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The Pairing Analysis Improvement of Magnetized Structure in Electromagnetic Bulging Process in Case of Tube with Field Shaper

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Journal of Electromagnetic Analysis and Applications 2024年第4期16卷 43-67页

作者： Blaise Kevine Lenz Soronga Zhanqian Zhan Jing Liu Jonas Asamoah Mohamed Bangali Bangura Matala Makiese N’kodia Kambani Chrionie Md Jobayer Hossain Yongdong Chen Program Debugging and Design Department Guangdong Lyric Robot Automation Co. Ltd Huizhou China Electrical and Automation Department China Three Gorges University Yichang China Electronic Department Power Electronic Research Center University of Kent Giles Ln Canterbury UK Program Debugging Department Guangdong Lyric Robot Automation Co. Ltd. Huizhou China Electrical and Computer Engineering Department South China University of Technology Guangzhou China World Standardization Certification and Testing Group (Shenzhen) Co. Ltd. Shenzhen China Electrical Engineering-Power System Mapon University Kindu Democratic Republic of Congo Department of Economy Managment School of Management Computer and Telecomunication Rabbat Marocco Materials Visual Calibration Department Guangdong Lyric Robot Automation Co. Ltd. Huizhou China Program Design Department Guangdong Lyric Robot Automation Co. Ltd. Huizhou China

In the current practical science, the accuracy in the formability of metal alloys being the goal when using electromagnetic forming (EMF) technology, which is a high-speed processing technology that uses Lorentz forces to achieve plastic deformation of sheet metal;according to the previous analysis, the results have shown that in most cases, the Lorentz force acting on the workpiece (metal) is not uniform, there are uneven axial deformations of the metal plates which prevent the rapid advancement of today’s technology. In this article, we presented some advanced analyzes which will lead us to improve the technical solution for the problems of non-uniform axial deformations of the metals in the traditional tube electromagnetic forming technology (EMF). A field shaper is used as a practical forming tool to influence the magnetic field and magnetic pressure distribution, thereby improving the forming ability and result during the electromagnetic forming (EMF) process and we see that induced eddy current control is realized by changing the structural parameters of the magnetic field shaper;which improves the strength and controllability of the magnetic force that acts on the workpiece;thereby a greater radial magnetic pressure can be achieved with field shaper than the case without it;the field shaper regulates the electromagnetic force, the distribution of the magnetic pressure decreases, and the uniform force area of the tube increases which effectively enhances the uniform range of the pipe electromagnetic bulging and the electromagnetic induction coupling between the coil and the metallic workpiece is generally required to produce the Lorentz forces. Using COMSOL Multiphysics® simulation software helped us to accurately represent the real world, simulating multiple physical effects that happened in this model during the process.

关键词： Electromagnetic Forming Field Shaper Lorentz Force Magnetic Pressure Distribution

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Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation

Out-of-Candidate Rectification for Weakly Supervised Semanti...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zesen Cheng Pengchong Qiao Kehan Li Siheng Li Pengxu Wei Xiangyang Ji Li Yuan Chang Liu Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China Peng Cheng Laboratory Shenzhen China Tsinghua University Sun Yat-Sen University

Weakly supervised semantic segmentation is typically inspired by class activation maps, which serve as pseudo masks with class-discriminative regions highlighted. Although tremendous efforts have been made to recall precise and complete locations for each class, existing methods still commonly suffer from the unsolicited Out-of-Candidate (OC) error predictions that do not belong to the label candidates, which could be avoidable since the contradiction with image-level class tags is easy to be detected. In this paper, we develop a group ranking-based Out-of-f;Candidate Rectification (OCR) mechanism in a plug-and-play fashion. Firstly, we adaptively split the semantic categories into In-Candidate (IC) and OC groups for each OC pixel according to their prior annotation correlation and posterior prediction correlation. Then, we derive a differentiable rectification loss to force OC pixels to shift to the IC group. Incorporating OCR with seminal baselines (e.g., AffinityNet, SEAM, MCTformer), we can achieve remarkable performance gains on both Pascal VOC (+3.2%, +3.3%, +0.8% mIoU) and MS COCO (+1.0%, +1.3%, +0.5% mIoU) datasets with negligible extra training overhead, which jus-tifies the effectiveness and generality of OCR. † † Ŋ ***/sennnnn/Out-of-Candidate-Rectification

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Mapping Research Trends and Visualization of E-learning in Entrepreneurship in the Last Ten Years 1

Mapping Research Trends and Visualization of E-learning in E...

