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Reliable 3D Reconstruction with Single-Shot Digital Grating and Physical Model-Supervised Machine Learning

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Li, Yiming Li, Zinan Chen, Weikang Zhang, Chaobo Wang, Hao Wang, Xiaohao Gui, Weihua Gao, Wen Liang, Xiaojun Li, Xinghui Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen 518055 China Pengcheng Laboratory Shenzhen 518000 China Central South University School of Automation Changsha 410083 China Peking University National Engineering Research Center of Visual Technology School of Computer Science Beijing 100871 China

Precise and rapid three-dimensional (3D) measurement of metal parts on production lines is essential. Modern artificial intelligence (AI) supports sensing devices to see the world in 3D vision. Digital fringe 3D profilometry reconstruction (DF-3D) based on deep learning has the potential to revolutionize society. Nevertheless, attaining the high precision and robust DF-3D through a single-shot end-to-end (SS-E2E) AI network remains an unresolved challenge. For fast and precise 3D reconstruction, this work proposes an SS-E2E absolute phase prediction network strategy, composed of a multi-path branch auxiliary supervision network and guided by the fringe-phase physical model (MPS_XNet). Specifically, MPS_XNet is based on the three key stages of the phase retrieval model, including numerator, denominator, and wrapped phase supervision, as auxiliary branches to assist the main path of absolute phase for long-distance computation in interpretable features. Besides, we designed a physics-based supervisory loss function to correctly guide the network training along the path defined by the physical model. Experiments have demonstrated that MPS_XNet outperforms the state-of-the-art (SOTA) regression network paradigm on five datasets (metal workpieces and complex contours) and our measurement system, which not only compensates for insufficient input information but also robustly and interpretably guides the network training through converting the long-distance challenge to multiple short-distance sub-tasks. Our strategy achieves a groundbreaking 90% MAE reduction in phase retrieval through physical model design and task decomposition in 20 ms. It remains promising for future SOTA networks, enabling real-time, high-precision 3D measurement in industrial and scientific applications. © 1963-2012 IEEE.

关键词： 3D perception Absolute phase prediction End-to-end One shot Physical supervision Structured light

Towards Unbiased Covid-19 Lesion Localisation And Segmentation Via Weakly Supervised Learning

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Towards Unbiased Covid-19 Lesion Localisation And Segmentati...

IEEE International Symposium on Biomedical Imaging

作者： Yang Yang Jiancong Chen Ruixuan Wang Ting Ma Lingwei Wang Jie Chen Wei-Shi Zheng Tong Zhang Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China School of Computer Science and Engineering Sun Yat-sen University China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China Shenzhen People’s Hospital Shenzhen Institute of Respiratory Diseases Shenzhen China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School China

Despite tremendous efforts, it is very challenging to generate a robust model to assist in the accurate quantification assessment of COVID-19 on chest CT images. Due to the nature of blurred boundaries, the supervised segmentation methods usually suffer from annotation biases. To support unbiased lesion localisation and to minimise the labelling costs, we propose a data-driven framework supervised by only image level labels. The framework can explicitly separate potential lesions from original images, with the help of an generative adversarial network and a lesion-specific decoder. Experiments on two COVID-19 datasets demonstrates the effectiveness of the proposed framework and its superior performance to several existing methods.

关键词： Location awareness COVID-19 Image segmentation Visualization Computed tomography Supervised learning Lung

Optimal-and-Robust Control Strategy Decoupling Torque and Magnetic-Flux for im by IRM-ILQ Design Method 9

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Optimal-and-Robust Control Strategy Decoupling Torque and Ma...

9th International Conference on Renewable Energy Research and Applications, ICRERA 2020

作者： Takami, Hiroshi Waida, Tsubasa Masunaga, Teppei Watabe, Masahiro Ishibashi, Fuminori Nakamura, Masashi Oka, Toshiaki Shibaura Institute of Technology Faculty of Eng Dept. of Electrical Eng Koto-ku Tokyo Japan Shibaura Institute of Technology Graduate School of Electrical Eng. and Computer Science Koto-ku Tokyo Japan Toshiba Mitsubishi-Electric Industrial Systems Corporation Fuchu-shi Tokyo Japan

ISBN: (纸本)9781728173696

Today, with the increase in greenhouse gases, the global environment has deteriorated. Terrible droughts and floods frequently break out among many parts of the world. In order to control the increasing greenhouse gas, renewable energy power generation such as wind power and solar energy has to be introduced *** this paper, a new optimal-and-robust control strategy, decoupling torque and magnetic flux of IM (Induction Generator and Motor) by ILQ (Inverse Linear Quadratic) design method with IRM (Inverse Reference Model), is proposed. Proposed method provides robust and independent optimal control of torque and magnetic flux even when the environment around the generator in such as wind generation changes. © 2020 IEEE.

