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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Dongxiao Hu Dapeng Lan Yu Liu Jiahong Ning Jia Wang Yun Yang Zhibo Pang School of Advanced Technology Xi’an Jiaotong-Liverpool University China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Department of Science and Technology Linköping University Sweden Dalian Marine University China National Pilot School of Software Yunnan University China Department of Intelligent Systems KTH Royal Institute of Technology Sweden

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

Embodied AI represents a crucial step towards achieving Artificial General Intelligence (AGI). The next paradigm of Embodied AI involves physical embodiment, enhanced perception capabilities, and adaptive automation. This advances the field significantly, paving the way for broader expansion. Despite the significant progress, existing computing frameworks, like local computation or cloud computing, struggle to meet the substantial demands of Embodied AI. The Cloud-Fog Embodied framework, namely based on CFA (cloud-fog automation) offers a promising solution to address these challenges. Our goal is to drive integration across multiple domains, including AI, robotics and industrial production, to tackle multifaceted challenges and seize opportunities to achieve AGI in the future.

关键词： Industrial electronics Cloud computing Automation Service robots Artificial general intelligence Buildings Production

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Interactive 3D Medical Image Segmentation with SAM 2

arXiv

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arXiv 2024年

作者： Shen, Chuyun Li, Wenhao Shi, Yuhang Wang, Xiangfeng Huang, Yifei School of Computer Science and Technology East China Normal University Shanghai200062 China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai200092 China Shanghai United Imaging Healthcare Advanced Technology Research Institute Co. Ltd Shanghai201807 China School of Software Engineering East China Normal University Shanghai200062 China

Interactive medical image segmentation (IMIS) has shown significant potential in enhancing segmentation accuracy by integrating iterative feedback from medical professionals. However, the limited availability of enough 3D medical data restricts the generalization and robustness of most IMIS methods. The Segment Anything Model (SAM), though effective for 2D images, requires expensive semi-auto slice-by-slice annotations for 3D medical images. In this paper, we explore the zero-shot capabilities of SAM 2, the next-generation Meta SAM model trained on videos, for 3D medical image segmentation. By treating sequential 2D slices of 3D images as video frames, SAM 2 can fully automatically propagate annotations from a single frame to the entire 3D volume. We propose a practical pipeline for using SAM 2 in 3D medical image segmentation and present key findings highlighting its efficiency and potential for further optimization. Concretely, numerical experiments on the BraTS2020 and the medical segmentation decathlon datasets demonstrate that SAM 2 still has a gap with supervised methods but can narrow the gap in specific settings and organ types, significantly reducing the annotation burden on medical professionals. Our code will be open-sourced and available at https://***/Chuyun-Shen/SAM_2_Medical_3D. Copyright © 2024, The Authors. All rights reserved.

关键词： Medical imaging

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Sea Level Anomalies Prediction Via Two-Stage Spatiotemporal Transformer

Sea Level Anomalies Prediction Via Two-Stage Spatiotemporal ...

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IEEE International Conference on Networking, Sensing and Control

作者： Shaoteng Fang Yuehao Zhao Kefan Wang Chenyu Tian Qi Kang Department of Control Science and Engineering Tongji University Shanghai China The School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney Sydney Australia Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China School of Software Technology Dalian University of Technology Dalian China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

This study successfully applies the spatiotemporal transformer-based model to sea surface height anomaly prediction task. An improved ResNet autoencoder, named SW-GD Autoencoder, is proposed based on Sliced-Wasserstein distance and Gradient Difference loss functions to extract spatial feature information. This autoencoder demonstrates a reduced complexity and training difficulty compared to GAN-based architectures while maintaining high data generation quality. Additionally, the VidHRFormer model is employed for inference, introducing a large receptive field spatial feed-forward network module that enhances information transmission between adjacent patches in the feature map, thereby improving the observability of prediction results. To simplify training, we used a two-stage approach: first training the autoencoder separately, then combining it with VidHRFormer for final training. Compared to state-of-the-art ConvLSTM models, our method yields superior results.

关键词： Training Adaptation models Accuracy Predictive models Transformers Feature extraction Spatiotemporal phenomena Sensors Sea surface temperature Sea level

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Parallel cognition:hybrid intelligence for human-machine interaction and management

引用

Frontiers of Information technology & Electronic Engineering 2022年第12期23卷 1765-1779页

作者： Peijun YE Xiao WANG Wenbo ZHENG Qinglai WEI Fei-Yue WANG State Key Laboratory for Management and Control of Complea Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China QingdaoAcademy of Intelligent Industries Qingdao 266109China School of Software Engineering Xi'an Jiaotong UniversityXi'an 710049China Macao Institute of System Engineering Macao University of Science and TechnologyMacao 999078China

