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arXiv 2024年

作者： Wang, Jingyao Mou, Luntian Zheng, Changwen Gao, Wen The University of Chinese Academy of Sciences Beijing100049 China The National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing100086 China Beijing Institute of Artiicial Intelligence Faculty of Information Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing100124 China The School of Electronics Engineering and Computer Science Peking University Beijing100871 China

Freeform handwriting authentication verifies a person’s identity from their writing style and habits in messy handwriting data. This technique has gained widespread attention in recent years as a valuable tool for various fields, e.g., fraud prevention and cultural heritage protection. However, it still remains a challenging task in reality due to three reasons: (i) severe damage, (ii) complex high-dimensional features, and (iii) lack of supervision. To address these issues, we propose SherlockNet, an energy-oriented two-branch contrastive self-supervised learning framework for robust and fast freeform handwriting authentication. It consists of four stages: (i) pre-processing: converting manuscripts into energy distributions using a novel plug-and-play energy-oriented operator to eliminate the influence of noise;(ii) generalized pre-training: learning general representation through two-branch momentum-based adaptive contrastive learning with the energy distributions, which handles the high-dimensional features and spatial dependencies of handwriting;(iii) personalized fine-tuning: calibrating the learned knowledge using a small amount of labeled data from downstream tasks;and (iv) practical application: identifying individual handwriting from scrambled, missing, or forged data efficiently and conveniently. Considering the practicality, we construct EN-HA, a novel dataset that simulates data forgery and severe damage in real applications. Finally, we conduct extensive experiments on six benchmark datasets including our EN-HA, and the results prove the robustness and efficiency of SherlockNet. We will release our code and dataset at https://***/WangJingyao07/SherlockNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Impact Time and Angle Control Guidance Based on Cubic Bézier Curves: Theory and Experiment

SSRN

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SSRN 2024年

作者： Li, Heng Lv, Xiaokang Yu, Jianglong Zhang, Kun Wang, Yanyan Dong, Xiwang Institute of Artificial Intelligence Beihang University Beijing100191 China School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Institute of Unmanned System Beihang University Beijing100191 China School of Astronautics Beihang University Beihang University Beijing100191 China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing100074 China

A trajectory-shaping guidance strategy with constraints on impact time, impact angle, and maximum lateral acceleration is proposed based on a cubic Bézier curve fitting method. Firstly, an optimal guidance model is formulated, in which the Bézier curves are employed to fit the guidance trajectory, ensuring compliance with the required constraints. The manipulation transforms the optimal guidance problem into an optimal Bézier guidance trajectory design problem and a trajectory tracking problem. Then, by parameterizing the Bézier curves and analyzing the trajectory constraints in detail, the optimal Bézier guidance trajectory design is transformed into optimizing the control parameters of the Bézier curves. Moreover, a multi-layer zeroth-order Adam trajectory optimization algorithm based on general and fine tuning is introduced to determine the control parameters without relying on gradient information. Subsequently, a hyperbolic tangent vector field path tracking guidance algorithm is proposed to follow the designed trajectory and fulfill the guidance missions. Finally, numerical simulations and equivalent physical experiments verify the effectiveness of the proposed guidance strategy. © 2024, The Authors. All rights reserved.

关键词： Trajectories

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BGRA-Net: Boundary-Guided and Region-Aware Convolutional Neural Network for the Segmentation of Breast Ultrasound Images

BGRA-Net: Boundary-Guided and Region-Aware Convolutional Neu...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Zhang, Xiang Li, Xuanya Hu, Kai Gao, Xieping Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Baidu Inc. Beijing100085 China Key Laboratory of Medical Imaging and Artifical Intelligence of Hunan Province Xiangnan University Chenzhou423000 China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

ISBN: (纸本)9781665401265

In this paper, we propose a novel convolutional neural network based on boundary-guided and region-aware (BGRA-Net) for breast tumor segmentation in ultrasound images. In particular, in the encoding stage, we propose a boundary-guided module (BGM) to guide the learning of boundary features in the decoding stage by explicitly strengthening the extracted boundary information. Meanwhile, in the decoding stage, we propose a region-aware module (RAM) to integrate different levels of detailed and semantic features to improve the comprehensive representation of tumor regional features. Besides, a scale-adaptive module (SAM) is further proposed to capture the characteristics of tumors with different sizes between the encoding and decoding stages. To evaluate the effectiveness of our BGRA-Net, we conduct extensive experiments on the UDIAT dataset and compare it with eight state-of-the-art methods. The experimental results show that our BGRA-Net outperforms the state-of-the-art methods and can achieve accurate segmentation of breast tumors with ambiguous boundaries. © 2021 IEEE.

