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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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Efficient EK-means:Extended K-means Clustering for Categorical data with High Processing Speed

Efficient EK-means:Extended K-means Clustering for Categoric...

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作者： Chen xi Peng Shuo HunanProvincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of computer and communication engineeringChangsha University of Science&technology

The typical representative of the hard clustering algorithm,K-means,is one of the fastest processing algorithms with good ***,it cannot deal with categorical attributes,which is one of the important indicators to measure the pros and *** to the lack of processing capabilities on categorical attributes,k-means has a large limit on data processing *** paper proposes a clustering algorithm extends from *** algorithm introduces the concept of a Pseudo-mean distance calculation formula and a counting-table so that categorical attributes can be processed while reducing the time cost as much as *** results illustrate the proposed Pseudo-means can extend the processing range of k-means to category-type data,and the counting table also effectively reduces the time cost.

关键词： Cluster Efficient EK-means:Extended K-means Clustering for Categorical data with High Processing Speed

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Grafting Transformer on Automatically Designed Convolutional Neural Network for Hyperspectral Image Classification

arXiv

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arxiv 2021年

作者： Xue, xizhe Zhang, Haokui Fang, Bei Bai, Zongwen Li, Ying The School of Computer Science National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xi’an China Harbin Institute of Technology Shenzhen China The Key Laboratory of Modern Teaching Technology Ministry of Education Shaanxi Normal University Xi’an China The School of Physics and Electronic Information Yan'an University Yan’an China

Hyperspectral image (HSI) classification has been a hot topic for decides, as hyperspectral images have rich spatial and spectral information and provide strong basis for distinguishing different land-cover objects. Benefiting from the development of deep learning technologies, deep learning based HSI classification methods have achieved promising performance. Recently, several neural architecture search (NAS) algorithms have been proposed for HSI classification, which further improve the accuracy of HSI classification to a new level. In this paper, NAS and Transformer are combined for handling HSI classification task for the first time. Compared with previous work, the proposed method has two main differences. First, we revisit the search spaces designed in previous HSI classification NAS methods and propose a novel hybrid search space, consisting of the space dominated cell and the spectrum dominated cell. Compared with search spaces proposed in previous works, the proposed hybrid search space is more aligned with the characteristic of HSI data, that is, HSIs have a relatively low spatial resolution and an extremely high spectral resolution. Second, to further improve the classification accuracy, we attempt to graft the emerging transformer module on the automatically designed convolutional neural network (CNN) to add global information to local region focused features learned by CNN. Experimental results on three public HSI datasets show that the proposed method achieves much better performance than comparison approaches, including manually designed network and NAS based HSI classification methods. Especially on the most recently captured dataset Houston University, overall accuracy is improved by nearly 6 percentage points. Code is available at: https://***/Cecilia-xue/HyT-NAS. © 2021, CC BY.

关键词： Convolutional neural networks

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VFGCN: A Vertical Federated Learning Framework With Privacy Preserving for Graph Convolutional Network

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wang, Gang Li, Qingming Liu, ximeng Yan, xiaoran Dong, Qingkuan Wu, Huiwen Kong, xiangjie Zhou, Li Zhejiang Lab HangZhou310000 China Zhejiang University College of Computer Science and Technology Hangzhou310058 China Fuzhou University Key Laboratory of Information Security of Network Systems College of Computer and Big Data Fuzhou350108 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Dalian University of Technology School of Software Dalian116620 China

Due to the robust representational capabilities of graph data, employing graph neural networks for its processing has demonstrated superior performance over conventional deep learning algorithms. Graph data encompasses abundant features and structural information;however, its large-scale collection is often challenging in practice. This difficulty arises because data predominantly exists in isolated compartments, making it arduous to harmonize information across various organizations or to enable multiple organizations to collaborate effectively while safeguarding local data privacy. In light of an extreme data distribution scenario, where each client possesses distinct nodes with partially overlapping segments yet divergent data features, we introduce a dual-cloud server architecture. This framework encompasses the design of four secure subprotocols: ReEnc (secure re-encryption), SecPSI (secure outsourcing of PSI), SecWeight (secure weight calculation), and SecAgg (secure aggregation). Together, these components facilitate a vertical federated learning framework for graph convolutional networks, ensuring privacy preservation. We provide a security proof for the entire system and extensive evaluation on three benchmark datasets (Cora, Citeseer, and Pubmed) illustrates that our Vertical Federated Graph Convolutional Network (VFGCN) surpasses existing privacy-preserving methodologies. © 2004-2012 IEEE.

