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Machine learning applied to property prediction of metal additive manufacturing products with textural features extraction

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International Journal of Advanced Manufacturing Technology 2024年第1-2期132卷 83-98页

作者： Chang, Lien-Kai Chen, Ri-Sheng Tsai, Mi-Ching Lee, Rong-Mao Lin, Ching-Chih Huang, Jhih-Cheng Chang, Tsung-Wei Horng, Ming-Huwi Electrical Motor Technology Research Center National Cheng Kung University Tainan700 Taiwan Department of Mechanical Engineering National Cheng Kung University Tainan700 Taiwan Department of Intelligent Automation Engineering National Chin-Yi University of Technology Taichung411 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan700 Taiwan Academy of Innovative Semiconductor and Sustainable Manufacturing National Cheng Kung University Tainan700 Taiwan

Laser powder bed fusion (LPBF) is one of the common metal additive manufacturing technologies, which has been increasingly applied across various industries, including healthcare, manufacturing, and aerospace, owing to its advantages in customization and faster prototyping. However, acquiring accurate product properties necessitates repetitive and time-consuming measurements, which risk damaging the product. Thus, there is a pressing need to develop an automated method for predicting product properties. In this study, to forecast these properties, we documented details related to metal additive manufacturing products, encompassing both the process parameters and textural features. These features were extracted from layer-by-layer images using the gray-level co-occurrence matrix (GLCM). Subsequently, we employed machine learning (ML) models, such as support vector regression (SVR), XGBoost, and LightGBM, to predict product properties and compare their performance. The experimental results reveal stronger correlations between process parameters and texture features of three-dimensional co-occurrence matrices of the product images, compared to two-dimensional ones. Additionally, the models exhibit high predictive accuracy, especially XGBoost, and LightGBM, with R2 scores approaching 0.9 for all properties. These findings highlight the superiority and feasibility of the proposed approach. Moreover, this proposed approach holds promise in accurately predicting diverse product properties, meeting the demands of multiple application contexts. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： 3D printing

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Mutual Retinex: Combining Transformer and CNN for Image Enhancement

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IEEE Transactions on Emerging Topics in Computational Intelligence 2024年第3期8卷 2240-2252页

作者： Jiang, Kui Wang, Qiong An, Zhaoyi Wang, Zheng Zhang, Cong Lin, Chia-Wen Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan430024 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu30013 Taiwan

Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural networks (CNNs) and Transformer architectures have made significant progress in computer vision tasks, there are few efforts to harmonize them into a more concise framework for enhancing such images. To this end, this study proposes to aggregate the individual capability of self-attention (SA) and CNNs for accurate perturbation removal while preserving background contents. Based on this, we carry forward a Retinex-based framework, dubbed as Mutual Retinex, where a two-branch structure is designed to characterize the specific knowledge of reflectance and illumination components while removing the perturbation. To maximize its potential, Mutual Retinex is equipped with a new mutual learning mechanism, involving an elaborately designed mutual representation module (MRM). In MRM, the complementary information between reflectance and illumination components are encoded and used to refine each other. Through the complementary learning via the mutual representation, the enhanced results generated by our model exhibit superior color consistency and naturalness. Extensive experiments have shown the significant superiority of our mutual learning based method over thirteen competitors on the low-light task and ten methods on the underwater image enhancement task. In particular, our proposed Mutual Retinex respectively surpasses the state-of-the-art method MIRNet-v2 by 0.90 dB and 2.46 dB in PSNR on the LOL 1000 and FIVEK datasets, while with only 19.8% model parameters. © 2017 IEEE.

关键词： Image enhancement

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Reducing the Computational Cost of Transformers for Person Re-identification

Reducing the Computational Cost of Transformers for Person R...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Wang, Wen Lin, Zheyuan Ji, Shanshan Li, Te Gu, Jason Wan, Minhong Zhang, Chunlong Research Center for Intelligent Robotics Zhejiang Lab Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350325706

Transformer-based visual technologies have witnessed remarkable progress in recent years, and person re-identification (ReID) is one of the active research areas that adopts transformers to improve the performance. However, a major challenge of applying transformers to ReID is the high computational cost, which hinders the real-time deployment of such methods. To address this issue, this paper proposes two simple yet effective techniques to reduce the computation of transformers for ReID. The first technique is to eliminate the invalid patches that do not contain any person information, thereby reducing the number of tokens fed into the transformer. Considering that computational complexity is quadratic with respect to input tokens, the second technique partitions the image into multiple windows, applies separate transformers to each window, and merges class tokens from each window, which can reduce the complexity of the self-attention mechanism. By combining these two techniques, our proposed method reduces the SOTA baseline model by 12.2% FLOPs, while slightly improving the rank-1 accuracy and only sacrificing 1.1% mAP on DukeMTMC-ReID dataset. © 2023 IEEE.

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Temperature dependent implantation of As-Isoelectronic Impurity in GaN Micro and Nanostructures

Temperature dependent implantation of As-Isoelectronic Impur...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Waseem, Aadil Chan, Clarence Wu, Xihang Liu, Yujie Shen, Yifan Kapritsos, Manos Norris, Theodore B. Li, Xiuling Microelectronic Research Center Chandara Family Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78758 United States Department of Electrical and Computer Engineering Holonyak Micro and Nanotechnology Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States

To enhance solar photo absorption of III-N semiconductors, we demonstrate Arsenic implantation at elevated temperatures, improving doping activation and reducing crystal damage. CLEO 2024 © Optica Publishing Grou... 详细信息

ISBN: (纸本)9781957171395

关键词： Gallium nitride

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Temperature dependent implantation of As-Isoelectronic Impurity in GaN Micro and Nanostructures

Temperature dependent implantation of As-Isoelectronic Impur...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Gallium nitride