关键词： Tumors

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Adversarial Learning Data Augmentation for Graph Contrastive Learning in Recommendation

arXiv

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arXiv 2023年

作者： Huang, Junjie Cao, Qi Xie, Ruobing Zhang, Shaoliang Xia, Feng Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China WeChat Tencent Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Recently, Graph Neural Networks (GNNs) achieve remarkable success in Recommendation. To reduce the influence of data sparsity, Graph Contrastive Learning (GCL) is adopted in GNN-based CF methods for enhancing performance. Most GCL methods consist of data augmentation and contrastive loss (e.g., InfoNCE). GCL methods construct the contrastive pairs by hand-crafted graph augmentations and maximize the agreement between different views of the same node compared to that of other nodes, which is known as the InfoMax principle. However, improper data augmentation will hinder the performance of GCL. InfoMin principle, that the good set of views shares minimal information and gives guidelines to design better data augmentation. In this paper, we first propose a new data augmentation (i.e., edge-operating including edge-adding and edge-dropping). Then, guided by InfoMin principle, we propose a novel theoretical guiding contrastive learning framework, named Learnable Data Augmentation for Graph Contrastive Learning (LDA-GCL). Our methods include data augmentation learning and graph contrastive learning, which follow the InfoMin and InfoMax principles, respectively. In implementation, our methods optimize the adversarial loss function to learn data augmentation and effective representations of users and items. Extensive experiments on four public benchmark datasets demonstrate the effectiveness of LDA-GCL. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Detection of Incipient Faults in Non-Stationary Processes Based on Canonical Variable Dissimilarity Analysis and Ensemble Learning

SSRN

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SSRN 2024年

作者： Gao, Xuejin Ma, Wenxuan Han, Huayun Gao, Huihui Qi, Yongsheng Han, Huazheng Beijing Laboratory for Urban Mass Transit Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education School of Information Science and Technology Beijing University of Technology Beijing Beijing100124 China School of Electric Power Inner Mongolia University of Technology Inner Mongolia Hohhot010051 China Beijing University China Beijing University of Technology China

Complex industrial processes’ nonstationary and dynamic characteristics pose a significant challenge for detecting incipient faults. Therefore, this paper presents a novel way to find early signs of faults by combining cointegration analysis (CA) and based on canonical variate dissimilarity analysis (CVDA) ensemble detector (CVDAED). Firstly, the co-integration analysis method (CA) is employed to identify the equilibrium relationship among non-stationary variables in fault detection, subsequently generating multiple stationary residual sequences. Following this, several CVDA-based detectors are developed through bootstrap sampling of the stationary datasets, which facilitate the creation of a matrix derived from dissimilarity indices. Finally, sensitive detection indicators are established by utilizing the sum of singular values from a one-step moving window along aforementioned matrix. This detection index proves effective in identifying early failures that may elude conventional single detectors. Penicillin fermentation and numerical examples thoroughly validate the efficacy of this method. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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RDNet: A reinforced-deformable convolutional network for crowd counting

RDNet: A reinforced-deformable convolutional network for cro...

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IEEE International Conference on Robotics and Biomimetics

作者： Qingzhen Shang Yidi Zhuo Jiahui Zhang Jinfu Yang Control engineering from Beijing University of Technology Beijing China Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China

ISBN: (纸本)9781665481106

Crowd counting is still a challenging task in crowded scenario due to heavy occlusions, appearance variations and perspective distortions. Thanks to the development of deep convolutional neural networks, especially the deformable convo-lution neurral networks, great progress has been made for crowd counting. However, existing deformable convolution networks cannot get satisfactory performance due to limited receptive field that is not conducive to crowded object detection. In this paper, a novel end-to-end Reinforced-Deformable convolutional neural Network (RDNet) is proposed. First, a multi-scale context information fusion strategy with skipping connection and shortcut connection is utilized to obtain rich semantic features and local details of the crowd distribution. Then a reinforced-deformable convolution module is designed as the back-end of the network, which can sample with adaptive offsets and scalars to enlarge the receptive field for more accurate edge information and crowd distribution localization. Finally, an impulse function convolved with cuphead Gaussian kernel is applied to generate density map for counting. Extensive experimental results on four popular datasets, namely, Shangha-itech, UCF _CC_50, WorldEXPO'10, and UCF-QNRF, show that our proposed approach outperforms other state-of-the-art methods.

