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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Lin, Danyu Xue, Shan Liu, Derong Zhao, Bo School of Automation Guangdong University of Technology Guangzhou510006 China South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Southern University of Science and Technology School of System Design and Intelligent Manufacturing Shenzhen518055 China Beijing Normal University School of Systems Science Beijing100875 China

ISBN: (纸本)9798350327182

Adaptive dynamic programming is used in this paper to solve the hierarchical decision problem for non-affine multi-player systems. A new affine system is formed by adding control dynamics to the original system. To solve the hierarchical decision problem of the original system, we modeled it as a Stackelberg game in the new system. Adaptive dynamic programming is used to approximate the value functions. The stability of the system is analyzed according to Lyapunov theory, which leads to the conclusion that both the system states and the weight errors are uniformly ultimately bounded. Finally, matlab simulation is used to verify the conclusion. © 2023 IEEE.

关键词： Dynamic programming

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MBTDiff: Multi-segmentation Brain Tumor Model with Diffusion Probabilistic Model 9

MBTDiff: Multi-segmentation Brain Tumor Model with Diffusion...

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9th International Conference on Image, Vision and Computing, ICIVC 2024

作者： Zeng, Shuqi Liu, Jun Xu, Ji Luo, Yuan School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China Key Laboratory of Biomarkers and In Vitro Diagnosis Translation Zhejiang province Hangzhou China Hubei Engineering Research Center for Intelligent Digital Technology in New Power System Hubei Central China Technology Development of Electric Power Co. Ltd Wuhan China

ISBN: (纸本)9798350385991

The diffusion probability model has recently garnered remarkable achievements in image generation, emerging as a prominent research focus within computer vision. It has been observed through numerous studies that the diffusion probability model holds potential for application across various domains within computer vision. Drawing inspiration from these models, we present a novel approach to extend their utility to the realm of brain tumor medical image segmentation. Our method entails training the model utilizing a diffusion framework, followed by iterative refinement of segmentation outcomes by incorporating additional encoding layers and decoders. This method employs an end-to-end training approach without relying on pre-trained backbone networks. Given the probabilistic nature of the results generated by the diffusion model and to simulate the actual decision-making process of doctors, we will generate multiple segmentation results and apply denoising techniques to obtain a stable output. These results will then be integrated into the final segmentation map to achieve more precise segmentation. © 2024 IEEE.

关键词： Image segmentation

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Maverick: Personalized Edge-Assisted Federated Learning with Contrastive Training 25

Maverick: Personalized Edge-Assisted Federated Learning with...

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34th ACM Web Conference, WWW 2025

作者： Wang, Kaibin He, Qiang Dong, Zeqian Chen, Rui He, Chuan Chua, Caslon Chen, Feifei Yang, Yun Swinburne University of Technology Melbourne Australia Huazhong University of Science and Technology Wuhan China Deakin University Melbourne Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798400712746

In an edge-assisted federated learning (FL) system, edge servers aggregate the local models from the clients within their coverage areas to produce intermediate models for the production of the global model. This significantly reduces the communication overhead incurred during the FL process. To accelerate model convergence, FedEdge, the state-of-the-art edge-assisted FL system, trains clients’ models in local federations when they wait for the global model in each training round. However, our investigation reveals that it drives the global model towards clients with excessive local training, causing model drifts that undermine model performance for other clients. To tackle this problem, this paper presents Maverick, a new edge-assisted FL system that mitigates model drifts by training personalized local models for clients through contrastive local training. It introduces a model-contrastive loss to facilitate personalized local federated training by driving clients’ local models away from the global model and close to their corresponding intermediate models. In addition, Maverick includes anomalous models in contrastive local training as negative samples to accelerate the convergence of clients’ local models. Extensive experiments are conducted on three widely-used models trained on three datasets to comprehensively evaluate the performance of Maverick. Compared to state-of-the-art edge-assisted FL systems, Maverick accelerates model convergence by up to 16.2x and improves model accuracy by up to 12.7%. © 2025 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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FogQSYM: An Industry 4.0 Analytical Model for Fog Applications

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computers, Materials & Continua 2021年第12期69卷 3163-3178页

作者： M.Iyapparaja M.Sathish Kumar SSiva Rama Krishnan Chiranji Lal Chowdhary Byungun Yoon Saurabh Singh Gi Hwan Cho School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India Department of Industrial and System Engineering Dongguk UniversitySeoulKorea Division of Computer Science Jeonbuk National UniversityJeonjuKorea

Industry 4.0 refers to the fourth evolution of technology development,which strives to connect people to various industries in terms of achieving their expected outcomes ***,resource management in an Industry 4.0 network is very complex and *** manage and provide suitable resources to each service,we propose a FogQSYM(Fog—Queuing system)model;it is an analytical model for Fog Applications that helps divide the application into several layers,then enables the sharing of the resources in an effective way according to the availability of memory,bandwidth,and network *** follows theMarkovian queuing model that helps identify the service rates of the devices,the availability of the system,and the number of jobs in the Industry 4.0 systems,which helps applications process data with a reasonable response *** experiment is conducted using a Cloud Analyst simulator with multiple segments of datacenters in a fog application,which shows that the model helps efficiently provide the arrival resources to the appropriate services with a low response *** implementing the proposed model with different sizes of fog services in Industry 4.0 applications,FogQSYM provides a lower response time than the existing optimized response time *** should also be noted that the average response time increases when the arrival rate increases.

