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A Three-Stage Physics-Driven Transmission Scheme For Integrated Sensing and Communication Systems With Noisy Angular Information

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IEEE Wireless Communications Letters 2025年

作者： Su, Huangyun Lian, Lixiang Huang, Kaixuan Zhang, Shunqing Shanghai University Shanghai Institute for Advanced Communication and Data Science Key Laboratory of Specialty Fiber Optics and Optical Access Networks School of Information and Communication Engineering Shanghai200444 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Integrated sensing and communication (ISAC) can facilitate both data transmission and environment sensing on a unified hardware platform. In the physical environment, the sensing targets may serve as part of the communication channel scatterers, which results in correlations between communication and sensing channel. In this letter, we propose to exploit such physics-driven correlations to design a three-stage transmission schemes to enhance the robustness of the ISAC system against noisy angular information, which refers to the error for estimating the angle of departure/arrival (AoD/AoA). In particular, we match angle information driven by the physical environment and fune-tune the matched pairs. Furthermore, we exploit this correlations to design a beamforming hybrid design algorithm based on optimal performance bound, which improving the robustness of system. Through numerical simulations, we verify that the correlation between communication and sensing channels can be effectively exploited to design transmission schemes that showcase improved sensing and communication performances with noisy angular information. © 2012 IEEE.

关键词： data communication systems

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Convergence-Aware Optimal Checkpointing for Exploratory Deep Learning Training Jobs

SSRN

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SSRN 2024年

作者： Li, Hongliang Wang, Zichen Zhao, Hairui Zhang, Meng Li, Xiang Xu, Haixiao College of Computer Science and Technology Jilin University Qianjin street 2699 Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering The Ministry of Education Qianjin street 2699 Jilin Changchun130012 China High Performance Computing Center Qianjin street 2699 Jilin Changchun130012 China

Training Deep Learning (DL) models are becoming more time-consuming, thus interruptions to the training processes are inevitable. Existing fault-tolerant work adopted checkpoint/recovery mechanism from traditional HPC platforms that makes periodical persistent copies of model states to save execution progress. These checkpoints have fixed time intervals that they are evenly placed across the training. We can obtain an optimal checkpointing interval for an HPC job with the precondition that the progress of a job is proportional to its execution time. Unfortunately, it is not the case in DL model training where a DL training job yields diminishing returns across its lifetime. It makes the early progress of a DL training job more valuable than the later ones. Even placement of checkpoints would either increase the risks in the early stages or waste resources overprotecting the latter stages. Meanwhile, the issue can get amplified for exploratory training jobs, where early terminations are common. Moreover, in data parallelism, the state-of-art quality-driven scheduling strategies allocate more resources for the early stages of a job than the later ones to accelerate the training progress which further amplifies the issue. This paper introduces a novel checkpointing interval problem for exploratory DL training jobs based on model convergence progress. We present COCI, an approach to compute optimal checkpointing configuration for a DL training job, minimizing the fault-tolerant overhead, including checkpointing cost and recovery cost. We implement COCI based on state-of-art iteration-level checkpointing mechanism, as a pluggable module compatible with PyTorch. COCI requires no extra user input. We conduct comprehensive evaluations with real DL application setups. The experimental results show that COCI reduces up to 40.18% fault-tolerant overhead compared to existing state-of-the-art DL fault-tolerant methods in serial scenario, 57.26% in data parallel scenario and 63.8

关键词： Iterative methods

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A Blockchain Based data Auction Mechanism 3

A Blockchain Based Data Auction Mechanism

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3rd International Conference on Blockchain Technology and Information Security, ICBCTIS 2023

作者： Wang, Hao Si, Jinxin Zang, Xiuhuan Ding, Shiqi Liu, Chang Pan, Jianhong Shen, Junzhe Engineering Data Center State Grid Economic and Technological Research Institute Co. Ltd Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Digital Department State Grid Jilin Electric Power Co.Ltd Changchun China Planning and Operation Department of Economic and Technological Research Institute of State Grid Jilin Electric Power Co. LTD Changchun China