关键词： Location awareness Convolution Sociology Semantics Object detection Convolutional neural networks Task analysis

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A Geometry Aware Diffusion Model for 3D Point Cloud Generation

A Geometry Aware Diffusion Model for 3D Point Cloud Generati...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Ao Zhang Zhen Shen Qihang Fang Jian Yang Gang Xiong Xisong Dong State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Point clouds have increasingly become the preferred representation for various visual and graphical applications. Denoising diffusion probability models (DDPMs) have demonstrated remarkable applications in areas such as 3D point cloud generation. Furthermore, the ability to generate or reconstruct high-resolution, high-fidelity point clouds stands as a pivotal requirement within this domain. To this end, we propose a geometry aware diffusion model for 3D shape generation. Considering that the global latent shape space overlooks the inherent topology and fine-grained information within the 3D shape itself, we introduce a geometry latent space that operates in a cascaded relationship with the global shape latent space. This latent space combines the global shape latent with the geometry latent space. To generate high-quality point clouds, we conduct training on the geometry latent space, which yields superior results compared to training solely on the global shape latent space. Specifically, we represent the point cloud as a set of unordered point representations with positional embedding. We further employ a geometry aware attention block to model global geometric relationships and retaining local detail features, capturing the inductive bias of the 3D geometric structure of the point cloud. We conduct experiments across various ShapeNet benchmarks, and our approach has demonstrated substantial advancements in the generation of point clouds when compared to the baseline.

关键词： Point cloud compression Geometry Training Solid modeling Visualization Three-dimensional displays Shape Noise reduction Diffusion models Topology

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion

arXiv

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arXiv 2024年

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024, CC BY.

关键词： Color image processing

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LBP index for evaluation of disk degaussing achievement based on AFM image

LBP index for evaluation of disk degaussing achievement base...

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2021 International Conference on Information, Cybernetics, and Computational Social systems, ICCSS 2021

作者： Zhang, Ziying Xu, Zhe Yao, Yaxuan Liu, Xiaoge Tang, Jian Beijing University of Technology Faculty of Information Technology Beijing China National Institute of Metrology Center for Advanced Measurement Science Beijing China Beijing University of Technology Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (纸本)9781665402453

In the field of information security, it is very important to judge whether the information on a magnetic storage medium is completely destroyed. But so far, domestic research on the degaussing effect of magnetic storage media is still lacking. Previous studies have shown that the magnetic images before and after degaussing can reflect the amount of meaningful information left on the disk, which is closely related to the degaussing effect. Therefore, this paper proposes a new method to study the magnetic images before and after degaussing. This paper introduces the LBP texture feature extraction algorithm to process the magnetic images before and after degaussing, and evaluates the degaussing effect of the magnetic storage medium through the extracted texture feature values. A new LBP degaussing evaluation index is proposed, and the parameters of the index are optimized to achieve the best evaluation performance. © 2021 IEEE.

关键词： Degaussing

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Deep Residual Feature Generating Network: A Novel Method for Few-Shot Fault Diagnosis of Chillers

Deep Residual Feature Generating Network: A Novel Method for...

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Chinese Control Conference (CCC)

作者： Kexin Jiang Xuejin Gao Huihui Gao Huayun Han Qi Yongsheng Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Centre of Digital Community Ministry of Education Beijing University of Technology Beijing China Beijing Laboratory for Urban Mass Transit Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China School of Electric Power Inner Mongolia University of Technology Hohhot Inner Mongolia China

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The scarcity of historical data on fault states is a common phenomenon in the health management of chillers. This phenomenon leads to poor performance of fault diagnosis methods based on deep learning for chillers. Generative Adversarial Network (GAN), a common data augmentation method, is an effective approach to conquer this challenge. However, dynamic coupling characteristics exist in the operational data of chillers. Dynamic characteristics lead to distribution differences in operational data at different times within the same category. The coupling characteristics result in higher inter-class similarity in the operational data. These characteristics pose challenges for GAN-based data augmentation tasks. Therefore, an encoder-decoder-based residual feature extraction method is introduced to enhance inter-class separability while improving intra-class cohesion. Furthermore, a residual feature generating network (DRFGN) is designed to augment the limited historical dataset. In the DRFGN, to address the issue of insufficient distribution paradigm, an extended class characteristic information called latent attributes is introduced. During the training process of DRFGN, latent attributes are introduced as extended discriminative information, allowing the generator to comprehensively learn the class characteristics of real data. Additionally, smooth L1 loss is employed to further enhance the similarity between the synthesized and real data. Finally, a dataset collected from the actual equipment, referred to as HY-31C, is used for the validation of the proposed method. The results indicate satisfactory distribution similarity between the synthesized and real data, benefiting the health management and maintenance decision of the equipment.

关键词： Training Fault diagnosis Couplings Deep learning Generative adversarial networks Feature extraction Data augmentation

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