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Defect detection of gear parts in virtual manufacturing

Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 76-87页

作者： Zhenxing Xu Aizeng Wang Fei Hou Gang Zhao School of Mechanical Engineering&Automation Beihang UniversityBeijing 100191China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

Gears play an important role in virtual manufacturing systems for digital twins;however,the image of gear tooth defects is difficult to acquire owing to its non-convex *** this study,a deep learning network is proposed to detect gear defects based on their point cloud *** approach mainly consists of three steps:(1)Various types of gear defects are classified into four cases(fracture,pitting,glue,and wear);A 3D gear dataset was constructed with 10000 instances following the aforementioned classification.(2)Gear-PCNet++introduces a novel Combinational Convolution Block,proposed based on the gear dataset for gear defect detection to effectively extract the local gear information and identify its complex topology;(3)Compared with other methods,experiments show that this method can achieve better recognition results for gear defects with higher efficiency and practicability.

关键词： Defect detection Gear surface Gear dataset Combinational Convolution Block

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Ambulance Location Optimization via Fine-grained Emergency Demand Forecasting 1

Ambulance Location Optimization via Fine-grained Emergency D...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Gao, Xin Zhu, Zhengye Wang, Yasha Zhao, Junfeng Wu, Rengyu Zhou, Qiang School of Computer Science Peking University Beijing China Peking University National Engineering Research Center for Software Engineering Beijing China Shenzhen Center for Prehospital Care Shenzhen China

ISBN: (纸本)9798350358780

Pre-hospital emergency care plays a pivotal role in contemporary emergency medical service systems, as it is paramount for safeguarding the lives and well-being of patients. However, the efficacy of historical emergency resource allocation schemes wanes over time, owing to the impact of urban development and evolving societal dynamics. Consequently, timely adjustments and optimization of emergency resource allocation schemes become imperative to uphold the unblemished efficiency of emergency response. This study delves into the inadequacies of existing optimization endeavors pertaining to pre-hospital emergency resource allocation, encompassing issues such as rudimentary granularity in emergency demand prediction, subpar precision, impractical constraints, and unwarranted costs within the resource optimization process. To surmount these challenges, we propose a sophisticated algorithm for fine-grained emergency resource prediction and a pragmatic and viable algorithm for emergency resource optimization. Empirical findings obtained from an extensive analysis of a pre-hospital emergency dataset from Shenzhen corroborate that our methods diligently optimize the prevailing resource allocation scenario in the city, thereby ensuring a seamless and efficient pre-hospital emergency response. © 2023 IEEE.

关键词： Hospitals

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Deep Reinforcement Learning for Joint Service Placement and Request Scheduling in Mobile Edge Computing Networks 28

Deep Reinforcement Learning for Joint Service Placement and ...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Deng, Yuxuan Li, Xiuhua Sun, Chuan Hao, Jinlong Wang, Xiaofei Leung, Victor C. M. School of Big Data and Software Engineering Chongqing University Chongqing China Chongqing Changan Automobile Software Technology Co. Ltd Chongqing China TKLAN College of Intelligence and Computing Tianjin University Tianjin China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350300482

Mobile edge computing aims to provide cloud-like services on edge servers located near Mobile Devices (MDs) with higher Quality of Service (QoS). However, the mobility of MDs makes it difficult to find a global optimal solution for the coupled service placement and request scheduling problem. To address these issues, we consider a three-Tier MEC network with vertical and horizontal cooperation. Then we formulate the joint service placement and request scheduling problem in a mobile scenario with heterogeneous services and resource limits, and convert it into two Markov decision processes to decouple decisions across successive time slots. We propose a Cyclic Deep Q-network-based Service placement and Request scheduling (CDSR) framework to find a long-Term optimal solution despite future information unavailability. Specifically, to solve the issue of enormous action space, we decompose the system agent and train them cyclically. Evaluation results demonstrates the effectiveness of our proposed CDSR on user-perceived QoS. © 2023 IEEE.

关键词： Reinforcement learning

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A Nature-Inspired AI Framework for Accurate Glaucoma Diagnosis

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computer Modeling in engineering & sciences 2025年第4期143卷 539-567页

作者： Jahanzaib Latif Ahsan Wajahat Alishba Tahir Anas Bilal Mohammed Zakariah Abeer Alnuaim School of Software Northwestern Polytechnical UniversityXi’an710072China Shifa College of Medicine Shifa Tameer-e-millat UniversityIslamabad44000Pakistan College of Information Science and Technology Hainan Normal UniversityHaikou571158China Department of Computer Science and Engineering College of Applied Studies and Community ServiceKing Saud UniversityRiyadh11495Saudi Arabia

Glaucoma,a leading cause of blindness,demands early detection for effective *** AI-based diagnostic systems are gaining traction,their performance is often limited by challenges such as varying image backgrounds,pixel intensity inconsistencies,and object size *** address these limitations,we introduce an innovative,nature-inspired machine learning framework combining feature excitation-based dense segmentation networks(FEDS-Net)and an enhanced gray wolf optimization-supported support vectormachine(IGWO-SVM).This dual-stage approach begins with FEDS-Net,which utilizes a fuzzy integral(FI)technique to accurately segment the optic cup(OC)and optic disk(OD)from retinal images,even in the presence of uncertainty and *** the second stage,the IGWO-SVM model optimizes the SVM classification process,leveraging a gray wolf-inspired optimization strategy to fine-tune the kernel function for superior *** testing on three benchmark glaucoma image databases DRIONS-DB,Drishti-GS,and Rim-One-r3 demonstrates the efficacy of our method,achieving classification accuracies of 97.65%,94.88%,and 93.2%,*** results surpass existing state-of-the-art techniques,offering a promising solution for reliable and early glaucoma detection.

关键词： Glaucoma fundus images machine learning nature-inspired algorithm segmentation classification

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FlexWalker: An Efficient Multi-Objective Design Space Exploration Framework for HLS Design

FlexWalker: An Efficient Multi-Objective Design Space Explor...

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International Conference on Field Programmable Logic and Applications

作者： Zheyuan Zou Cheng Tang Lei Gong Chao Wang Xuehai Zhou School of Computer Science and Technology University of Science and Technology of China School of Software Engineering University of Science and Technology of China

ISBN: (数字)9798331530075

ISBN: (纸本)9798331530082

The HLS toolchain effectively reduces the design complexity of FPGA hardware accelerators. However, in scenarios involving the multi-objective optimization of large-scale HLS designs, determining the knob configurations of Pareto design points remains a challenging task for designers. Our work re-evaluates the key factors affecting the efficiency of multiobjective design space exploration in HLS design and proposes an efficient framework named FlexWalker. It utilizes the upper confidence bound algorithm to organize various heterogeneous regression models for predicting the quality of HLS designs with different knob configurations in the design space and introduces a probability sampling algorithm and an elastic Pareto frontier to counteract the negative impact of regression model errors. Experimental results show that our work can stably eliminate over 90% of non-Pareto frontier design points in the tested HLS design space, effectively enhancing the efficiency of multiobjective design space exploration.

关键词： Predictive models Prediction algorithms Space exploration Complexity theory Logic Optimization Hardware acceleration Field programmable gate arrays

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Leveraging Blockchain-as-a-Certificate Authority for Authentication in 6G-Enabled Spatial Crowdsourcing Drone Services 100

Leveraging Blockchain-as-a-Certificate Authority for Authent...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Akram, Junaid Anaissi, Ali Sidana, Sagar Kumar, Deepak Jhaveri, Rutvij H. The University of Sydney School of Computer Science CamperdownNSW2008 Australia University of Technology Sydney Td School UltimoNSW2007 Australia McKinsey & Company Principal Software Engineer Dallas United States National Institute of Technology Meghalaya Department of Computer Science and Engineering India Pandit Deendayal Energy University School of Technology India

ISBN: (纸本)9798331517786

The integration of the Internet of Drone Things (IoDT) with spatial crowdsourcing, enhanced by 6G technology, has revolutionized environmental monitoring, particularly in managing Australian bushfires. This approach leverages drones' mobility, multidimensional motion, and ease of deployment to gather real-time data from hazardous or inaccessible areas. However, the unsecured wireless communication channels and limited computational resources of drones in typical IoDT scenarios make them susceptible to cyber-attacks, including spoofing, GPS manipulation, impersonation, man-in-the-middle, and hijacking. To counter these threats, we propose a robust security protocol that utilizes blockchain technology augmented by Hyperelliptic Curve Cryptography (HECC). By employing blockchain as a Certificate Authority (CA) and treating transactions as certifications, our framework, DronCert, eliminates the need for traditional CAs or Trusted Third Parties (TTP). This decentralized approach, combined with the high-speed, low-latency capabilities of 6G, significantly enhances data transmission security within the IoDT network. A comprehensive security analysis demonstrates DronCert's resilience against various attacks, such as Denial-of-Service (DoS), man-in-the-middle, replay, and unauthorized device representation. © 2024 IEEE.

关键词： Drones

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Dual-branch Feature Interaction Network with Structure Information Learning for Retinopathy of Prematurity Classification

Dual-branch Feature Interaction Network with Structure Infor...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lei, Haijun Zhao, Jia Xie, Hai Liu, Yaling Zhang, Guoming Lei, Baiying Shenzhen University College of Computer Science & Software Engineering Shenzhen China Shenzhen University School of Biomedical Engineering Shenzhen China Shenzhen University Shenzhen Eye Hospital Shenzhen China

