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A deep learning solution for real-time quality assessment and control in additive manufacturing using point cloud data

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JOURNAL OF INTELLIGENT MANUFACTURING 2024年第3期35卷 1389-1406页

作者： Akhavan, Javid Lyu, Jiaqi Manoochehri, Souran Stevens Inst Technol Dept Mech Engn Hoboken NJ 07030 USA

This work presents an in-situ quality assessment and improvement technique using point cloud and AI for data processing and smart decision making in Additive Manufacturing (AM) fabrication to improve the quality and accuracy of fabricated artifacts. The top surface point-cloud containing top surface geometry and quality information is pre-processed and passed to an improved deep Hybrid Convolutional Auto-Encoder decoder (HCAE) model used to statistically describe the artifact's quality. The HCAE's output is comprised of 9 x 9 segments, each including four channels with the segment's probability to contain one of four labels, Under-printed, Normally-printed, Over-printed, or Empty region. This data structure plays a significant role in command generation for fabrication process optimization. The HCAE's accuracy and repeatability were measured by a multi-label multi-output metric developed in this study. The HCAE's results are used to perform a real-time process adjustment by manipulating the future layer's fabrication through the G-code modification. By adjusting the machine's print speed and feed-rate, the controller exploits the subsequent layer's deposition, grid-by-grid. The algorithm is then tested with two defective process plans: severe under-extrusion and over-extrusion conditions. Both test artifacts' quality advanced significantly and converged to an acceptable state by four iterations.

关键词： Smart additive manufacturing Point cloud processing Defect detection and classification Machine learning (ML) and deep learning (DL) Digital manufacturing Adaptive process control

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Fourier-attention network: A deep neural network for lithographic misalignment sensing

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OPTICS AND LASERS IN ENGINEERING 2024年 176卷

作者： Wang, Nan Li, Yi Jiang, Wei Qin, Zhen 'an Liu, Jun Southwest Jiaotong Univ Sch Phys Sci & Technol Chengdu 611756 Peoples R China Chengdu Rail Transit Grp CO LTD Chengdu 610096 Peoples R China ShanghaiTech Univ Sch Phys Sci & Technol Shanghai 201210 Peoples R China Zhangjiang Lab Shanghai 201210 Peoples R China

A recurring challenge in integrated lithography is subnanoscale misalignment sensing. In widely-used moire'-based misalignment sensing schemes, measurement accuracy is restricted by the performance of the image processing schemes. This is also a fundamental problem in the field of Fourier optics that has received extensive attention in the science and engineering fields. This paper proposes a Fourier-attention neural network that can achieve real time-lapse misalignment sensing with an accuracy of 0.23 nm. This is enabled by the system's robustness to system errors and noise. We hope that this strategy can provide an effective solution for various misalignment sensing applications and that the approach can be applied to future problems.

关键词： Misalignment sensing Lithography deep learning Moire ' fringes

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A Novel Simulation for Polarization Dehazing

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IEEE SIGNAL processing LETTERS 2024年 31卷 341-345页

作者： Yan, Changda Zhang, Xin Wang, Xia Jiao, Gangcheng He, Huiyang Beijing Inst Technol Minist Educ China Key Lab Photoelect Imaging Technol & Syst Beijing 100081 Peoples R China Sci & Technol Low Light Level Night Vis Lab Xian 710065 Peoples R China

Haze and fog, as severe weather conditions, have absorbing and scattering effects on the optical images, severely affecting image quality. Polarization-based dehazing algorithms can estimate the original radiance distribution of the scene through the polarization of skylight and transmitted light. However, current traditional methods lack consideration for the polarization of transmitted light, and the datasets required for deep learning-based methods are difficult to obtain. This letter proposes a polarized haze image synthesis method that can generate scene intensity and polarization after passing through different distances and concentrations from existing DoFP images, Specially, we equate the attenuation of the scattering medium to a superposition of a series of Mueller matrices, and in combination with the atmospheric attenuation model, which thoroughly integrates both intensity characteristics and polarization properties. We establish a comprehensive polarization dataset for image dehazing, including 300 sets of simulated data, 40 sets real world data from artificial scenes with haze-free ground truth and 40 sets real world data from urban scenes. The network model trained on our simulated dataset demonstrates the effectiveness of the simulation method in testing experiments.

关键词： Scattering Attenuation Atmospheric modeling Feature extraction Training Meteorology Cameras deep learning polarization-based dehazing simulation

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Automatic quality control of aluminium parts welds based on 3D data and artificial intelligence

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JOURNAL OF INTELLIGENT MANUFACTURING 2024年第4期35卷 1629-1648页

作者： Cardellicchio, Angelo Nitti, Massimiliano Patruno, Cosimo Mosca, Nicola di Summa, Maria Stella, Ettore Reno, Vito Natl Res Council Italy Inst Intelligent Ind Technol & Syst Adv Mfg CNR STIIMA Via G Amendola 122 D-O I-70126 Bari Italy