关键词： Privacy by design

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Machine Memory Intelligence: Inspired by Human Memory Mechanisms

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Engineering 2025年

作者： Qinghua Zheng Huan Liu xiaoqing Zhang Caixia Yan xiangyong Cao Tieliang Gong Yong-Jin Liu Bin Shi Zhen Peng xiaocen Fan Ying Cai Jun Liu School of Computer Science and Technology Xi’an Jiaotong University Xi’an 710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an 710049 China Translational Research Institute of Brain and Brain-Like Intelligence Shanghai Fourth People’s Hospital Shanghai 200434 China School of Medicine Tongji University Shanghai 200092 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China Department of Computer Science Iowa State University Ames IA 50011 USA

Large models, exemplified by ChatGPT, have reached the pinnacle of contemporary artificial intelligence (AI). However, they are plagued by three inherent drawbacks: excessive training data and computing power consumption, susceptibility to catastrophic forgetting, and a deficiency in logical reasoning capabilities within black-box models. To address these challenges, we draw insights from human memory mechanisms to introduce “machine memory,” which we define as a storage structure formed by encoding external information into a machine-representable and computable format. Centered on machine memory, we propose the brand-new machine memory intelligence (M 2 I) framework, which encompasses representation, learning, and reasoning modules and loops. We explore the key issues and recent advances in the four core aspects of M 2 I, including neural mechanisms, associative representation, continual learning, and collaborative reasoning within machine memory. M 2 I aims to liberate machine intelligence from the confines of data-centric neural networks and fundamentally break through the limitations of existing large models, driving a qualitative leap from weak to strong AI.

关键词： Machine memory intelligence Neural mechanism Associative representation Continual learning Collaborative reasoning

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On the connectivity of highly dynamic wireless sensor networks in smart factory

On the connectivity of highly dynamic wireless sensor networ...

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2019 International Conference on Networking and Network Applications, NaNA 2019

作者： Gao, Cong Wang, Zhongmin Chen, Yanping School of Computer Science and Technology Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an China Xi'An University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9781728126296

With the development of smart manufacturing in Industry 4.0, large amount of heterogeneous data are generated from multiple sources. Various data processing techniques can be applied to these data for the purpose of extracting potential information about the status of the manufacturing network. Thus, data are valuable to the analysis and adjustment of the manufacturing processes. Due to the diversity of products, assembly lines and manufacturing processes are highly dynamic. In view of the flexible deployment of wireless sensor networks, they have been widely deployed to conduct data collection. However, due to the limited radio range of a wireless sensor node, the connectivity of a highly dynamic wireless sensor network is a key issue. In this paper, we analyzed the connectivity in a wireless sensor network and accorded three types of connectivity. Then, we developed two network connectivity models from the perspective of one dimension and two dimensions. The degrees of connectivity for both two nodes and the network are elaborated. Finally, the relation between the number of sink nodes and the average degree of connectivity of the network is analyzed based on experimental results. © 2019 IEEE.

关键词： data handling

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Meta Mask Correction for Nuclei Segmentation in Histopathological Image

Meta Mask Correction for Nuclei Segmentation in Histopatholo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiangbo Shi Chang Jia Zeyu Gao Tieliang Gong Chunbao Wang Chen Li National Engineering Lab for Big Data Analytics Xi’an Jiao tong University Xi’an Shaanxi China School of Computer Science and Technology Xi’an Jiao tong University Xi’an Shaanxi China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech.R&D Xi’an Jiao tong University Xi’an Shaanxi China the First Affiliated Hospital of Xi’an Jiaotong University Xi’an Shaanxi China

ISBN: (纸本)9781665429825

Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks using a small amount of clean meta-data. Then the corrected masks can be used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model in an end-to-end way. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. It even achieves comparable performance with the model training on supervised data in some noisy settings.