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1st International Conference on Electrical and Information Technology, IEIT 2021

作者： Maulana, Fairuz Iqbal Candra Pratama, Febby Permana, Frihandhika Khaeruddin Herasmara, Ridho School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia BINUS Business School Undergraduate Program Bina Nusantara University Entrepreneurship Department Jakarta11480 Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Malang Indonesia Electrical Engineering Faculty of Science and Technology Raden Rahmat Islamic University Malang Indonesia

ISBN: (纸本)9781665417860

E-learning is a system or concept of education or learning process that utilizes information technology in the teaching and learning process anywhere and anytime. E-learning is widely applied in various fields of science. In this study, we draw scientific efforts on e-learning and entrepreneurship from publications in the Scopus database. We reviewed 218 academic documents published over the last 10 years, from 2011 to 2020. Our method is a global bibliometric analysis by visualizing connected networks using Vosviewer. The method used consists of five stages: determining search keywords, initial search results, refinement of search results, initial compilation, and data analysis. We researched several countries, affiliations, authors, subject areas, to types of publications that have relations in this field. Among the most published and indexed articles by Scopus, papers published by researchers in the China have the highest number of publications is 36 academic documents, followed by United States 18 academic publications, and Italy with 16 academic publications. The results showed that it had a limited impact, and through visualization, the network presented research gaps that future researchers should fill. The processed data shows the pattern and trend of increasing the number of international publications on E-learning in the field of entrepreneurship indexed by Scopus. Finally, we address the limitations of this study. © 2021 IEEE.

关键词： Visualization

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GraCo: Granularity-Controllable Interactive Segmentation

arXiv

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arXiv 2024年

作者： Zhao, Yian Li, Kehan Cheng, Zesen Qiao, Pengchong Zheng, Xiawu Ji, Rongrong Liu, Chang Yuan, Li Chen, Jie School of Electronic and Computer Engineering Peking University Shenzhen China Peng Cheng Laboratory Shenzhen China -Preferred Program Peking University Shenzhen Graduate School China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China Department of Automation and BNRist Tsinghua University Beijing China

Interactive Segmentation (IS) segments specific objects or parts in the image according to user input. Current IS pipelines fall into two categories: single-granularity output and multi-granularity output. The latter aims to alleviate the spatial ambiguity present in the former. However, the multi-granularity output pipeline suffers from limited interaction flexibility and produces redundant results. In this work, we introduce Granularity-Controllable Interactive Segmentation (GraCo), a novel approach that allows precise control of prediction granularity by introducing additional parameters to input. This enhances the customization of the interactive system and eliminates redundancy while resolving ambiguity. Nevertheless, the exorbitant cost of annotating multi-granularity masks and the lack of available datasets with granularity annotations make it difficult for models to acquire the necessary guidance to control output granularity. To address this problem, we design an any-granularity mask generator that exploits the semantic property of the pre-trained IS model to automatically generate abundant mask-granularity pairs without requiring additional manual annotation. Based on these pairs, we propose a granularity-controllable learning strategy that efficiently imparts the granularity controllability to the IS model. Extensive experiments on intricate scenarios at object and part levels demonstrate that our GraCo has significant advantages over previous methods. This highlights the potential of GraCo to be a flexible annotation tool, capable of adapting to diverse segmentation scenarios. The project page: https://***/GraCo. Copyright © 2024, The Authors. All rights reserved.

关键词： Pipelines

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LaPE: Layer-adaptive Position Embedding for Vision Transformers with Independent Layer Normalization

LaPE: Layer-adaptive Position Embedding for Vision Transform...

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International Conference on computer Vision (ICCV)

作者： Runyi Yu Zhennan Wang Yinhuai Wang Kehan Li Chang Liu Haoyi Duan Xiangyang Ji Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China Peng Cheng Laboratory Shenzhen China Department of Automation and BNRist Tsinghua University Beijing China School of Computer Science and Technology Zhejiang University Zhejiang China

Position information is critical for Vision Transformers (VTs) due to the permutation-invariance of self-attention operations. A typical way to introduce position information is adding the absolute Position Embedding (PE) to patch embedding before entering VTs. However, this approach operates the same Layer Normalization (LN) to token embedding and PE, and delivers the same PE to each layer. This results in restricted and monotonic PE across layers, as the shared LN affine parameters are not dedicated to PE, and the PE cannot be adjusted on a per-layer basis. To overcome these limitations, we propose using two independent LNs for token embeddings and PE in each layer, and progressively delivering PE across layers. By implementing this approach, VTs will receive layer-adaptive and hierarchical PE. We name our method as Layer-adaptive Position Embedding, abbreviated as LaPE, which is simple, effective, and robust. Extensive experiments on image classification, object detection, and semantic segmentation demonstrate that LaPE significantly outperforms the default PE method. For example, LaPE improves +1.06% for CCT on CIFAR100, +1.57% for DeiT-Ti on ImageNet-1K, +0.7 box AP and +0.5 mask AP for ViT-Adapter-Ti on COCO, and +1.37 mIoU for tiny Segmenter on ADE20K. This is remarkable considering LaPE only increases negligible parameters, memory, and computational cost.

