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Line Drawing Guided Progressive Inpainting of Mural Damage

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arXiv 2022年

作者： Li, Luxi Zou, Qin Zhang, Fan Yu, Hongkai Chen, Long Song, Chengfang Huang, Xianfeng Wang, Xiaoguang Li, Qingquan Department of Computer Science Technology United International College of Beijing Normal University HongKong Baptist University Zhuhai China Machine Vision and Robotics Laboratory School of Computer Science Wuhan University Wuhan China State Key Laboratory of Surveying Mapping and Remote Sensing Information Engineering Wuhan University Wuhan China Department of Electrical Engineering and Computer Science Cleveland State University OH United States Institute of Automation Chinese Academy of Sciences Beijing China Cultural Heritage Intelligent Computing Laboratory Wuhan University Wuhan China Guangming Laboratory Shenzhen University Shenzhen China

Mural image inpainting is far less explored compared to its natural image counterpart and remains largely unsolved. Most existing image-inpainting methods tend to take the target image as the only input and directly repair the damage to generate a visually plausible result. These methods obtain high performance in restoration or completion of some pre-defined objects, e.g., human face, fabric texture, and printed texts, etc., however, are not suitable for repairing murals with varying subjects and large damaged areas. Moreover, due to discrete colors in paints, mural inpainting may suffer from apparent color bias. To this end, in this paper, we propose a line drawing guided progressive mural inpainting method. It divides the inpainting process into two steps: structure reconstruction and color correction, implemented by a structure reconstruction network (SRN) and a color correction network (CCN), respectively. In structure reconstruction, SRN utilizes the line drawing as an assistant to achieve large-scale content authenticity and structural stability. In color correction, CCN operates a local color adjustment for missing pixels which reduces the negative effects of color bias and edge jumping. The proposed approach is evaluated against the current state-of-the-art image inpainting methods. Qualitative and quantitative results demonstrate the superiority of the proposed method in mural image inpainting. The codes and data are available at https://***/qinnzou/mural-image-inpainting . © 2022, CC BY-NC-ND.

关键词： Restoration

Global Finite Time Stabilization of SISO Hurwitz Linear Systems Subject to Actuator Saturation: The Case of Real Eigenvalues

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Global Finite Time Stabilization of SISO Hurwitz Linear Syst...

American Control Conference (ACC)

作者： Tan Hou Yuanlong Li Zongli Lin Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA U.S.A.

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper considers the global finite time stabilization of linear systems subject to actuator saturation. We show that a controllable SISO system whose eigenvalues are real and negative can be globally stabilized in finite time by a homogeneous feedback law. To obviate numerical issues, this homogeneous feedback law is implemented by discretizing a compact set. Numerical simulation validates the effectiveness of the proposed approach.

关键词： Linear systems Actuators Numerical simulation Control systems Eigenvalues and eigenfunctions

SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification

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arXiv 2022年

作者： Wang, Jialin Gao, Rui Zheng, Haotian Zhu, Hao Shi, C.-J. Richard State Key Laboratory of ASIC and Systems The Institute of Brain-Inspired Circuits and Systems Zhangjiang Fudan International Innovation Center Fudan University Shanghai 201203 China Department of Electrical Engineering and Automation Aalto University Espoo02150 Finland Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States

In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for solving Epileptic EEG signal classification problems. The aim is to achieve a lightweight deep learning model without losing model classification accuracy. We propose a weighted neighborhood field graph (WNFG) to represent EEG signals, which reduces the redundant edges between graph nodes. WNFG has lower time complexity and memory usage than the conventional solutions. Using the graph representation, the sequential graph convolutional network is based on a combination of sparse weight pruning technique and the alternating direction method of multipliers (ADMM). Our approach can reduce computation complexity without effect on classification accuracy. We also present convergence results for the proposed approach. The performance of the approach is illustrated in public and clinical-real datasets. Compared with the existing literature, our WNFG of EEG signals achieves up to 10 times of redundant edge reduction, and our approach achieves up to 97 times of model pruning without loss of classification accuracy. © 2022, CC0.

关键词： Convolutional neural networks

Classifying Cover Crop Residue from RGB Images: A Simple SVM versus a SVM Ensemble

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Classifying Cover Crop Residue from RGB Images: A Simple SVM...