关键词： Training Learning systems Image segmentation Pathology Annotations Biological system modeling Optimization methods

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RadioGAT: A Joint Model-based and data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks

arXiv

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arxiv 2024年

作者： Li, xiaojie Zhang, Songyang Li, Hang Li, xiaoyang Xu, Lexi Xu, Haigao Mei, Hui Zhu, Guangxu Qi, Nan xiao, Ming Shenzhen Research Institute of Big Data The Chinese University of Hong Kong-Shenzhen Guangdong China College of Physics Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA70503 United States Research Institute China United Network Communications Corporation Beijing100048 China China Mobile Communications Group Jiangxi Co. Ltd 330038 China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing University of Aeronautics and Astronautics Nanjing210016 China State Key Laboratory of Integrated Services Networks Xidian University Xi'An710071 China Department of Information Science and Engineering School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden

Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning. However, traditional machine-learning-based MB-RMR methods, which rely heavily on simulated data or complete structured ground truth, face significant deployment challenges. These challenges stem from the differences between simulated and actual data, as well as the scarcity of real-world measurements. To address these challenges, our study presents RadioGAT, a novel framework based on Graph Attention Network (GAT) tailored for MBRMR within a single area, eliminating the need for multiregion datasets. RadioGAT innovatively merges model-based spatial-spectral correlation encoding with data-driven radiomap generalization, thus minimizing the reliance on extensive data sources. The framework begins by transforming sparse multiband data into a graph structure through an innovative encoding strategy that leverages radio propagation models to capture the spatial-spectral correlation inherent in the data. This graph-based representation not only simplifies data handling but also enables tailored label sampling during training, significantly enhancing the framework's adaptability for deployment. Subsequently, The GAT is employed to generalize the radiomap information across various frequency bands. Extensive experiments using raytracing datasets based on real-world environments have demonstrated RadioGAT's enhanced accuracy in supervised learning settings and its robustness in semi-supervised scenarios. These results underscore RadioGAT's effectiveness and practicality for MBRMR in environments with limited data availability. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Crowdsourcing Answer Integration Algorithm For big data Environment

Crowdsourcing Answer Integration Algorithm For Big Data Envi...

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International Conference on Networking and Network Applications (NaNA)

作者： Hong xia YongKang Zhang Han Wang YanPing Chen ZhongMin Wang Shannxi Key Laboratory of Network Data Analysis and Intelligent Progcessing Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9781728189543

ISBN: (纸本)9781728189550

Crowdsourcing is an emerging distributed computing model that is widely used. Aiming at the uneven quality of crowdsourcing answers due to different workers' capabilities and attitudes, it is necessary to effectively study the hotspot issue of crowdsourcing answer integration. A crowdsourced answer integration algorithm based on “filter-evaluate-vote” is proposed. This algorithm is implemented using MapReduce parallel programming model in the Hadoop platform, and experiments are performed on multiple data sets. The results show that the proposed algorithm can be effective. It improves the accuracy of crowdsourced answers, and has high computing performance and horizontal scalability, which is suitable for answer integration in a big data environment.

关键词： Crowdsourcing Computational modeling Scalability big data Quality assessment Time factors Task analysis

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Cooperative Game Model of Delegation Computing: Verifier Separated from Calculators

Cooperative Game Model of Delegation Computing: Verifier Sep...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Duo Zhang Youliang Tian Linjie Wang Sheng Gao Jianfeng Ma State Key Laboratory of Public Big Data College of Computer Science and Technology College of Mathematics and Statistics Guizhou University Guiyang China State Key Laboratory of Public Big Data College of Computer Science and Technology Institute of Cryptography and Date Security Guizhou University Guiyang China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Information Central University of Finance and Economics Beijing China School of Cyber Engineering College of Computer Science and Technology Xi'dian University Xi'an China

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186962

In cloud computing, the delegation computation service is required because end users usually are resource-constrained, and at the same time the correctness of computing results needs to be verified. However, all the existing and available technologies have a high cost to verify which is the main problem faced by the delegation computing under the cloud platform. In order to address this issue, based on the game theory and a smart contract, we construct three protocols. The client and the calculator sign the Prisoner's Protocol to incentivize correct computation by asking the calculator to pay a deposit upfront; The client and the verifier sign the Long-acting Mechanism Protocol to ensure the validity of the verification results; The calculator and the verifier sign the Collusion Protocol to make collusion the most profitable strategy for all colluding parties. Through the combination of these protocols, the work is realized by separation the computing task and the verification task, and the heavy verification task is avoided. Finally, the performance analysis of the results shows that the combination of all the protocols not only solves the problem of verification complexity in the traditional delegation computing, but also guarantees the benefit of the honest player.

关键词： Calculators Contracts Protocols Task analysis Computational modeling Cloud computing Security

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