ISBN: (纸本)9798350340402

In the present digital era, data has become an increasingly valuable asset to both enterprises and society. Despite the vast amount of available data, the absence of a reliable means of transaction often hinders the direct benefits that data can bring. This results in a lack of willingness among data owners to conduct transactions, poor data mobility, and an inability to fully realize the value of data. In order to promote the transaction and circulation of data, this study Eposesa data auction model based on blockchain technology, which ensures transparency, security, and efficiency in data auctions. Furthermore, an optimal auction algorithm is devised, which demonstrates that in the process of data trading with multiple participants, the auction model can maximize the utility of data owners, improve their willingness to trade, enhance data liquidity, fully realize the value of data, and provide a platform for sustainable development of data trading. © 2023 IEEE.

关键词： Smart contract

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Feature Compression for Cloud-Edge Multimodal 3D Object Detection

arXiv

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arXiv 2024年

作者： Tian, Chongzhen Li, Zhengxin Yuan, Hui Hamzaoui, Raouf Shen, Liquan Kwong, Sam The School of Control Science and Engineering Shandong University Jinan250061 China The School of Engineering and Sustainable Development De Montfort University LeicesterLE1 9BH United Kingdom The Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai200444 China The School of Data Science Lingnan University Hong Kong

Machine vision systems, which can efficiently manage extensive visual perception tasks, are becoming increasingly popular in industrial production and daily life. Due to the challenge of simultaneously obtaining accurate depth and texture information with a single sensor, multimodal data captured by cameras and LiDAR is commonly used to enhance performance. Additionally, cloud-edge cooperation has emerged as a novel computing approach to improve user experience and ensure data security in machine vision systems. This paper proposes a pioneering solution to address the feature compression problem in multimodal 3D object detection. Given a sparse tensor-based object detection network at the edge device, we introduce two modes to accommodate different application requirements: Transmission-Friendly Feature Compression (T-FFC) and Accuracy-Friendly Feature Compression (A-FFC). In T-FFC mode, only the output of the last layer of the network’s backbone is transmitted from the edge device. The received feature is processed at the cloud device through a channel expansion module and two spatial upsampling modules to generate multi-scale features. In A-FFC mode, we expand upon the T-FFC mode by transmitting two additional types of features. These added features enable the cloud device to generate more accurate multi-scale features. Experimental results on the KITTI dataset using the VirConv-L detection network showed that T-FFC was able to compress the features by a factor of 6061 with less than a 3% reduction in detection performance. On the other hand, A-FFC compressed the features by a factor of about 901 with almost no degradation in detection performance. We also designed optional residual extraction and 3D object reconstruction modules to facilitate the reconstruction of detected objects. The reconstructed objects effectively reflected the shape, occlusion, and details of the original objects. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine vision

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Numerical analysis of added mass and damping of elastic hydrofoils

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Journal of Hydrodynamics 2020年第5期32卷 1009-1023页

作者： Jia-sheng Li Ye-gao Qu Hong-xing Hua School of Naval Architecture and Ocean Engineering Huazhong University of Science and TechnologyWuhan 430074China Hubei Provincial Engineering Research Center of Data Techniques and Supporting Software for Ships(DTSSS) Wuhan 430074China State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghai 200240China

A numerical model is proposed for analyzing the effects of added mass and damping on the dynamic behaviors of *** coupled fluid-structure interactions(FSIs)of hydrofoils are analyzed by using the 3-D panel method for the fluid and the finite element method for the *** added mass and damping matrices due to the external fluid of the hydrofoil are asymmetric and computational *** computational inefficiencies associated with these asymmetric matrices are overcome by using a modal reduction technique,in which the first several wet mode vectors of the hydrofoil are employed in the analysis of the FSI *** discretized system of equations of motion for the hydrofoil are solved using the Wilson-6 *** present methods are validated by comparing the computed results with those obtained from the finite element *** is found that the stationary flow is sufficient for determining the wet modes of the hydrofoil under the condition of single-phase potential flow and without phase *** the case of relatively large inflow velocity,the added damping of the fluid can significantly affect the structural responses of the hydrofoil.