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Parth C. Upadhyay Lokesh Karanam John A. Lory Guilherme N. DeSouza Vision-Guided and Intelligent Robotics (ViGIR) Laboratory Department of Electrical Engineering and Computer Science (EECS) Division of Plant Sciences University of Missouri Columbia MO

ISBN: (纸本)9781728190495

Plant-derived crop residue on soil surface provides many important advantages including preventing erosion and conserving soil moisture. In that sense, making accurate determination on the percent of crop residue cover using RGB images can be a fundamental tool in protecting the soil. In our research, we approach the determination of such percentages as a classification problem, and in this paper, we compare two of these approaches. Both approaches relied on support vector machines (SVM) as the classifier of choice, and the same set of features, which were selected in our previous studies on the same topic. In this paper we developed a SVM ensemble with a hierarchical structure and compared it against a single, multi-class SVM classifier. In the SVM ensemble framework, four two-class SVMs and one five-class SVM were combined in sequence to better separate adjacent levels of residue cover. The rationale of the ensemble was to allow each of the two-class SVMs to find the hyperplanes that maximize the margin between the corresponding two consecutive classes. Then, based on the distance of the samples to these hyperplanes, probabilistic estimates of the data-point belonging to the class were computed and added as extra inputs for the last SVM. In order to enhance the performance of the ensemble, other considerations such as the use of Grid Search method for optimizing the hyperparameters were employed in the tuning of the SVMs. Numerical experiments were conducted over a dataset of 4,400 images, which were collected from 88 locations in 40 row crop fields in five Missouri counties between mid-April and early July in 2018 and 2019. The images were collected using a camera mounted on a tripod, with a spatial resolution of 0.014 cm pixel- 1 GSD (Ground Sampling Distance). The experiments highlighted the better performance of the proposed hierarchical ensemble classifier, which achieved a cross-validation accuracy of 86.3 % vs an accuracy of 80.4 % for the single SVM, whil

关键词： Support vector machines Measurement Training Search methods Soil moisture Crops Probabilistic logic

Mixture of Experts for Network Optimization: A Large Language Model-enabled Approach

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arXiv 2024年

作者： Du, Hongyang Liu, Guangyuan Lin, Yijing Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation resource and energy consumption and can lead to inconsistent outcomes. To address this issue, we propose a novel approach utilizing a Mixture of Experts (MoE) framework, augmented with Large Language Models (LLMs), to analyze user objectives and constraints effectively, select specialized DRL experts, and weigh each decision from the participating experts. Specifically, we develop a gate network to oversee the expert models, allowing a collective of experts to tackle a wide array of new tasks. Furthermore, we innovatively substitute the traditional gate network with an LLM, leveraging its advanced reasoning capabilities to manage expert model selection for joint decisions. Our proposed method reduces the need to train new DRL models for each unique optimization problem, decreasing energy consumption and AI model implementation costs. The LLM-enabled MoE approach is validated through a general maze navigation task and a specific network service provider utility maximization task, demonstrating its effectiveness and practical applicability in optimizing complex networking systems. © 2024, CC BY.

关键词： Reinforcement learning

Fully implantable and battery-free wireless optoelectronic system for modulable cancer therapy and real-time monitoring

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npj Flexible Electronics 2023年第1期7卷 119-130页

作者： Kiho Kim In Sik Min Tae Hee Kim Do Hyeon Kim Seungwon Hwang Kyowon Kang Kyubeen Kim Sangun Park Jongmin Lee Young Uk Cho Jung Woo Lee Woon-Hong Yeo Young Min Song Youngmee Jung Ki Jun Yu Functional Bio-integrated Electronics and Energy Management Lab School of Electrical and Electronic EngineeringYonsei University50 Yonsei-roSeodaemun-guSeoul 03722Republic of Korea Center for Biomaterials Biomedical Research InstituteKorea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea Department of Fusion Research and Collaboration Biomedical Research InstituteSeoul National University HospitalSeoul 03080Republic of Korea School of Electrical Engineering and Computer Science(EECS) Gwangju Institute of Science and Technology(GIST)Gwangju 61005Republic of Korea PA1 Foundry BusinessSamsung Electronics1 Samsungjeonja-roHwaseong-siGyeonggi-doRepublic of Korea R&D Center DNA Lab PharmaresearchSeongnam 13486Republic of Korea KU-KIST Graduate School of Converging Science and Technology Korea UniversitySeoul 136-705Republic of Korea School of Materials Science and Engineering Energy Materials for Soft Electronics LaboratoryPusan National UniversityBusan 46241Republic of Korea IEN Center for Human-Centric Interfaces and Engineering and George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University School of MedicineAtlantaGA 30332USA Institute for Robotics and Intelligent Machines Parker H.Petit Institute for Bioengineering and BiosciencesInstitute for MaterialsNeural Engineering CenterGeorgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Electronic Engineering YU-Korea Institute of Science and Technology(KIST)InstituteYonsei University50Yonsei-roSeodaemun-guSeoul 03722Republic of Korea