关键词： Fog computing industry 4.0 fog layer Markovian queuing model

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Synergy of machine learning and Mie-tronics: tailoring resonant response of meta-atom on demand 28

Synergy of machine learning and Mie-tronics: tailoring reson...

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Integrated Optics: Devices, Materials, and Technologies XXVIII 2024

作者： George, Renee C. Sedeh, Hooman Barati Lai, Fangxing Li, Hao Li, Wenhao Gao, Jiannan Sun, Jingbo Xiao, Shumin Kuznetsov, Arseniy Litchinitser, Natalia M. Department of Electrical and Computer Engineering Duke University DurhamNC United States Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology Shenzhen Shenzhen518055 China School of Materials Science and Engineering Tsinghua University Beijing100084 China A*STAR Institute Singapore Singapore

ISBN: (纸本)9781510670389

Optical metasurfaces are engineered 2D electromagnetic structures enabling flat optical elements with properties not readily found in nature. Their unit cells, meta-atoms, usually are represented by a set of electric and magnetic multipoles. All-dielectric-based metasurfaces have recently attracted significant attention owing to their virtually lossless transmission properties at optical frequencies. A majority of reported dielectric metamaterials are composed of relatively simple meta-atoms such as spheres, cubes, and cylinders, whose electromagnetic response is dominated by the electric dipole. However, magnetic dipoles and higher-order multipoles may enable new optical properties and functionalities, including directional scattering, beam steering, and new frequency generation. Despite impressive progress in the field of optical metamaterials and nanofabrication technologies, engineering meta-atoms that support such higher-order resonances is still challenging. Here, we demonstrate that designed titanium dioxide meta-atoms can enable dominant magnetic dipole response. We apply a machine-learning model to predict a meta-atom shape with a strong magnetic dipole resonant mode at the operating wavelength of 750 nm. Using finite-element-based numerical simulations implemented in COMSOL Multiphysics, we found that the optimized meta-atom is robust against experimental variations and conditions such as a non-perfectly collimated incident beam, nanofabrication inaccuracies, and an added substrate. The meta-atoms have been fabricated using two approaches, focused ion beam lithography and an electron beam lithography followed by reactive ion etching, and characterized using white light spectroscopy. To the best of our knowledge, this is the first experimental realization of a machine-learning-based optimization of a magnetic dipole mode at optical frequencies. © 2024 SPIE.

关键词： Atoms

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A belief rule-based fault diagnosis method for Multi-UAVs under missing attribute data

A belief rule-based fault diagnosis method for Multi-UAVs un...

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2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Zhao, ErKai Hao, MingRui Yang, RuoHan Yue, KeYuan Yu, DeLin Harbin Normal University School of Computer Science and Information Engineering Harbin China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Northwestern Polytechnical University School of Electronics and Information Xi'an China Northwestern Polytechnical University School of Mathematics and Statistics Xi'an China

ISBN: (纸本)9798350312492

To reasonably improve the missing attribute data and effectively integrate sample data and uncertain expert knowledge, this paper proposes a new fault diagnosis method based on a belief rule base (BRB). In the case of missing attribute data, the maximum likelihood estimation (MLE) method is used to complete the missing attribute data, and the fault diagnosis method of the multi-UAVs is constructed by using other complete attribute data as the input information of BRB. Finally, the effectiveness of the proposed method is verified by experimental simulation. © 2023 IEEE.

关键词： Failure analysis

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A Novel Method for Tracking Neck Motions Using a Skin-Conformable Wireless Accelerometer: A Pilot Study

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Digital Biomarkers 2024年第1期8卷 40-51页

作者： Huang, Le Chun, Keum San Yu, Lian Lee, Jong Yoon Soetikno, Alan Chen, Hope Jeong, Hyoyoung Barrett, Joshua Martell, Knute Kang, Youn Patel, Alpesh A. Xu, Shuai Feinberg School of Medicine Northwestern University ChicagoIL United States Sibel Health NilesIL United States Electrical and Computer Engineering University of California Davis DavisCA United States Department of Orthopaedic Surgery Northwestern University Feinberg School of Medicine ChicagoIL United States Querrey Simpson Institute for Bioelectronics Northwestern University EvanstonIL United States Department of Ocean System Engineering Jeju National University Jeju Korea Republic of Departments of Orthopaedic Surgery and Neurosurgery Northwestern University Feinberg School of Medicine ChicagoIL United States Department of Dermatology Feinberg School of Medicine Northwestern University ChicagoIL United States