关键词： Fluid-structure interaction(FSI) hydroelastic response hydrofoil added mass added damping

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Remaining Useful Life Prediction with Spatio-Temporal Graph Transform and Weakly Supervised Adversarial Network: An Application in Power Components

SSRN

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SSRN 2024年

作者： Deng, Shuhan Lan, Hao Yue, Ke Chen, Zhuyun Zhang, Xuning Huang, Ruyi Huang, Zhicong Li, Weihua Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou China School of Mechanical & Automotive Engineering South China University of Technology Guangzhou China State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangdong University of Technology Guangzhou China Department of Industrial Engineering and Decision Analytics The Hong Kong University of Science and Technology Hong Kong College of Computing and Data Science Nanyang Technological University Singapore Singapore

Predictive maintenance is crucial for improving the reliability of power components and having attracted widespread attention, for which remaining useful life (RUL) prediction is a precondition. Owing to the heterogeneity of the power components, accuracy RUL prediction through different components remains challenging. To make things worse, the structural property of the data itself is also neglected. Herein, take the insulated gate bipolar transistors (IGBTs) as the sample, a novel RUL prediction framework is proposed in this paper. In brief, a time window and Radius distance metric function is adopted to generate graph information matrix. Specifically, spatio-temporal fusion graph network (STFGN) is proposed to obtain unique IGBTs data structure information and incorporate past and future moments aging features to construct health index (HI). Then, weakly supervised adversarial learning strategy is considered to realize the cross-domain RUL prediction, which conditional domain adaptation (CDA)-based adversarial method is adopted to reduce distribution discrepancies between distinct domains and weakly supervised training strategy with some target domain labels is also used to enhance positive transfer. Moreover, the proposed method is proved by four transfer tasks and the results show that overcomes the challenge of unavailability of labeled failure samples and outperforms state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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MRI-derived radiomics models for diagnosis,aggressiveness,and prognosis evaluation in prostate cancer

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2023年第8期24卷 663-681页

作者： Xuehua ZHU Lizhi SHAO Zhenyu LIU Zenan LIU Jide HE Jiangang LIU Hao PING Jian LU Department of Urology Peking University Third HospitalBeijing 100191China CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100080China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing 100191China Key Laboratory of Big Data-Based Precision Medicine(Beihang University) Ministry of Industry and Information Technology of the People’s Republic of ChinaBeijing 100191China Department of Urology Beijing Tongren HospitalCapital Medical UniversityBeijing 100730China

Prostate cancer(PCa)is a pernicious tumor with high heterogeneity,which creates a conundrum for making a precise diagnosis and choosing an optimal treatment *** magnetic resonance imaging(mp-MRI)with anatomical and functional sequences has evolved as a routine and significant paradigm for the detection and characterization of ***,using radiomics to extract quantitative data has emerged as a promising field due to the rapid growth of artificial intelligence(AI)and image data *** acquires novel imaging biomarkers by extracting imaging signatures and establishes models for precise *** models provide a reliable and noninvasive alternative to aid in precision medicine,demonstrating advantages over traditional models based on clinicopathological *** purpose of this review is to provide an overview of related studies of radiomics in PCa,specifically around the development and validation of radiomics models using MRI-derived image *** current landscape of the literature,focusing mainly on PCa detection,aggressiveness,and prognosis evaluation,is reviewed and *** than studies that exclusively focus on image biomarker identification and method optimization,models with high potential for universal clinical implementation are ***,we delve deeper into the critical concerns that can be addressed by different models and the obstacles that may arise in a clinical *** review will encourage researchers to design models based on actual clinical needs,as well as assist urologists in gaining a better understanding of the promising results yielded by radiomics.

