Our method derives from a fact that a small target is an isotropic Gaussian distribution at a long distance, while clutters show different characteristics in different directions. Based on this difference, we decompose the original image into first-order sub-images with different directions by using a first-order directional derivative (FODD) filter. Then zero-crossing points are detected in each direction step by step to distinguish small targets and background clutters. After screening progressively, the positions where existing zero-crossing points in every sub-image can be confirmed as targets. Experimental results show that our method acquires higher detection rates and lower false alarm rates compared with other methods. At the same time, our method can still keep better performance under various complex backgrounds. The robustness of our method is strong. © 2018 IEEE.

关键词： Clutter (information theory)

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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SVM Based Fast CU Partitioning Algorithm for VVC Intra Coding

SVM Based Fast CU Partitioning Algorithm for VVC Intra Codin...

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IEEE International Symposium on Circuits and Systems

作者： Guoqing Wu Yan Huang Chen Zhu Li Song Wenjun Zhang Institute of Image Communication and Network Engineering Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University

Recently, Joint Video Experts Team (JVET) has completed the new Versatile Video Coding (H.266/VVC) standard. VVC employs a new block partition structure named quad-tree with nested multi-type tree (QTMT) to improve coding efficiency. However, the new block partition structure increases huge encoding time compared with HEVC for brute-force ratedistortion (RD) optimization. To reduce encoding complexity, we propose a Support Vector Machine (SVM) based fast CU partitioning algorithm for VVC intra coding in this paper which terminates redundant partitions early by predicting the partition of CU using texture information. We trained classifiers for CUs of different sizes to improve accuracy and control the complexity of the classifiers themselves. Different thresholds are set for each classifier to achieve a trade-off between encoding complexity and RD performance. Experimental results show that the proposed method can save encoder time ranging from 30.78% to 63.16% with 1.10% to 2.71% BD-BR increase.

关键词： Support vector machines Prediction algorithms Encoding Distance measurement Partitioning algorithms Classification algorithms Copper

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Impact of Rail Transit Systems on Transport Accessibility under Road Congestion

Impact of Rail Transit Systems on Transport Accessibility un...

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2022世界交通运输大会(WTC2022)

作者： Bo Zhan Hui Zhang Min Ouyang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Lab.for Image Processing and Intelligent Control Huazhong University of Science and TechnologyMinistry of Education

Investment inrail transit systems is widely advocated in large,high-density cities on the basis of its purported role in reducing road congestion,but the impact of rail transit systems on congestion remains a *** paper provides a method for evaluating the impact of rail transit systems on road congestion in terms of transport accessibility,and applies the method to 43 Chinese cities that have opened rail transit *** show that the rail transit systems can reduce road congestion in most cities,but their performance largely depends on the congestion speed and the rail transit network ***,the structural properties analysis of rail transit networks in two typical cities(Dalian and Changchun)indicates that rail transit lines with longer length,smaller detour factor,and larger population coverage rate tend to perform better in congestion *** findings in this paper provide guidance for city authorities regarding current and future investment in rail transit systems.

关键词： Rail transit system Congestion reduction Accessibility Rail transit network size Congestion speed Structural properties

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Exploiting learnable joint groups for hand pose estimation

arXiv

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arXiv 2020年

作者： Li, Moran Gao, Yuan Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent AI Lab United States

In this paper, we propose to estimate 3D hand pose by recovering the 3D coordinates of joints in a group-wise manner, where less-related joints are automatically categorized into different groups and exhibit different features. This is different from the previous methods where all the joints are considered holistically and share the same feature. The benefits of our method are illustrated by the principle of multi-task learning (MTL), i.e., by separating less-related joints into different groups (as different tasks), our method learns different features for each of them, therefore efficiently avoids the negative transfer (among less related tasks/groups of joints). The key of our method is a novel binary selector that automatically selects related joints into the same group. We implement such a selector with binary values stochastically sampled from a Concrete distribution, which is constructed using Gumbel softmax on trainable parameters. This enables us to preserve the differentiable property of the whole network. We further exploit features from those less-related groups by carrying out an additional feature fusing scheme among them, to learn more discriminative features. This is realized by implementing multiple 1x1 convolutions on the concatenated features, where each joint group contains a unique 1x1 convolution for feature fusion. The detailed ablation analysis and the extensive experiments on several benchmark datasets demonstrate the promising performance of the proposed method over the state-of-the-art (SOTA) methods. Besides, our method achieves top-1 among all the methods that do not exploit the dense 3D shape labels on the most recently released FreiHAND competition at the submission date. The source code and models are available at https://***/moranli-aca/LearnableGroups-Hand. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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Agglomerative Transformer for Human-Object Interaction Detection

Agglomerative Transformer for Human-Object Interaction Detec...

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International Conference on Computer Vision (ICCV)

作者： Danyang Tu Wei Sun Guangtao Zhai Wei Shen Institute of Image Communication and Network Engineering Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University

We propose an agglomerative Transformer (AGER) that enables Transformer-based human-object interaction (HOI) detectors to flexibly exploit extra instance-level cues in a single-stage and end-to-end manner for the first time. AGER acquires instance tokens by dynamically clustering patch tokens and aligning cluster centers to instances with textual guidance, thus enjoying two benefits: 1) Integrality: each instance token is encouraged to contain all discriminative feature regions of an instance, which demonstrates a significant improvement in the extraction of different instance-level cues and subsequently leads to a new state-of-the-art performance of HOI detection with 36.75 mAP on HICO-Det. 2) Efficiency: the dynamical clustering mechanism allows AGER to generate instance tokens jointly with the feature learning of the Transformer encoder, eliminating the need of an additional object detector or instance decoder in prior methods, thus allowing the extraction of desirable extra cues for HOI detection in a single-stage and end-to-end pipeline. Concretely, AGER reduces GFLOPs by 8.5% and improves FPS by 36%, even compared to a vanilla DETR-like pipeline without extra cue extraction. The code will be available at https://***/six6607/***.

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