Photodynamic therapy(PDT)is attracting attention as a next-generation cancer treatment that can selectively destroy malignant tissues,exhibit fewer side effects,and lack pain during *** PDT systems have recently been developed to resolve the issues of bulky and expensive conventional PDT systems and to implement continuous and repetitive *** implantable PDT systems,however,are not able to perform multiple functions simultaneously,such as modulating light intensity,measuring,and transmitting tumor-related data,resulting in the complexity of cancer ***,we introduce a flexible and fully implantable wireless optoelectronic system capable of continuous and effective cancer treatment by fusing PDT and hyperthermia and enabling tumor size monitoring in *** system exploits micro inorganic light-emitting diodes(μ-LED)that emit light with a wavelength of 624 nm,designed not to affect surrounding normal tissues by utilizing a fully programmable light intensity ofμ-LED and precisely monitoring the tumor size by Si phototransistor during a long-term implantation(2–3 weeks).The superiority of simultaneous cancer treatment and tumor size monitoring capabilities of our system operated by wireless power and data transmissions with a cell phone was confirmed through in vitro experiments,ray-tracing simulation results,and a tumor xenograft mouse model in *** all-in-one single system for cancer treatment offers opportunities to not only enable effective treatment of tumors located deep in the tissue but also enable precise and continuous monitoring of tumor size in real-time.

关键词： optoelectronic enable battery

Deep Learning-Based Object Pose Estimation: A Comprehensive Survey

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arXiv 2024年

作者： Liu, Jian Sun, Wei Yang, Hui Zeng, Zhiwen Liu, Chongpei Zheng, Jin Liu, Xingyu Rahmani, Hossein Sebe, Nicu Mian, Ajmal The National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China The School of Architecture and Art Central South University Changsha410082 China The Department of Automation Tsinghua University Beijing100084 China The School of Computing and Communications Lancaster University LA1 4YW United Kingdom The Department of Information Engineering and Computer Science University of Trento Trento38123 Italy The Department of Computer Science The University of Western Australia WA6009 Australia

Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contemporary methods, including their dependency on labeled training data, model compactness, robustness under challenging conditions, and their ability to generalize to novel unseen objects. A recent survey discussing the progress made on different aspects of this area, outstanding challenges, and promising future directions, is missing. To fill this gap, we discuss the recent advances in deep learning-based object pose estimation, covering all three formulations of the problem, i.e., instance-level, category-level, and unseen object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks, providing the readers with a holistic understanding of this field. Additionally, it discusses training paradigms of different domains, inference modes, application areas, evaluation metrics, and benchmark datasets, as well as reports the performance of current state-of-the-art methods on these benchmarks, thereby facilitating the readers in selecting the most suitable method for their application. Finally, the survey identifies key challenges, reviews the prevailing trends along with their pros and cons, and identifies promising directions for future research. We also keep tracing the latest works at Awesome-Object-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

A Continual Learning Method for Reducing Class Interference Based on Replay

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A Continual Learning Method for Reducing Class Interference ...

Chinese Control Conference (CCC)

作者： Zhibo Xu Tian Wang Jian Wang Ce Li Yao Fu Hichem Snoussi School of Automation Science and Electrical Engineering Beihang University Beijing China Institute of Artificial Intelligence SKLSDE Beihang University Beijing China Zhongguancun Laboratory Beijing China School of Computer Science Wuhan University Wuhan China Department of Computer Science and Technology China University of Mining and Technology Beijing China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun China Laboratory of Computer Science and Digital Society University of Technology of Troyes Troyes France

Although deep neural networks perform well on many individual tasks, they suffer from catastrophic forgetting when learning new tasks continually. Recently, various continual learning methods have been proposed, and some approaches based on replaying memory data achieve promising performance. Maximally Interfered Retrieval is a strong replay-based baseline, however, exists class interference due to class imbalance and lacks the ability to generalize to the real class-incremental scenario. In this paper, aiming at these problems, we design Class cumulative Classifier to replace the shared output layer of the original network, which makes it closer to practical applications, and Class balanced Buffer to address the class imbalance of stored samples. In addition, we propose Retrospect strategy to further improve the accuracy. Experimental results on benchmark datasets show that our method outperforms several strong baselines and is more suitable for complex datasets with more classes.

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Minimal-time Deadbeat Consensus and Individual Disagreement Degree Prediction for High-order Linear Multi-agent Systems

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arXiv 2023年

作者： Hu, Fu-Long Zhang, Hai-Tao Xu, Bowen Hu, Zhe Ren, Wei School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University Xi’an710072 China Department of Electrical and Computer Engineering University of California Riverside92521 United States

In this paper, a Hankel matrix-based fully distributed algorithm is proposed to address a minimal-time deadbeat consensus prediction problem for discrete-time high-order multi-agent systems (MASs). Therein, each agent can predict the consensus value with the minimum number of observable historical outputs of its own. Accordingly, compared to most existing algorithms only yielding asymptotic convergence, the present method can attain deadbeat consensus instead. Moreover, based on the consensus value prediction, instant individual disagreement degree value of MASs can be calculated in advance as well. Sufficient conditions are derived to guarantee both the minimal-time deadbeat consensus and the instant individual disagreement degree prediction. Finally, both the effectiveness and superiority of the proposed deadbeat consensus algorithm are substantiated by numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems