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Edge-cloud cooperation driven surface roughness classification method for selective laser melting

Advanced engineering Informatics 2025年 66卷

作者： Ma, Shuaiyin Huang, Yuming Liu, Yang Yan, Zhiqiang Lv, Jingxiang Cai, Wei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an710121 China Shaanxi Union Research Centre of University and Enterprise for 5G+ Industrial Internet Communication Terminal Technology Xi'an University of Posts and Telecommunications Xi'an710121 China Department of Management and Engineering Linköping University LinköpingSE-581 83 Sweden Industrial Engineering and Management University of Oulu Oulu90570 Finland Key Laboratory of Road Construction Technology and Equipment of MOE Chang'an University Xi'an710064 China College of Engineering and Technology Southwest University Chongqing400715 China

Additive manufacturing (AM) technology is extensively utilized in aerospace and industrial manufacturing. However, parts built using AM are susceptible to spheroidization, porosity, cracks, and poor surface quality, making it difficult to establish an actionable product quality degree. Hence, developing a reasonable method to equate product quality with a new degree and further analyzing the product quality based on these standards has proven effective for enhancing part quality in AM. To achieve this goal, this paper proposes a surface roughness classification method that utilizes surface roughness analysis and sample enhancement. This method leverages edge cloud cooperation to efficiently analyze and integrate data from different sensors, enabling real-time monitoring and adjustment of the manufacturing process. Subsequently, the quality degree analysis system was developed utilizing matter-element extension cloud model. Furthermore, a bidirectional-gated recurrent unit (Bi-GRU) based model for quality classification and recognition has been established, with Wasserstein generative adversarial network (WGAN) employed for sample enhancement to address the issue of imbalanced column classification and to enhance the accuracy of both classification and recognition. Finally, the results obtained from this case study demonstrate through comparative experiments that the proposed method for classifying surface roughness can accurately identify 98% of prepared samples. © 2025 The Authors

关键词： Inspection

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Joint resource management for MC-NOMA: A deep reinforcement learning approach

arXiv

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arXiv 2021年

作者： Wang, Shaoyang Lv, Tiejun Ni, Wei Beaulieu, Norman C. Guo, Y. Jay Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Sydney2122 Australia Beijing Key Laboratory for Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Global Big Data Technologies Centre University of Technology Sydney UltimoNSW2007 Australia

This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware sensitivity and imperfect successive interference cancellation (SIC) are considered. We first formulate the JRM problem to maximize the weighted-sum system throughput. Then, the JRM problem is decoupled into two iterative subtasks: subcarrier assignment (SA, including user grouping) and power allocation (PA). Each subtask is a sequential decision process. Invoking a deep deterministic policy gradient algorithm, our proposed DRL-based JRM (DRL-JRM) approach jointly performs the two subtasks, where the optimization objective and constraints of the subtasks are addressed by a new joint reward and internal reward mechanism. A multi-agent structure and a convolutional neural network are adopted to reduce the complexity of the PA subtask. We also tailor the neural network structure for the stability and convergence of DRL-JRM. Corroborated by extensive experiments, the proposed DRL-JRM scheme is superior to existing alternatives in terms of system throughput and resistance to interference, especially in the presence of many users and strong inter-cell interference. DRL-JRM can flexibly meet individual service requirements of users. © 2021, CC BY.

关键词： Resource allocation

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Blockchain Assisted Federated Learning over Wireless Channels: Dynamic Resource Allocation and Client Scheduling

arXiv

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arXiv 2021年

作者： Deng, Xiumei Li, Jun Ma, Chuan Wei, Kang Shi, Long Ding, Ming Chen, Wen Poor, H. Vincent The School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Zhejiang Lab Hangzhou China Nanjing University of Science and Technology Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China Data61 CSIRO KensingtonWA6152 Australia The Department of Electronics Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Computer Engineering Princeton University NJ08544 United States

The blockchain technology has been extensively studied to enable distributed and tamper-proof data processing in federated learning (FL). Most existing blockchain assisted FL (BFL) frameworks have employed a third-party blockchain network to decentralize the model aggregation process. However, decentralized model aggregation is vulnerable to pooling and collusion attacks from the third-party blockchain network. Driven by this issue, we propose a novel BFL framework that features the integration of training and mining at the client side. To optimize the learning performance of FL, we propose to maximize the long-term time average (LTA) training data size under a constraint of LTA energy consumption. To this end, we formulate a joint optimization problem of training client selection and resource allocation (i.e., the transmit power and computation frequency at the client side), and solve the long-term mixed integer non-linear programming based on a Lyapunov technique. In particular, the proposed dynamic resource allocation and client scheduling (DRACS) algorithm can achieve a trade-off of [O(1/V), O(√V)] to balance the maximization of the LTA training data size and the minimization of the LTA energy consumption with a control parameter V. Our experimental results show that the proposed DRACS algorithm achieves better learning accuracy than benchmark client scheduling strategies with limited time or energy consumption. Copyright © 2021, The Authors. All rights reserved.

关键词： Blockchain

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What we should learn from pandemic publishing

arXiv

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arXiv 2024年

作者： Sikdar, Satyaki Venturini, Sara Charpignon, Marie-Laure Kumar, Sagar Rinaldi, Francesco Tudisco, Francesco Fortunato, Santo Majumder, Maimuna S. Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Computer Science Loyola University Chicago ChicagoIL United States Senseable City Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Mathematics "Tullio Levi-Civita" University of Padova Padova Italy Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Network Science Institute Northeastern University BostonMA United States School of Mathematics The University of Edinburgh Edinburgh United Kingdom School of Mathematics Gran Sasso Science Institute L’Aquila Italy Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children’s Hospital BostonMA United States

Since the emergence of COVID-19, discussions of ongoing pandemic-related research have accounted for an unprecedented share of media coverage and debate in the public sphere1. The urgency of the pandemic forced researchers to operate on an accelerated timeline, as both policymakers and the public relied on the most current evidence to guide their decisions and behaviors. With high demand for rapid pandemic-related insights and lower barriers to entry via preprint servers, the volume of COVID-19 articles skyrocketed2. The pressing need for research triggered the participation of many researchers with expertise in the science of infectious disease outbreaks (‘outbreak scientists’), who were joined by researchers from other disciplines (‘bellwethers’) and more junior researchers still in training (‘newcomers’) with the common goal of advancing the frontiers of pandemic science and informing policy decisions3. Please see Box 1 for details of this taxonomy. © 2024, CC BY-NC-ND.

关键词： COVID-19

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Generating High-quality Movie Tags from Social Reviews: A Learning-driven Approach

Generating High-quality Movie Tags from Social Reviews: A Le...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Zhenxiao Luo Guopin Tang Chen Wang Yipeng Zhou Xi Zheng Jessie Hui Wang Gang Liu Di Wu School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Faculty of Science and Engineering Macquarie University Sydney Australia Institute for Network Sciences and Cyberspace Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781665417631

As a kind of keywords to describe video contents, tags are extremely beneficial for viewers to locate videos when they search the site. Additionally, tags also help video platform operators to better organize and recommend videos to platform users. Previous approaches are mainly based on manually tagging or tag propagation via video content analysis, which is time-consuming and resource-consuming. Especially, given the high volume of fresh movies generated per year, it is difficult to accurately tag all the movies manually. Moreover, video content analysis is also not easy considering typical features (e.g., long duration, complicated scenarios) of commercial movies. In this paper, we propose an automatic tagging algorithm called TagRec that exploits crowdsourced user reviews to generate accurate movie tags. We observe that user reviews contain rich information about movies (e.g., quality, actors) which can be learned to generate high-quality movie tags. Inspired by the above observation, we choose to transform the movie video tagging problem into a tag recommendation problem, in which tags are recommended to different movies by extracting knowledge from crowdsourced movie reviews. We take latent topics, tag co-occurrence probability and tag semantics into account, and formulate the problem as a recommendation optimization problem. We evaluate the performance of our pro-posed TagRec algorithm with a large-scale real-world dataset. Extensive experiments demonstrate that TagRec achieves 7.1 % and 9.6% improvement compared with other state-of-the-art methods in terms of Hit Ratio and Normalized Discounted Cumulative Gain respectively.

关键词： Social computing Filtering Semantics Transforms Tagging Motion pictures Optimization

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Improving preparation and luminescence properties of Eu and dy doped Sr2MgSi2O7 phosphors by adding excess silica

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Scientific reports 2025年第1期15卷 18367页

作者： Chi Zhang Liuming Wei Qin Liu Ruirui Cui Chaoyong Deng Jingyu Li College of Electrical Engineering Henan University of Technology Zhengzhou 450001 China. chizhang@***. Department of Network Security Henan Police College Zhengzhou 450046 China. Anqiu Qingyun Bilingual School Weifang China. Key Laboratory of Functional Composite Materials of Guizhou Province College of Big Data and Information Engineering Guizhou University Guiyang 550025 China. Key Laboratory of Functional Composite Materials of Guizhou Province College of Big Data and Information Engineering Guizhou University Guiyang 550025 China. cydeng@***. Spallation Neutron Source Science Center Dongguan 523803 China. ***@***.