关键词： Inverse problems

Enhancing AI-Driven Education: Integrating Cognitive Frameworks, Linguistic Feedback Analysis, and Ethical Considerations for Improved Content Generation

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arXiv 2025年

作者： Yaacoub, Antoun Tarnpradab, Sansiri Khumprom, Phattara Assaghir, Zainab Prevost, Lionel Da-Rugna, Jérôme ESIEA Lab ESIEA Paris France Department of Computer Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand Graduate School of Management and Innovation King Mongkut’s University of Technology Thonburi Bangkok Thailand Faculty of Science Lebanese University Beirut Lebanon

Artificial intelligence (AI) is rapidly transforming education, presenting unprecedented opportunities for personalized learning and streamlined content creation. However, realizing the full potential of AI in educational settings necessitates careful consideration of the quality, cognitive depth, and ethical implications of AI-generated materials. This paper synthesizes insights from four related studies to propose a comprehensive framework for enhancing AI-driven educational tools. We integrate cognitive assessment frameworks (Bloom’s Taxonomy and SOLO Taxonomy), linguistic analysis of AI-generated feedback, and ethical design principles to guide the development of effective and responsible AI tools. We outline a structured three-phase approach encompassing cognitive alignment, linguistic feedback integration, and ethical safeguards. The practical application of this framework is demonstrated through its integration into OneClickQuiz, an AI-powered Moodle plugin for quiz generation. This work contributes a comprehensive and actionable guide for educators, researchers, and developers aiming to harness AI’s potential while upholding pedagogical and ethical standards in educational content generation. © 2025, CC BY.

关键词： Teaching

Find the Best Rule of Time Series Data Mining with Cluster Analysis

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Journal of Physics: Conference Series 2021年第1期1830卷

作者： Relita Buaton Muhammad Zarlis Herman Mawengkang Syahril Efendi Graduate Program Of Computer Science Department Of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

Data mining is an analytical process of knowledge discovery in large and complex data sets. Many studies wish to explore data, to find information so that knowledge can be obtained through the grouping process, classification, rules discovery, associations and data mining visualization which shows similarity. Periodic data often occurs in business applications and sciences that has big size, high dimension and continuously updated. The similarity in periodic data is based on several approaches. One of common approaches is to transform periodic series into other domains so that dimensions are reduced, followed by index mechanism. Many studies of time series do not give optimal result because limited to extracting data not able to represent time series and its pattern which is then change into rules. Rules can be found in time series data, but they are still constrained by over fitting and difficult to present. It causes time series data and non linier function of data mining decision can't be optimal. The basic idea in the method proposed is to do periodic discretization for sub-sequential formation. These sub-sequences are grouped through a measure of similarity. The simple rule-finding technique is applied to obtain hidden rules in the temporal pattern. The optimal time series data expected to generate the uncertainty trend, previously unknown and can be used to make decisions or forecasting in the future.

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DBMS-KU approach for author profiling and deception detection in Arabic 11

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DBMS-KU approach for author profiling and deception detectio...

11th Forum for Information Retrieval Evaluation, FIRE 2019

作者： Siagian, Al Hafiz Akbar Maulana Aritsugi, Masayoshi Computer Science and Electrical Engineering Graduate School of Science and Technology Kumamoto University Japan Big Data Science and Technology Faculty of Advanced Science and Technology Kumamoto University Japan Indonesian Institute of Sciences Indonesia

This paper presents DBMS-KU team approach for task 1, i.e., author profiling in Arabic tweets, and task 2, viz., deception detection in Arabic texts, of Author Profiling and Deception Detection in Arabic (APDA). Our approach utilizes word n-grams, character ngrams, word and character n-grams combinations, and function words as features for classifiers to deal with these two tasks. We then examine several term frequency thresholds to attributes of the features. Our obtained results indicated that our approach could work well in both tasks of this APDA. © Copyright 2019 for this paper by its authors.

