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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Wan, Jingyan Zhou, Changsheng Zhao, Xu Shanghai Jiao Tong University Laboratory of Computer Vision Department of Automation Shanghai200240 China

ISBN: (纸本)9798350372601

Texture defect detection is an essential technology in large-scale industrial abnormal detection. Currently, researchers in the defect detection field primarily calculate the anomaly scores of images and perform threshold segmentation to obtain defect detection results. This method generally yields good detection results, but precise boundaries are challenging to obtain due to the similarity of anomaly scores in the edge regions of images. Additionally, there is a growing interest in accelerating defect detection methods to meet real-world application scenarios. To address these challenges, this paper introduces salient object detection methods for texture defect detection. We define the defect regions in defect images as salient regions, effectively capturing the overall image through multiscale learning. This achieves precise localization of defects (salient objects). Moreover, data augmentation is used to artificially synthesize defect images, resolving the salient object detection method's reliance on strong supervised information and achieving self-supervised defect detection. To better distinguish features between normal and salient regions, we incorporate a contrastive learning module aimed at enhancing feature differentiation. The effectiveness of our proposed method is demonstrated by experimental results on the MVTec texture dataset, achieving a high frame rate exceeding 30 FPS on the Nvidia GeForce GTX 2080ti GPU. © 2024 IEEE.

关键词： Contrastive Learning

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Real-Time Industrial Anomaly Detection via Sparse Reconstruction

Real-Time Industrial Anomaly Detection via Sparse Reconstruc...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Zhou, Changsheng Wan, Jingyan Zhao, Xu Shanghai Jiao Tong University Laboratory of Computer Vision Department of Automation Shanghai200240 China

ISBN: (纸本)9798350372601

In the field of industrial anomaly detection, the scarcity of anomalous data and labels poses significant challenges, necessitating models that can efficiently detect and localize anomalies with minimal reliance on anomalous training data. Traditional approaches often utilize outlier detection strategies on pre-trained features, which are hampered by the inclusion of redundant and irrelevant information, leading to decreased computational efficiency and diminished performance in real-time applications. Addressing these limitations, this paper introduces a novel defect detection and localization strategy that emphasizes rapid feature reconstruction. Our methodology comprises three key components: (1) a robust pre-trained feature extractor that generates descriptive image features, (2) an innovative feature dictionary developed via dictionary learning to embed features from normal images, and (3) a dynamic feature reconstructor designed for swift reconstruction of test features utilizing the dictionary. This approach enables precise anomaly identification and localization by assessing differences between original and reconstructed features. Rigorous testing on the MVTec AD dataset - a benchmark for real-world industrial anomaly detection - validates the method's superiority, demonstrating substantial improvements in detection speed with minimal impact on accuracy. The findings suggest that this strategy holds significant promise for enhancing the efficiency and reliability of anomaly detection in industrial settings. © 2024 IEEE.

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AmplitudeArrow: On-the-Go AR Menu Selection Using Consecutive Simple Head Gestures and Amplitude Visualization

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IEEE Transactions on Visualization and computer Graphics 2025年第05期31卷 3408-3417页

作者： Tian, Yang Zhang, Youpeng Yan, Yukang Zhao, Shengdong Ma, Xiaojuan Shi, Yuanchun Guangxi University Department of Computer Science Guangxi Key Laboratory of Multimedia Communications and Network Technology China University of Rochester Department of Computer Science United States City University of Hong Kong School of Creative Media and the Department of Computer Science Hong Kong Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Tsinghua University Department of Computer Science and Technology China

Heads-up computing aims to provide synergistic digital assistance that minimally interferes with users' on-the-go daily activities. Currently, the input modalities of heads-up computing are mainly voice and finger gestures. In this work, we propose and evaluate the AmplitudeArrow (AA) technique designed for on-the-go AR menu selection to demonstrate that consecutive simple head gestures can also be an effective input modality for heads-up computing. Specifically, AA arranges menu icons into one/two row(s). To select a target icon, the user first makes their head yaw to pre-select the target icon or the column containing it and then makes their head pitch to make the arrow in the target icon expand until the arrow covers the target icon completely, i.e., the pitch amplitude surpasses the selection confirmation threshold. User studies indicated that AA demonstrated robust resistance to walking-caused head perturbation and external factors such as other people/obstacles, delivering high accuracy (error rate © 1995-2012 IEEE.

关键词： Augmented reality

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Minimizing Age of Information in UAV-Assisted Edge Computing System with Multiple Transmission Modes

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1060-1078页

作者： Yanhua Pei Yunzhi Zhao Fen Hou State Key Laboratory of IoT for Smart City and Department of Electrical and Computer Engineering University of MacauMacau 999078China

With the advance of 5G technologies and the development of space-air-ground-sea applications,the fast and efficient collection and processing of the explosive growth of sensing data have become significant and *** this paper,considering the Age of Information(AoI),the limited coverage of Base Stations(BS),and the constrained computation capability of Unmanned Aerial Vehicle(UAV),we propose a hybrid communication framework that utilizes UAVs as relays to optimize the collection of sensing *** aim to minimize the average AoI of the data among all sensor nodes while considering the energy consumption constraints of sensor nodes,which is formulated as a Mixed Integer NonLinear Programming(MINLP).To address this problem,we decompose it into communication resource allocation and computation resource ***,the average AoI of the whole system is minimized and the average energy consumption constraint of sensor nodes is *** simulation results show that our proposed method can achieve significant performance *** specific,our proposed method can reduce the average AoI by 20％,11％,and 43％compared to the three counterparts,Data Transmission Directly Algorithm(DTDA),Max Weight Algorithm(MWA),and matching algorithm,respectively.

关键词： edge computing hybrid relay Unmanned Aerial Vehicle(UAV)assisted Age of Information(AoI)

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Orbit-Orbit Interaction in Spatiotemporal Optical Vortex

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engineering 2025年第2期45卷 44-51页

作者： Jian Chen Jie Zhao Xi Shen Dewei Mo Cheng-Wei Qiu Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

While spin-orbit interaction has been extensively studied,few investigations have reported on the interaction between orbital angular momenta(OAMs).In this work,we study a new type of orbit-orbit coupling between the longitudinal OAM and the transverse OAM carried by a three-dimensional(3D)spatiotemporal optical vortex(STOV)in the process of tight *** 3D STOV possesses orthogonal OAMs in the x-y,t-x,and y-t planes,and is preconditioned to overcome the spatiotemporal astigmatism effect.x,y,and t are the axes in the spatiotemporal *** corresponding focused wavepacket is calculated by employing the Debye diffraction theory,showing that a phase singularity ring is generated by the interactions among the transverse and longitudinal vortices in the highly confined *** Fourier-transform decomposition of the Debye integral is employed to analyze the mechanism of the orbit-orbit *** is the first revelation of coupling between the longitudinal OAM and the transverse OAM,paving the way for potential applications in optical trapping,laser machining,nonlinear light-matter interactions,and more.

关键词： Spatiotemporal optical vortex Orbital angular momentum Momentum interaction Highly confined wavepacket Diffraction

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Two-time-scale Congestion Management Scheme for Microgrid Integrated Distribution Networks

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CSEE Journal of Power and Energy Systems 2023年第4期9卷 1312-1325页

作者： Wei Zhang Yinliang Xu Chuheng Tang Smart Microgrid and Renewable Technology Research Laboratory Department of Electrical and Computer EngineeringLehigh UniversityBethlehemPAUSA

Microgrids(MGs)with high penetration of dis-tributed generators may cause congestion in the distribution net-work during *** address this issue,this paper proposes a two-time-scale congestion management scheme for multiple MGs integrated distribution ***-ahead hourly-scale dynamic congestion management(DCM)is formulated as a con-strained optimization problem,which can be solved by utilizing the proposed alternating iterative method,with the privacy of both the distribution network and MGs being *** sub-hourly-scale contract energy tracking aims at fully utilizing the controllable resources of the MGs to minimize the difference of the contract and actual exchanged energy between the MG and distribution *** coordination of the proposed two timescales of management schemes,the MGs integrated distribution networks can operate economically while avoiding the probable congestion predicament with high penetration of renewable *** studies with a i3-bus system MGs integrated distribution network demonstrated this proposed approach is effective to manage the congestion problem in the distribution network,while the energy tracking approach can improve the welfare of the MGs engaged in energy contracts ***-Alternating giterativemethod,congestion management,microgrids,renewable energy integration.

关键词： Alternatinggiterativemethod congestion management microgrids renewable energy integration.

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Optimized Autonomous Computing with Trusted Resilient Data Management for IoT Emerging Networks

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IEEE Sensors Journal 2025年第11期25卷 20778-20785页

作者： Shafique, Ayesha Cheng, Benmao Zhao, Bo Alwakid, Ghadah Naif Haq, Muhammad Inaam Ul Laboratory of Intelligent IOT Technology and Applications in Universities China Jouf University College of Computer and Information Sciences Department of Computer Science Al Jowf Sakaka72341 Saudi Arabia Quanzhou University of Information Engineering School of Computer Science and Software Engineering Quanzhou362000 China

The innovative city network integrates numerous computational and physical components to develop real-time systems. These systems can capture sensor data and distribute it to end stations. Most solutions have been presented based on the centralized computing paradigm, which effectively and systematically increases data flow;however, distributed wireless technologies and heterogeneous network services continue to raise significant research problems. These challenges lower the optimization criteria and reflect communication structure around the network edges. In this research, we proposed a sustainable development for smart networks using efficient big data management with collaborative decisions for network devices. It differs from most existing work in the mentioned aspect. It applies computational lightweight intelligence for forwarding collected data using mobile collectors and reduces the congestion flow between devices on the limited bandwidth of wireless links. Moreover, the energy load is efficiently managed with edge-driven methods, and the incorporation of trusted devices ensures the integrity of the smart network. It also tackles potential communication threats smartly. Based on the experiments conducted in Network Simulator (NS-3), the proposed model enhances the efficacy of smart networks for performance metrics with reliability and effective management of resources in Internet of Things (IoT) network. © 2025 IEEE.

关键词： Mathematical models Computational modeling Intelligent sensors Data aggregation Internet of Things Security Routing Load modeling Silicon Data models

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How far are we to GPT-4V?Closing the gap to commercial multimodal models with open-source suites

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Science China(Information Sciences) 2024年第12期67卷 5-22页

作者： Zhe CHEN Weiyun WANG Hao TIAN Shenglong YE Zhangwei GAO Erfei CUI Wenwen TONG Kongzhi HU Jiapeng LUO Zheng MA Ji MA Jiaqi WANG Xiaoyi DONG Hang YAN Hewei GUO Conghui HE Botian SHI Zhenjiang JIN Chao XU Bin WANG Xingjian WEI Wei LI Wenjian ZHANG Bo ZHANG Pinlong CAI Licheng WEN Xiangchao YAN Min DOU Lewei LU Xizhou ZHU Tong LU Dahua LIN Yu QIAO Jifeng DAI Wenhai WANG State Key Laboratory for Novel Software Technology Nanjing University Shanghai AI Laboratory School of Computer Science Fudan University SenseTime Research Department of Information Engineering The Chinese University of Hong Kong Department of Electronic Engineering Tsinghua University

In this paper, we introduce InternVL 1.5, an open-source multimodal large language model(MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements.(1) Strong vision encoder: we explored a continuous learning strategy for the large-scale vision foundation model — InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs.(2) Dynamic high-resolution: we divide images into tiles ranging from 1 to 40 of 448×448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input.(3) High-quality bilingual dataset: we carefully collected a high-quality bilingual dataset that covers common scenes, document images,and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in optical character recognition(OCR) and Chinese-related tasks. We evaluate InternVL 1.5 through a series of benchmarks and comparative studies. Compared to both open-source and proprietary commercial models, InternVL 1.5 shows competitive performance, achieving state-of-the-art results in 8 of 18 multimodal benchmarks. Code and models are available at https://***/OpenGVLab/InternVL.

关键词： multimodal model open-source vision encoder dynamic resolution bilingual dataset

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Human-Inspired Anticipatory Motion Planning for Robotic Manipulators in Contact Tasks

Human-Inspired Anticipatory Motion Planning for Robotic Mani...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Slim, Malak Daher, Naseem Elhajj, Imad H. American University of Beirut Vision and Robotics Lab Electrical and Computer Engineering Department Beirut Lebanon

ISBN: (纸本)9781665481090

In this work, we present a novel solution aimed at improving robotic manipulators' performance in contact tasks. Inspired by the human motor control system, which relies on a feedforward mechanism to anticipate and plan movements based on the physical properties of the target environment, our approach plans the robot's motion during the reaching phase, prior to contact. To validate our approach, we conducted experiments using the KUKA youBot arm in two distinct environments, represented by soft and hard materials. Results showed that the robot exhibited compliant behavior, with an average reduction of 71% in overshoot, 60% in rise-time, and 68% steady-state error of the force control response during contact. © 2024 IEEE.

关键词： Motion planning

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GCA-Net: Global Cross Aware Network for salient object detection 23

GCA-Net: Global Cross Aware Network for salient object detec...

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8th International Conference on Multimedia and Image Processing, ICMIP 2023

作者： Zhang, Zixuan Shi, Fan Geng, Xinbo Tao, Yi He, Jing Key Laboratory of Computer Vision and System Ministry of Education School of Computer Science and Engineering Tianjin University of Technology China

ISBN: (纸本)9781450399586

In recent years, significant progress has been made in salient object detection. Nevertheless, there remains a need for further improvements in the effective combination of local and global perspectives. Combining global perception with the local focus can enhance the ability to capture the integrity of protruding objects while achieving accurate segmentation. In this paper, we introduce a newly proposed salient object detection network named GCA-Net, which is built upon three essential components. Firstly, we utilize atrous convolution to effectively aggregate contextual information and refine high-level semantic information via spatial pooling. Secondly, we employ 1-dim crossover operations to achieve global perception while minimizing computational effort. Lastly, We have innovatively integrated a contour-aware loss into our salient object detection task to constrain our model's predictions, leading to more accurate segmentation. After evaluating our model on the established salient object detection benchmark dataset, we conducted extensive experiments to demonstrate the exceptional performance of our method. As a result, our method achieved 1st in both Sm and MAE. © 2023 ACM.

关键词： Semantics

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