Solid state reaction method is most commonly used to synthesize SrMgSiO: Eu, Dy phosphors. In order to avoid the energy waste caused by high sintering temperature, we systematically studied and optimized the preparation process of SrMgSiO: Eu, Dy long afterglow phosphors by increasing the Si/Sr ratio in the raw materials. The appropriate addition of silica not only facilitates the preparation of single phase SrMgSiO: Eu, Dy phosphor but also enhances the rate of solid state reaction, so as to achieve the purpose of reducing the sintering temperature. The photoluminescence performance of the sample with a Si/Sr ratio of 1.1 sintered at 1350 °C is the highest, followed by that of the sample sintered at 1400 °C with a Si/Sr ratio of 1.0. Notably, the photoluminescence properties of the Si/Sr ratio of 1.3 sample sintered at 1300 °C are comparable to those of the Si/Sr ratio of 1.0 sample sintered at 1400 °C. However, in terms of afterglow properties, the former exhibits an enhancement by a factor of 3.5 compared to the latter. This finding suggests that strategically increasing the Si/Sr ratio can reduce the sintering temperature by 100 °C while maintaining or even enhancing the luminescence and afterglow properties of the samples. Additionally, the electronic structures of the SrMgSiO host, the doped luminescence center Eu, the oxygen vacancies and the co-doped activator Dy have been methodically investigated through first-principles calculations. By integrating the findings on the distribution of the dopants and defects levels, we predict and propose the photoluminescence and afterglow mechanisms of SrMgSiO: Eu, Dy. These studies suggest an efficient, cheap and energy-saving strategy for the preparation of SrMgSiO: Eu, Dy long afterglow phosphor, and reveal the roles of oxygen vacancies and co-doped Dy in the afterglow luminescence.

关键词： Electron traps First-principles calculation Optimizing synthesis Persistent luminescence Sr2MgSi2O7

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Adaptive Fuzzy Tracking Control with Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems

arXiv

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arXiv 2022年

作者： Mao, Bing Wu, Xiaoqun Liu, Hui Xu, Yuhua Lü, Jinhu The School of Mathematics and Statistics Wuhan University Hubei430072 China Hubei Key Laboratory of Computational Science Wuhan University Hubei430072 China Research Center of Complex Network Wuhan University Hubei430072 China The School of Automation Huazhong University of Science and Technology Hubei430074 China The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Hubei430074 China The School of Statistics and Data Science Nanjing Audit University Jiangsu211815 China The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

Adaptive fuzzy control strategies are established to achieve global prescribed performance with prescribed-time convergence for strict-feedback systems with mismatched uncertainties and unknown nonlinearities. Firstly, to quantify the transient and steady performance constraints of the tracking error, a class of prescribed-time prescribed performance functions are designed, and a novel error transformation function is introduced to remove the initial value constraints and solve the singularity problem in existing works. Secondly, based on dynamic surface control methods, controllers with or without approximating structures are established to guarantee that the tracking error achieves prescribed transient performance and converges into a prescribed bounded set within prescribed time. In particular, the settling time and initial value of the prescribed performance function are completely independent of initial conditions of the tracking error and system parameters, which improves existing results. Moreover, with a novel Lyapunov-like energy function, not only the differential explosion problem frequently occurring in backstepping techniques is solved, but the drawback of the semi-global boundedness of tracking error induced by dynamic surface control can be overcome. The validity and effectiveness of the main results are verified by numerical simulations on practical examples. Copyright © 2022, The Authors. All rights reserved.

关键词： Fuzzy control

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Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation

arXiv

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arXiv 2025年

作者： Cheng, Zihan Guo, Jintao Zhang, Jian Qi, Lei Zhou, Luping Shi, Yinghuan Gao, Yang Shanghai Jiao Tong University School of Medicine Shanghai200025 China National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210093 China State Key Laboratory for Novel Software Technology the National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210093 China School of Computer Science and Engineering Key Lab of Computer Network and Information Integration Southeast University Jiangsu Nanjing211189 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2006 Australia