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Fuzzy Positive Learning for Semi-Supervised Semantic Segmentation

Fuzzy Positive Learning for Semi-Supervised Semantic Segment...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Pengchong Qiao Zhidan Wei Yu Wang Zhennan Wang Guoli Song Fan Xu Xiangyang Ji Chang Liu Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China Peng Cheng Laboratory Shenzhen China AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China Department of Automation and BNRist Tsinghua University Beijing China

Semi-supervised learning (SSL) essentially pursues class boundary exploration with less dependence on human annotations. Although typical attempts focus on ameliorating the inevitable error-prone pseudo-labeling, we think differently and resort to exhausting informative semantics from multiple probably correct candidate labels. In this paper, we introduce Fuzzy Positive Learning (FPL) for accurate SSL semantic segmentation in a plug-and-play fashion, targeting adaptively encouraging fuzzy positive predictions and suppressing highly-probable negatives. Being conceptually simple yet practically effective, FPL can remarkably alleviate interference from wrong pseudo labels and progressively achieve clear pixel-level semantic discrimination. Concretely, our FPL approach consists of two main components, including fuzzy positive assignment (FPA) to provide an adaptive number of labels for each pixel and fuzzy positive regularization (FPR) to restrict the predictions of fuzzy positive categories to be larger than the rest under different perturbations. Theoretical analysis and extensive experiments on Cityscapes and VOC 2012 with consistent performance gain justify the superiority of our approach. Codes are provided in https://***/qpc1611094/FPL.

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Development of teaching materials for improving motivation to learn electromagnetics using ict

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IEEJ Transactions on Fundamentals and Materials 2020年第4期140卷 171-178页

作者： Ohtaki, Yu Ohchi, Masashi Master's Program in Electrical Electronics and Computer Engineering Graduate School of Engineering Chiba Institute of Technology 2-17-1 Tsudanuma Narashino275-0016 Japan Department of Electrical and Electronic Engineering Faculty of Engineering Chiba Institute of Technology 2-17-1 Tsudanuma Narashino275-0016 Japan

In this research, we have been developed teaching materials for electromagnetics that demonstrate theories using real-world actual applications (capacitors, coils, piezoelectric elements, Wireless Power Transfer(WPT), WiFi, direct-current motor, etc.) presented as animations and video data via Web services. Hypertext Markup Language (HTML) and JavaScript were used to create the Web services for teaching materials. Recently, the lack of academic ability of Japanese engineering course students has become a large social problem at Japanese higher education. In particular, in the field of electrical engineering education, the visualization and mathematical abilities of students have been declining remarkably. In addition, today's teaching materials for educational institutions in Japan tend to reply primarily on rote learning. However, we consider that rote learning is not the primary method for university education. We tested our new developed materials on students learning motivation of electromagnetics and evaluated their effectiveness with some questionnaires. It was clear that the new teaching method has effectiveness for increasing learning motivation of Electromagnetics. © 2020 The Institute of Electrical Engineers of Japan.

关键词： Web services

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Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning

Out-of-Distributed Semantic Pruning for Robust Semi-Supervis...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yu Wang Pengchong Qiao Chang Liu Guoli Song Xiawu Zheng Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China AI for Science(AI4S)-Preferred Program Peking University Shenzhen Graduate School China Peng Cheng Laboratory Shenzhen China Department of Automation and BNRist Tsinghua University Beijing China

Recent advances in robust semi-supervised learning (SSL) typically filter out-of-distribution (OOD) information at the sample level. We argue that an overlooked problem of robust SSL is its corrupted information on semantic level, practically limiting the development of the field. In this paper, we take an initial step to explore and propose a unified framework termed OOD Semantic Pruning (OSP), which aims at pruning OOD semantics out from in-distribution (ID) features. Specifically, (i) we propose an aliasing OOD matching module to pair each ID sample with an OOD sample with semantic overlap. (ii) We design a soft orthogonality regularization, which first transforms each ID feature by suppressing its semantic component that is collinear with paired OOD sample. It then forces the predictions before and after soft orthogonality decomposition to be consistent. Being practically simple, our method shows a strong performance in OOD detection and ID classification on challenging benchmarks. In particular, OSP surpasses the previous state-of-the-art by 13.7% on accuracy for ID classification and 5.9% on AUROC for OOD detection on TinyImageNet dataset. The source codes are publicly available at https://***/rain305f/OSP.

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Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

TechRxiv

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TechRxiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Software Engineering Sun Yat-Sen University Zhuhai China Department of Electrical and Computer Engineering Auburn University Auburn United States

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet the resource-intensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of effectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing this gap in current research, we introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diffusion model-based approach for generating the optimal ASP selection decisions. Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, enhancing the efficiency and effectiveness of ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it as a promising approach for future research on AIGC-driven services. The implementation of our proposed method is available at: https://***/Lizonghang/AGOD. © 2023, CC BY.

关键词： Reinforcement learning

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Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

arXiv

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Software Engineering Sun Yat-Sen University Zhuhai China The Department of Electrical and Computer Engineering Auburn University Auburn United States

关键词： Reinforcement learning

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