As an interdisciplinary research approach,traditional cognitive science adopts mainly the experiment,induction,modeling,and validation *** models are sometimes not applicable in cyber-physical-socialsystems (CPSSs),where the large number of human users involves severe heterogeneity and *** reduce the decision-making conflicts between people and machines in human-centered systems,we propose a new research paradigm called parallel cognition that uses the system of intelligent techniques to investigate cognitive activities and functionals in three stages:descriptive cognition based on artificial cognitive systems (ACSs),predictive cognition with computational deliberation experiments,and prescriptive cognition via parallel behavioral *** make iteration of these stages constantly on-line,a hybrid learning method based on both a psychological model and user behavioral data is further proposed to adaptively learn an individual’s cognitive *** experiments on two representative scenarios,urban travel behavioral prescription and cognitive visual reasoning,indicate that our parallel cognition learning is effective and feasible for human behavioral prescription,and can thus facilitate human-machine cooperation in both complex engineering and social systems.

关键词： Cognitive learning Artificial intelligence Behavioral prescription

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Younger: The First Dataset for Artificial Intelligence-Generated Neural Network Architecture

arXiv

引用

arXiv 2024年

作者： Yang, Zhengxin Gao, Wanling Peng, Luzhou Huang, Yunyou Tang, Fei Zhan, Jianfeng Research Center of Distributed Systems Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University China Inspur Group Co. Ltd. China Jinan Inspur Data Technology Co. Ltd. China

Designing and optimizing neural network architectures typically requires extensive expertise, starting with handcrafted designs and then manual or automated refinement. This dependency presents a significant barrier to rapid innovation. Recognizing the complexity of automatically generating neural network architecture from scratch, we introduce Younger, a pioneering dataset to advance this ambitious goal. Derived from over 174K real-world models across more than 30 tasks from various public model hubs, Younger includes 7,629 unique architectures, and each is represented as a directed acyclic graph with detailed operator-level information. The dataset facilitates two primary design paradigms: global, for creating complete architectures from scratch, and local, for detailed architecture component refinement. By establishing these capabilities, Younger contributes to a new frontier, Artificial Intelligence-Generated Neural Network Architecture (AIGNNA). Our experiments explore the potential and effectiveness of Younger for automated architecture generation and, as a secondary benefit, demonstrate that Younger can serve as a benchmark dataset, advancing the development of graph neural networks. We release the dataset2 and code3 publicly to lower the entry barriers and encourage further research in this challenging area. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Forecasting COVID-19 Infections in Gulf Cooperation Council (GCC) Countries using Machine Learning

arXiv

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arXiv 2023年

作者： Ismail, Leila Materwala, Huned Hennebelle, Alain Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates Independent Data Scientist and Engineer Al Ain Abu Dhabi United Arab Emirates

COVID-19 has infected more than 68 million people worldwide since it was first detected about a year ago. Machine learning time series models have been implemented to forecast COVID-19 infections. In this paper, we develop time series models for the Gulf Cooperation Council (GCC) countries using the public COVID-19 dataset from Johns Hopkins. The dataset set includes the one-year cumulative COVID-19 cases between 22/01/2020 to 22/01/2021. We developed different models for the countries under study based on the spatial distribution of the infection data. Our experimental results show that the developed models can forecast COVID-19 infections with high *** Codes 68-04, 68T05, 68T07, 68T20 © 2023, CC BY-NC-ND.

关键词： Coronavirus

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Review of the State-of-the-Art of Data Gloves 8

Review of the State-of-the-Art of Data Gloves

引用

8th IEEE International Symposium on Smart Electronic systems, iSES 2022

作者： Pan, Mingzhang Tang, Yingzhe Li, Hongqi Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology School of Mechanical Engineering Guangxi University Nanning530004 China Brain-Controlled Intelligent Systems Laboratory School of Software Northwestern Polytechnical University Xi'an710072 China Yangtze River Delta Research Institute of NPU Taicang215400 China

ISBN: (纸本)9798350399226

Human-machine interaction has been the focus of recent research, with data gloves as one of the approaches gaining popularity for their high accuracy and convenience. This paper summarizes the latest research status of data gloves with a focus on the area of glove-based gesture recognition. The core technology of gesture recognition is described in detail from data processing to classification, and a quantitative analysis of the recent research is conducted. We found that though studies on applications and algorithms are the mainstream, the hardware design also accounts for a pretty large proportion of about 30%. Moreover, the number of gestures, subjects, and algorithms for majority of relevant studies has been summarized. © 2022 IEEE.

关键词： Gesture recognition

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One-shot Implicit Animatable Avatars with Model-based Priors

One-shot Implicit Animatable Avatars with Model-based Priors

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International Conference on Computer Vision (ICCV)

作者： Yangyi Huang Hongwei Yi Weiyang Liu Haofan Wang Boxi Wu Wenxiao Wang Binbin Lin Debing Zhang Deng Cai State Key Lab of CAD & CG Zhejiang University Xiaohongshu Inc. Max Planck Institute for Intelligent Systems Tübingen University of Cambridge School of Software Technology Zhejiang University Fullong Inc.

Existing neural rendering methods for creating human avatars typically either require dense input signals such as video or multi-view images, or leverage a learned prior from large-scale specific 3D human datasets such that reconstruction can be performed with sparse-view inputs. Most of these methods fail to achieve realistic reconstruction when only a single image is available. To enable the data-efficient creation of realistic anima table 3D humans, we propose ELICIT, a novel method for learning human-specific neural radiance fields from a single image. Inspired by the fact that humans can effortlessly estimate the body geometry and imagine full-body clothing from a single image, we leverage two priors in ELICIT: 3D geometry prior and visual semantic prior. Specifically, ELICIT utilizes the 3D body shape geometry prior from a skinned vertex-based template model (i.e., SMPL) and implements the visual clothing semantic prior with the CLIP-based pre-trained models. Both priors are used to jointly guide the optimization for creating plausible content in the invisible areas. Taking advantage of the CLIP models, ELICIT can use text descriptions to generate text-conditioned unseen regions. In order to further improve visual details, we propose a segmentation-based sampling strategy that locally refines different parts of the avatar. Comprehensive evaluations on multiple popular benchmarks, including ZJU-MoCAP, Human3.6M, and DeepFashion, show that ELICIT outperforms strong baseline methods of avatar creation when only a single image is available. The code is public for research purposes at https://***/ELICIT

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Method for detecting surface defects of ceramic tile based on improved Cascade RCNN 4

Method for detecting surface defects of ceramic tile based o...

引用

4th International Conference on Frontiers technology of Information and Computer, ICFTIC 2022

作者： Cao, Yu Wang, Yu Feng, Haiwen Wang, Ting School of Software College Shenyang University of Technology Shenyang110870 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9798350321951

In view of the problems of small scale and low contrast of ceramic tile defects, various types of ceramic tile surface defects, and difficulty in realizing high-precision ceramic tile defect detection, a ceramic tile surface defect detection model based on improved Cascade RCNN is proposed to locate and identify the types of ceramic tile surface defects in different texture backgrounds. The improved ResNest network is used to improve the classification ability of the algorithm and optimize the performance of the model. The improved feature pyramid enhanced the feature extraction ability of the algorithm, and improved the accuracy of detecting small-scale defects and low-contrast defects of ceramic tiles. The whole connection structure of the last layer of cascade detectors was modified to double-head structure, which improved the ability of detectors to perform classification and regression tasks, and solved the problem of various kinds of defects on the surface of ceramic tiles. After data collection, 2810 tiles defect pictures were obtained, and then 7934 tiles defect slices were obtained by image segmentation, and the slices were made into tiles defect data sets. Experiments show that this method can achieve 77.8% MAP and 93.9% average positive detection rate, which is higher than Faster RCNN and original Cascade RCNN. © 2022 IEEE.

关键词： Deep learning

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MLFuse: Multi-Scenario Feature Joint Learning for Multi-Modality Image Fusion

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IEEE Transactions on Multimedia 2025年

作者： Lei, Jia Li, Jiawei Liu, Jinyuan Wang, Bin Zhou, Shihua Zhang, Qiang Wei, Xiaopeng Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Multi-modality image fusion (MMIF) entails synthesizing images with detailed textures and prominent objects. Existing methods tend to use general feature extraction to handle different fusion tasks. However, these methods have difficulty breaking fusion barriers across various modalities owing to the lack of targeted learning routes. In this work, we propose a multi-scenario feature joint learning architecture, MLFuse, that employs the commonalities of multi-modality images to deconstruct the fusion progress. Specifically, we construct a cross-modal knowledge reinforcing network that adopts a multipath calibration strategy to promote information communication between different images. In addition, two professional networks are developed to maintain the salient and textural information of fusion results. The spatial-spectral domain optimizing network can learn the vital relationship of the source image context with the help of spatial attention and spectral attention. The edge-guided learning network utilizes the convolution operations of various receptive fields to capture image texture information. The desired fusion results are obtained by aggregating the outputs from the three networks. Extensive experiments demonstrate the superiority of MLFuse for infrared-visible image fusion and medical image fusion. The excellent results of downstream tasks (i.e., object detection and semantic segmentation) further verify the high-quality fusion performance of our method. The code is publicly available at https://***/jialei-sc/MLFuse. © 1999-2012 IEEE.

关键词： Semantic Segmentation

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