关键词： Magnetic resonance imaging(MRI) Radiomics Prostate cancer Predictive model

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Slimmable transformer with hybrid axial-attention for medical image segmentation

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Computers in Biology and Medicine 2024年 173卷 108370-108370页

作者： Hu, Yiyue Mu, Nan Liu, Lei Zhang, Lei Jiang, Jingfeng Li, Xiaoning College of Computer Science Sichuan Normal University Chengdu610101 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen518172 China Department of Biomedical Engineering Michigan Technological University HoughtonMI49931 United States Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu610068 China Education Big Data Collaborative Innovation Center of Sichuan 2011 Chengdu610101 China

The transformer architecture has achieved remarkable success in medical image analysis owing to its powerful capability for capturing long-range dependencies. However, due to the lack of intrinsic inductive bias in modeling visual structural information, the transformer generally requires a large-scale pre-training schedule, limiting the clinical applications over expensive small-scale medical data. To this end, we propose a slimmable transformer to explore intrinsic inductive bias via position information for medical image segmentation. Specifically, we empirically investigate how different position encoding strategies affect the prediction quality of the region of interest (ROI) and observe that ROIs are sensitive to different position encoding strategies. Motivated by this, we present a novel Hybrid Axial-Attention (HAA) that can be equipped with pixel-level spatial structure and relative position information as inductive bias. Moreover, we introduce a gating mechanism to achieve efficient feature selection and further improve the representation quality over small-scale datasets. Experiments on LGG and COVID-19 datasets prove the superiority of our method over the baseline and previous works. Internal workflow visualization with interpretability is conducted to validate our success better;the proposed slimmable transformer has the potential to be further developed into a visual software tool for improving computer-aided lesion diagnosis and treatment planning. © 2024 Elsevier Ltd

关键词： COVID-19

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Temporal Hierarchical Graph Attention Network for Traffic Prediction with Prompt Learning

Temporal Hierarchical Graph Attention Network for Traffic Pr...

引用

International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Cheng Li Pei-Yuan Lai Yu-Xuan Zhou Chang-Dong Wang Faculty of Innovation Engineering Macau University of Science and Technology Macau China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China South China Technology Commercialization Center Guangzhou China School of Information Engineering Guangdong University of Technology Guangzhou Guangdong China Beijing Normal University - Hong Kong Baptist University United International College Zhuhai China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Traffic prediction is a complex task. In the traditional forecasting methodologies, there are limitations regarding generalization, as specifically observed in the pretraining approaches. This is particularly evident in models involving temporal and spatial attributes, where finetuning often encounters numerous challenges. With the emergence of graph prompt learning, a new finetuning paradigm has gained increasing application. In the context of the traffic prediction methods involving spatio-temporal attributes, we endeavor to enhance its performance in the downstream tasks through graph pretraining and prompt-based finetuning. In particular, we propose a traffic prediction model based on graph prompt learning. This model deeply mines the hierarchical information of traffic data and its temporal attributes, by employing the method of graph pretraining and prompt learning to enhance the model’s generalizability and accuracy. Extensive experiments conducted on two real-world traffic prediction datasets confirm the effectiveness of the proposed method.

关键词： Wireless communication Accuracy Computational modeling Predictive models data models Task analysis Forecasting

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Towards Accurate Search for E-Commerce in Steel Industry: A knowledge-Graph-Based Approach 16th

Towards Accurate Search for E-Commerce in Steel Industry: A ...

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Chen, Maojian Shen, Hailun Huang, Ziyang Luo, Xiong Yin, Junluo School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China Shunde Graduate School University of Science and Technology Beijing Foshan Guangdong528399 China Ouyeel Co. Ltd. Shanghai201999 China

ISBN: (纸本)9783030675363

Mature artificial intelligence (AI) makes human life more and more convenient. However, in some application fields, it is impossible to achieve the satisfactory results only depending on the traditional AI algorithm. Specifically, in order to avoid the limitations of traditional searching strategies in e-commerce field related to steel, such as the inability to analyzing long technical sentences, we propose a collaborative decision making method in this field, through the combination of deep learning algorithms and expert systems. Firstly, we construct a knowledge graph (KG) on the basis of steel commodity data and expert database, and then train a model to accurately extract steel entities from long technical sentences, while using an advanced bidirectional encoder representation from transformers (BERT), a bidirectional long short-term memory (Bi-LSTM), and a conditional random field (CRF) approach. Finally, we develop an intelligent searching system for e-commence in steel industry, with the help of the designed KG and entity extraction model, while improving the searching performance and user experience in such system. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Decision making

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