关键词： Database systems

Ensemble Laplacian Biogeography-Based Sine Cosine Algorithm for Structural Engineering Design Optimization Problems

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arXiv 2023年

作者： Garg, Vanita Deep, Kusum Alnowibet, Khalid Abdulaziz Mohamed, Ali Wagdy Shokouhifar, Mohammad Werner, Frank School of Basic and Applied Sciences Galgotias University Greater Noida201306 India Department of Mathematics Indian Institute of Technology Uttarakhand Roorkee247667 India Statistics and Operations Research Department College of Science King Saud University Riyadh11451 Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza12613 Egypt Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical & Computer Engineering Shahid Beheshti University Tehran*** Iran Faculty of Mathematics Otto-von-Guericke University Magdeburg39016 Germany

In this paper, an ensemble metaheuristic algorithm (denoted as LX-BBSCA) is introduced. It combines the strengths of Laplacian Biogeography-Based Optimization (LX-BBO) and the Sine Cosine Algorithm (SCA) to address structural engineering design optimization problems. Our primary objective is to mitigate the risk of getting stuck in local minima and accelerate the algorithm's convergence rate. We evaluate the proposed LX-BBSCA algorithm on a set of 23 benchmark functions, including both unimodal and multimodal problems of varying complexity and dimensions. Additionally, we apply LX-BBSCA to tackle five real-world structural engineering design problems, comparing the results with those obtained using other metaheuristics in terms of objective function values and convergence behavior. To ensure the statistical validity of our findings, we employ rigorous tests such as the t-test and the Wilcoxon rank test. The experimental outcomes consistently demonstrate that the ensemble LX-BBSCA algorithm outperforms not only the basic versions of BBO, SCA, and LX-BBO but also other state-of-the-art metaheuristic *** Codes 90 C 59 © 2023, CC BY.

关键词： Structural design

Intrinsic synaptic plasticity of ferroelectric field effect transistors for online learning

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arXiv 2021年

作者： Saha, Arnob Nafiul Islam, A.N.M. Zhao, Zijian Deng, Shan Ni, Kai Sengupta, Abhronil School of Electrical Engineering & Computer Science Pennsylvania State University University ParkPA16802 United States Microsystems Engineering Ph.D. program Rochester Institute of Technology RochesterNY14623 United States

Nanoelectronic devices emulating neuro-synaptic functionalities through their intrinsic physics at low operating energies is imperative toward the realization of brain-like neuromorphic computers. In this work, we leverage the non-linear voltage dependent partial polarization switching of a ferroelectric field effect transistor to mimic plasticity characteristics of biological synapses. We provide experimental measurements of the synaptic characteristics for a 28nm high-k metal gate technology based device and develop an experimentally calibrated device model for large-scale system performance prediction. Decoupled read-write paths, ultra-low programming energies and the possibility of arranging such devices in a cross-point architecture demonstrate the synaptic efficacy of the device. Our hardware-algorithm co-design analysis reveals that the intrinsic plasticity of the ferroelectric devices has potential to enable unsupervised local learning in edge devices with limited training data. Copyright © 2021, The Authors. All rights reserved.

关键词： E-learning

Manipulating Fano coupling in an opto-thermoelectric field

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arXiv 2024年

作者： Lin, Linhan Lepeshov, Sergey Krasnok, Alex Huang, Yu Jiang, Taizhi Peng, Xiaolei Korgel, Brian A. Alu, Andrea Zheng, Yuebing State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision Instrument Tsinghua University Beijing100084 China Department of Electrical and Photonics Engineering DTU Electro Technical University of Denmark Building 343 Kgs. LyngbyDK-2800 Denmark Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States School of Physics and Electronics Hunan University Changsha410082 China Mc Ketta Department of Chemical Engineering The University of Texas at Austin AustinTX78712 United States Materials Science & Engineering Program Texas Materials Institute The University of Texas at Austin AustinTX78712 United States Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York NY10016 United States Walker Department of Mechanical Engineering The University of Texas at Austin AustinTX78712 United States

Fano resonances in photonics arise from the coupling and interference between two resonant modes in structures with broken symmetry. They feature an uneven and narrow and tunable lineshape, and are ideally suited for optical spectroscopy. Many Fano resonance structures have been suggested in nanophotonics over the last ten years, but reconfigurability and tailored design remain challenging. Herein, we propose an all-optical "pick-and-place" approach aimed at assemble Fano metamolecules of various geometries and compositions in a reconfigurable manner. We study their coupling behavior by in-situ dark-field scattering spectroscopy. Driven by a light-directed opto-thermoelectric field, silicon nanoparticles with high quality-factor Mie resonances (discrete states) and low-loss BaTiO3 nanoparticles (continuum states) are assembled into all-dielectric heterodimers, where distinct Fano resonances are observed. The Fano parameter can be adjusted by changing the resonant frequency of the discrete states or the light polarization. We also show tunable coupling strength and multiple Fano resonances by altering the number of continuum states and discrete states in dielectric heterooligomers. Our work offers a general design rule for Fano resonance and an all-optical platform for controlling Fano coupling on demand. Copyright © 2024, The Authors. All rights reserved.

关键词： Light polarization