Introduction: Cervical spine disease is a leading cause of pain and disability. Degenerative conditions of the spine can result in neurologic compression of the cervical spinal cord or nerve roots and may be surgically treated with an anterior cervical discectomy and fusion (ACDF) in up to 137,000 people per year in the United States. A common sequelae of ACDF is reduced cervical range of motion (CROM) with patient-based complaints of stiffness and neck pain. Currently, tools for assessment of CROM are manual, subjective, and only intermittently utilized during doctor or physical therapy visits. We propose a skin-mountable acousto-mechanic sensor (ADvanced Acousto-Mechanic sensor;ADAM) as a tool for continuous neck motion monitoring in postoperative ACDF patients. We have developed and validated a machine learning neck motion classification algorithm to differentiate between eight neck motions (right/left rotation, right/left lateral bending, flexion, extension, retraction, protraction) in healthy normal subjects and patients. Methods: Sensor data from 12 healthy normal subjects and 5 patients were used to develop and validate a Convolutional Neural Network (CNN). Results: An average algorithm accuracy of 80.0 ± 3.8% was obtained for healthy normal subjects (94% for right rotation, 98% for left rotation, 65% for right lateral bending, 87% for left lateral bending, 89% for flexion, 77% for extension, 50% for retraction, 84% for protraction). An average accuracy of 67.5 ± 5.8% was obtained for patients. Discussion: ADAM, with our algorithm, may serve as a rehabilitation tool for neck motion monitoring in postoperative ACDF patients. Sensor-captured vital signs and other events (extubation, vocalization, physical therapy, walking) are potential metrics to be incorporated into our algorithm to offer more holistic monitoring of patients after cervical spine surgery. © 2024 The Author(s).

关键词： Machine learning

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Coherent stacking of 128 pulses amplified in the large nonlinear regime with pre-amplitude modulation

Coherent stacking of 128 pulses amplified in the large nonli...

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CLEO: Science and Innovations, S and I 2022

作者： Zhou, Kangzhu Yang, Bowei Zhang, Zhigang Li, Qian School of Electronic and Computer Engineering Peking University Shenzhen518055 China State Key Laboratory of Advanced Optical Communication System and Networks School of Electronics Peking University Beijing100871 China

ISBN: (纸本)9781557528209

关键词： Amplitude modulation

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Multi-View Representation Learning for Multi-Instance Learning with Applications to Medical Image Classification

Multi-View Representation Learning for Multi-Instance Learni...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Zhao, Lu Yuan, Liming Li, Zhenliang Wen, Xianbin School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China

ISBN: (纸本)9781665468190

Multi-Instance Learning (MIL) is a weakly supervised learning paradigm, in which every training example is a labeled bag of unlabeled instances. In typical MIL applications, instances are often used for describing the features of regions/parts in a whole object, e.g., regional patches/lesions in an eye-fundus image. However, for a (semantically) complex part the standard MIL formulation puts a heavy burden on the representation ability of the corresponding instance. To alleviate this pressure, we still adopt a bag-of-instances as an example in this paper, but extract from each instance a set of representations using 1 times1 convolutions. The advantages of this tactic are two-fold: i) This set of representations can be regarded as multi-view representations for an instance;ii) Compared to building multi-view representations directly from scratch, extracting them automatically using 1 times1 convolutions is more economical, and may be more effective since 1 times1 convolutions can be embedded into the whole network. Furthermore, we apply two consecutive multi-instance pooling operations on the reconstituted bag that has actually become a bag of sets of multi-view representations. We have conducted extensive experiments on several canonical MIL data sets from different application domains. The experimental results show that the proposed framework outperforms the standard MIL formulation in terms of classification performance and has good interpretability. © 2022 IEEE.

关键词： Convolution

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MULTIMODAL LEARNING TO IMPROVE CARDIAC LATE MECHANICAL ACTIVATION DETECTION FROM CINE MR IMAGES

arXiv

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arXiv 2024年

作者： Xing, Jiarui Wu, Nian Bilchick, Kenneth C. Epstein, Frederick H. Zhang, Miaomiao Department of Electrical and Computer Engineering University of Virginia United States Department of Computer Science University of Virginia United States Department of Biomedical Engineering University of Virginia Health System United States School of Medicine University of Virginia Health System United States

This paper presents a multimodal deep learning framework that utilizes advanced image techniques to improve the performance of clinical analysis heavily dependent on routinely acquired standard images. More specifically, we develop a joint learning network that for the first time leverages the accuracy and reproducibility of myocardial strains obtained from Displacement Encoding with Stimulated Echo (DENSE) to guide the analysis of cine cardiac magnetic resonance (CMR) imaging in late mechanical activation (LMA) detection. An image registration network is utilized to acquire the knowledge of cardiac motions, an important feature estimator of strain values, from standard cine CMRs. Our framework consists of two major components: (i) a DENSE-supervised strain network leveraging latent motion features learned from a registration network to predict myocardial strains;and (ii) a LMA network taking advantage of the predicted strain for effective LMA detection. Experimental results show that our proposed work substantially improves the performance of strain analysis and LMA detection from cine CMR images, aligning more closely with the achievements of DENSE. © 2024, CC BY.

关键词： Strain

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