ISBN: (纸本)9798350337488

Diagnosing retinopathy of prematurity (ROP) is a time-consuming and complex task, even for experienced clinicians, as it is challenging to determine its specific stages accurately. In this study, we propose an advanced dual-branch feature interaction network for predicting the stages of ROP using color fundus photographs. Specifically, the proposed network includes a Vision Transformer (ViT) branch and a convolutional neural network (CNN) branch, which are used to capture global contextual information and express local detail features, respectively. To improve the efficiency of ViT, we introduce a cascaded group attention (CGA) module feeding attention heads with different splits of the full feature, which not only saves computation cost but also improves attention diversity. The semantic features extracted from both the Transformer and CNN branches are fused through the branch feature interaction (BFI) module, allowing us to leverage the unique characteristics of both branches to optimize ROP feature representations comprehensively. Afterwards, we further design a Transformer block with structure information learning (SIL) to gather ROP-related semantic information from high-level features, gradually constructing ROP feature information structure to highlight important regions and improve the model's discriminative ability for different lesion feature structures. Our extensive experiments on both clinical and public datasets produce promising results, showcasing the outstanding performance of our method. © 2023 IEEE.

关键词： dual-branch feature interaction ROP diagnosis structure information learning

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An effective fault localization approach for Verilog based on enhanced contexts

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Frontiers of computer science 2024年第5期18卷 223-225页

作者： Zhuo ZHANG Ya LI Lei XIA Jianxin XUE Jiang WU Xiaoguang MAO School of Computer Science and Engineering Xi'an University of TechnologyXi'an 710048China Ningbo Artificial Intelligence Institute Shanghai Jiao Tong UniversityNingbo 315000China No.83 Army Joint and Truma Disease Treatment Centre of PLA Xinxiang 453000China Department of Software Engineering Shanghai Second Polytechnic UniversityShanghai 201209China College of Computer National University of Defense TechnologyChangsha 410073China

New computer architecture innovationswith diverse functionalities and comprehensive features continue to emerge incessantly,resulting in a rising trend of incorporating a larger number of circuit devices into these products[1].In the case of a sophisticated and expansive integrated circuit chip,the presence of defective or malfunctioning components can significantly impact the overall performance of the *** situation may even result in costly repercussions.

关键词： Verilog computer circuit.

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Aster: Encoding Data Augmentation Relations into Seed Test Suites for Robustness Assessment and Fuzzing of Data-Augmented Deep Learning Models 23

Aster: Encoding Data Augmentation Relations into Seed Test S...

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23rd IEEE International Conference on software Quality, Reliability, and Security, QRS 2023

作者： Wang, Haipeng Wei, Zhengyuan Zhou, Qilin Jiang, Bo Chan, W.K. City University of Hong Kong Hong Kong Beihang University State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beijing China

ISBN: (纸本)9798350319583

Data-augmented deep learning models are widely used in real-world applications. However, many state-of the-art loss-based or coverage-based fuzzing techniques fail to produce fuzzing samples for them from many seeds. This paper proposes Aster, a novel technique to address this problem to enhance their fuzzing effectiveness for deep learning models trained with multi-sample data augmentation methods. Aster formulates a novel reachability-based strategy to encode the insights of every seed's direct and indirect data augmentation relation instances into the replacement seed of that seed systematically. Our experiment shows that Aster is highly effective. On average, loss-based and coverage-based fuzzing techniques can generate 166% and 110% more fuzzing samples and reduce 31% and 22% unsuccessful seeds, respectively, after adopting the replacement seeds generated by Aster to replace their original seeds. Their improved models also become up to 55% and 40% on average more robust against FGSM and PGD attacks in the experiment. © 2023 IEEE.

关键词： Encoding (symbols)

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Dual Graph Multitask Framework for Imbalanced Delivery Time Estimation 28th

Dual Graph Multitask Framework for Imbalanced Delivery Time...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Zhang, Lei Wang, Mingliang Zhou, Xin Wu, Xingyu Cao, Yiming Xu, Yonghui Cui, Lizhen Shen, Zhiqi School of Software Shandong University Jinan China Shandong University Jinan China Alibaba Group Hangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9783031306778

Delivery Time Estimation (DTE) is a crucial component of the e-commerce supply chain that predicts delivery time based on merchant information, sending address, receiving address, and payment time. Accurate DTE can boost platform revenue and reduce customer complaints and refunds. However, the imbalanced nature of industrial data impedes previous models from reaching satisfactory prediction performance. Although imbalanced regression methods can be applied to the DTE task, we experimentally find that they improve the prediction performance of low-shot data samples at the sacrifice of overall performance. To address the issue, we propose a novel Dual Graph Multitask framework for imbalanced Delivery Time Estimation (DGM-DTE). Our framework first classifies package delivery time as head and tail data. Then, a dual graph-based model is utilized to learn representations of the two categories of data. In particular, DGM-DTE re-weights the embedding of tail data by estimating its kernel density. We fuse two graph-based representations to capture both high- and low-shot data representations. Experiments on real-world Taobao logistics datasets demonstrate the superior performance of DGM-DTE compared to baselines. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graph neural networks

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