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Au/SiC Microfluidic Devices Fabricated by Rapid Laser Cladding for Photocatalytic Degradation of Water Pollutants

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ACS Sustainable Chemistry and engineering 2024年第11期12卷 4486-4496页

作者： Chang, Chun-Hao Lin, Hsiu-Pen Chen, Chien-Wei Lu, Yu-Cheng Hsiao, Fu-He Hsieh, Chi-Hsiang Lin, Bo-Cheng Yu, Bang-Ying Ma, Dai-Liang Kuo, Hao-Chung Tu, Chang-Ching Li, Bor-Ran Institute of Biomedical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrical and Computer Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Centre for Emergent Functional Matter Science National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrophysics College of Science National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei11492 Taiwan Department of Photonics Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Taisic Materials Corp. Taoyuan320 Taiwan Department of Electrical Engineering National Central University Taoyuan32001 Taiwan Medical Device Innovation and Translation R&D Center National Yang Ming Chiao Tung University Taipei112304 Taiwan

The increasing proliferation of third-generation semiconductor silicon carbide (SiC) products has led to the emergence of SiC powder as an industrial byproduct. To reutilize the SiC efficiently, we propose a facile method for fabrication of photocatalytic plates from reclaimed SiC. Our approach involves affixing the SiC onto a glass surface with laser cladding, followed by sputter deposition of a gold film and laser annealing. The resulting nanogold-coated SiC plate exhibited enhanced photocatalytic activity due to the injection of hot electrons into the SiC via surface plasmon resonance. Unlike chemical reduction, which requires hours for the synthesis, purification, and isolation of Au/SiC, our approach accomplished this in just 30 min. When illuminated, the Au/SiC plate generated electron-hole pairs that formed radicals in the presence of water and oxygen, leading to the decomposition of methylene blue. Furthermore, the Au/SiC plate can be integrated into a microfluidic device for an enhanced degradation rate and efficiency. This research provides a simple and environmentally friendly approach to the production of photocatalytic plates from reclaimed SiC and contributes to sustainable development. © 2024 The Authors. Published by American Chemical Society.

关键词： Silicon carbide

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Characterizing Memory Access Patterns of Various Convolutional Neural Networks for Utilizing Processing-in-Memory

Characterizing Memory Access Patterns of Various Convolution...

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2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

作者： Jang, Jihoon Kim, Hyun Lee, Hyokeun Research Center for Electrical and Information Technology Seoul National University of Science and Technology Department of Electrical and Information Engineering Seoul Korea Republic of Inter-University Semiconductor Research Center Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350320213

Convolutional neural network (CNN) models require deeper networks and more training data for better performance, which in turn results in greater computational and memory requirements. In this paper, we analyze the memory access patterns that occur in main memory during the training processes of various CNN models. CNN training is a linear procedure consisting of a forward pass (FP) and a backward pass (BP). As a result of the analysis, we found that BP accounted for 83.4% of the total main memory accesses on average. Therefore, CNN training including FP and BP is much more memory-intensive than CNN inference using only FP. This demonstrates that CNN training is a suitable application for near-data processing to reduce memory bottlenecks and conserve computational resources. © 2023 IEEE.

关键词： Convolutional Neural Networks Memory access pattern Near data processing Network training Processing-in-Memory

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Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Color image forensics JPEG JPEG2000 Mixed double compression Quaternion convolutional neural network

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UCR-SSL: Uncertainty-Based Consistency Regularization for Semi-Supervised Learning

UCR-SSL: Uncertainty-Based Consistency Regularization for Se...

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2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

作者： Lee, Seungil Kim, Hyun Chun, Dayoung Seoul National University of Science and Technology Research Center for Electrical and Information Technology Department of Electrical and Information Engineering Seoul Korea Republic of Seoul National University Inter-University Semiconductor Research Center Department of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350320213

Recently, semi-supervised learning methods are being actively developed to increase the performance of neural networks by using large amounts of unlabeled data. Among these techniques, pseudo-labeling methods have the advantage of low computational complexity, but are vulnerable to missing annotations. To solve this problem, we propose a method called uncertainty-based consistency regularization (UCR). UCR models a detection head to obtain different outputs for input images and computes a feature map of each. Subsequently, these feature maps are matched with the original and filtered ground truth (GT), and are classified as positive and negative samples, respectively. In this process, missing samples are generated by the filtered GT;therefore, we use a specialized loss function designed to reduce the logit difference of the samples for robustness against missing annotations. We also use the uncertainty extracted through Gaussian modeling as a criterion for annotation filtering to train the network to focus on reliable results. As a result of experiments with an SSD model on the Pascal VOC dataset, the proposed approach achieved an improvement of 0.7% in terms of mAP compared to a baseline method. © 2023 IEEE.

关键词： Consistency Regularization Convolutional Neural Network Semi-Supervised Object Detection Uncertainty

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High bandwidth semipolar (20-21) micro-LED-based white light-emitting diodes utilizing perovskite quantum dots and organic emitters in color-conversion layers for visible light communication and solid-state lighting applications

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Nanoscale 2023年第17期15卷 7715-7721页

作者： Sadhu, Annada Sankar Pai, Yi-Hua Chen, Li-Yin Hsieh, Chung-An Lin, Hao-Wu Kuo, Hao-Chung Department of Photonics Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Materials Science and Engineering National Tsing Hua University Hsinchu30013 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei11492 Taiwan

We demonstrate semipolar (20-21) micro-LED-based high-bandwidth WLEDs utilizing perovskite QDs and organic emitters in color-conversion films. The WLEDs exhibit a bandwidth in excess of 1 GHz and a CCT of 6141 K, making these devices suitable for visible light communication and lighting applications. © 2023 The Royal Society of Chemistry.

关键词： Perovskite

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