To segment medical images with distribution shifts, domain generalization (DG) has emerged as a promising setting to train models on source domains that can generalize to unseen target domains. Existing DG methods are mainly based on CNN or ViT architectures. Recently, advanced state space models, represented by Mamba, have shown promising results in various supervised medical image segmentation. The success of Mamba is primarily owing to its ability to capture long-range dependencies while keeping linear complexity with input sequence length, making it a promising alternative to CNNs and ViTs. Inspired by the success, in the paper, we explore the potential of the Mamba architecture to address distribution shifts in DG for medical image segmentation. Specifically, we propose a novel Mamba-based framework, Mamba-Sea, incorporating global-to-local sequence augmentation to improve the model’s generalizability under domain shift issues. Our Mamba-Sea introduces a global augmentation mechanism designed to simulate potential variations in appearance across different sites, aiming to suppress the model’s learning of domain-specific information. At the local level, we propose a sequence-wise augmentation along input sequences, which perturbs the style of tokens within random continuous sub-sequences by modeling and resampling style statistics associated with domain shifts. To our best knowledge, Mamba-Sea is the first work to explore the generalization of Mamba for medical image segmentation, providing an advanced and promising Mamba-based architecture with strong robustness to domain shifts. Remarkably, our proposed method is the first to surpass a Dice coefficient of 90% on the Prostate dataset, which exceeds previous SOTA of 88.61%. The code is available at https://***/orange-czh/Mamba-Sea. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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Weakly Supervised Fine-Grained Image Classification via Guassian Mixture Model Oriented Discriminative Learning

Weakly Supervised Fine-Grained Image Classification via Guas...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhihui Wang Shijie Wang Shuhui Yang Haojie Li Jianjun Li Zezhou Li International School of Information Science & Engineering Dalian University of Technology China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province China School of Computer Science and Technology Hangzhou Dianzi University China Shanghai Pinlan Data Technology

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Existing weakly supervised fine-grained image recognition (WFGIR) methods usually pick out the discriminative regions from the high-level feature maps directly. We discover that due to the operation of stacking local receptive filed, Convolutional Neural network causes the discriminative region diffusion in high-level feature maps, which leads to inaccurate discriminative region localization. In this paper, we propose an end-to-end Discriminative Feature-oriented Gaussian Mixture Model (DF-GMM), to address the problem of discriminative region diffusion and find better fine-grained details. Specifically, DF-GMM consists of 1) a low-rank representation mechanism (LRM), which learns a set of low-rank discriminative bases by Gaussian Mixture Model (GMM) to accurately select discriminative details and filter more irrelevant information in high-level semantic feature maps, 2) a low-rank representation reorganization mechanism (LR2M) which resumes the space information of low-rank discriminative bases to reconstruct the low-rank feature maps. By recovering the low-rank discriminative bases into the same embedding space of highlevel feature maps, LR2M alleviates the discriminative region diffusion problem in high-level feature map and discriminative regions can be located more precisely on the new low-rank feature maps. Extensive experiments verify that DF-GMM yields the best performance under the same settings with the most competitive approaches, in CUBBird, Stanford-Cars datasets, and FGVC Aircraft.

关键词： Gaussian mixture model Correlation Feature extraction Kernel Image recognition Semantics

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Overview of AI and Communication for 6G network: Fundamentals, Challenges, and Future Research Opportunities

arXiv

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arXiv 2024年

作者： Cui, Qimei You, Xiaohu Ni, Wei Nan, Guoshun Zhang, Xuefei Zhang, Jianhua Lyu, Xinchen Ai, Ming Tao, Xiaofeng Feng, Zhiyong Zhang, Ping Wu, Qingqing Tao, Meixia Huang, Yongming Huang, Chongwen Liu, Guangyi Peng, Chenghui Pan, Zhiwen Sun, Tao Niyato, Dusit Chen, Tao Khan, Muhammad Khurram Jamalipour, Abbas Guizani, Mohsen Yuen, Chau National Engineering Research Center of Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing100876 China Department of Broadband Communication Peng Cheng Laboratory Shenzhen518055 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Fudan University Shanghai200433 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China CICT Mobile Communication Technology Co. Ltd. Beijing100020 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University Shanghai200240 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310058 China China Mobile Research Institute Beijing100053 China Huawei Technologies Shanghai201206 China College of Computing and Data Science Nanyang Technological University Singapore117583 Singapore VTT Technical Research Centre of Finland Ltd. EspooFI-02044 VTT Finland Center of Excellence in Information Assurance King Saud University Riyadh11362 Saudi Arabia School of Electrical and Information Engineering University of Sydney NSW2006 Australia Abu Dhabi United Arab Emirates School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore

With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance, particularly in intricate and dynamic environments. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on emphasizing their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, delineated across three progressive developmental stages. The first stage, AI for network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies, such as digital twins for AI and semantic communication. The final stage, AI as a Service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. Specifically, we have defined the quality of AI service, which refers to a framework for measuring AI services within the network. We further summarize the standardization process of AI for wireless networks, highlighting key milestones and ongoing efforts. In addition, we conduct an in-depth analysis of the critical challenges faced by the integration of AI and communications

关键词： Resource allocation

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