Detecting defects in welds used in critical or non-critical industrial applications is of intense interest. Several non-destructive inspection methods are available, each allowing the preservation of the integrity of the sample under analysis. However, visual-based inspection methods are the most well-assessed, which usually require human experts to inspect each sample, looking for shallow defects. This process often requires time and effort by the human operator, therefore not allowing to perform real-time defect identification, which may result in unexpected (and undesired) production costs. In recent years, several methods have been proposed to automatically deal with visual-based inspection, mainly through convolutional neural networks. However, while effective, these models require a lot of data and computational power to be trained, which is also time-consuming. This paper proposes a high-throughput data gathering and processing method using laser profilometry, along with an automatic defect detection method based on lightweight machine learning algorithms. Six different machine and deep learning approaches are compared, including SVMs, decision forests, and neural networks, achieving a top-1 accuracy of 99.79% for defect identification and 99.71% for defect categorization. Thanks to its effectiveness and the high data throughput achievable by data gathering, the whole method can be implemented in real production lines to minimize costs and perform real-time monitoring and defects assessment.

关键词： 3D laser profilometry Automatic quality control of aluminum weldings Machine learning deep learning

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A vision system based on CNN-LSTM for robotic citrus sorting Author links open overlay panel

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Information processing in Agriculture 2024年第1期11卷 14-25页

作者： Yonghua Yu Xiaosong An Jiahao Lin Shanjun Li Yaohui Chen College of Engineering Huazhong Agricultural UniversityWuhan 430070China Key Laboratory of Agricultural Equipment in Mid-Lower Yangtze River Ministry of Agriculture and Rural AffairsWuhan 430070China National R&D Center for Citrus Preservation Wuhan 430070China Citrus Mechanization Research Base Ministry of Agriculture and Rural AffairsWuhan 430070China

Compared with manual sorting of citrus fruit,vision-based sorting solutions can help achieve higher accuracy and *** this study,we present a vision system based on CNN-LSTM,which can cooperate with robotic grippers for real-time sorting and is readily applicable to various citrus processing plants.A CNN-based detector was adopted to detect the defective oranges in view and temporarily classify them into corresponding types,and an LSTM-based predictor was used to predict the position of the oranges in a future frame based on image sequential *** fusion of CNN and LSTM networks enabled the system to track defective ones during rotation and identify their true types,and their future path was also predicted which is vital for predictive control of visually guided robotic *** detection accuracy of 94.1%was obtained based on experimental results,and the error for path prediction was within 4.33 pixels 40 frames *** average time to process a frame was between 28 and 62 frames per second,which also satisfied real-time *** results proved the potential of the proposed system for automated citrus sorting with good precision and efficiency,and it can be readily extended to other fruit crops featuring high versatility.

关键词： deep learning Long short-term memory Vision system Online citrus sorting Path prediction

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deep learning-assisted arrhythmia classification using 2-D ECG spectrograms

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EURASIP JOURNAL ON ADVANCES IN SIGNAL processing 2024年第1期2024卷 1-15页

作者： Malleswari, Pinjala N. Odugu, Venkata krishna Rao, T. J. V. Subrahmanyeswara Aswini, T. V. N. L. Sasi Inst Technol & Engn Elect & Commun Technol Tadepalligudem 534101 AP India CVR Coll Engn Elect & Commun Engn Hyderabad 501510 Telangana India Sasi Inst Technol & Engn Dept Elect & Commun Engn Tadepalligudem 534101 AP India Sri Vasavi Engn Coll Elect & Commun Engn Tadepalligudem 534101 AP India

This article studies modern classification techniques in ECG signals through the transfer learning approach with CNN (Convolutional Neural Network). The proposed pre-trained network combines an imagenet with huge labeled image datasets and a separate network composed of fully connected layers. This method uses the CWT (Continuous Wavelet Transform) to construct a time-frequency visualization of ECG signals, which are subsequently transformed into RGB images. The developed images are plugged into a pre-trained CNN to retrieve the desired features. We next employ supervised learning to train the neural network on the ECG labeled data using CNN features. To train a deep Neural Network, three sets of PhysioNet databases are used: MIT-BIH (ARR) Arrhythmia, NSR (Normal Sinus Rhythm), and BIDMC CHF (Congestive Heart Failure). The classification Accuracy, Sensitivity, Specificity, F1-score, Precision, and Detection Error Rate of the CNN classifier are compared to AlexNet, GoogleNet, Vgg16, and SqueezeNet pre-trained networks. Among all these networks, SqueezeNet provides an Acc of 98.7%, Se of 99.1%, Sp of 99.20%, F1-score of 98.33%, Precision of 98.67%, and DER of 0.89%. For further investigation, the technique suggested can be implemented in addition to Bi-LSTM on some real ECG data.

关键词： Electrocardiogram Convolutional Neural Networks Classification deep learning

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Automated angular measurement for puncture angle using a computer-aided method in ultrasound-guided peripheral insertion

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PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE 2024年第2期47卷 679-689页

作者： Watanabe, Haruyuki Fukuda, Hironori Ezawa, Yuina Matsuyama, Eri Kondo, Yohan Hayashi, Norio Ogura, Toshihiro Shimosegawa, Masayuki Gunma Prefectural Coll Hlth Sci Sch Radiol Technol Maebashi Japan Cardiovasc Hosp Cent Japan Dept Radiol Shibukawa Japan Univ Fukuchiyama Fac Informat Fukuchiyama Japan Niigata Univ Grad Sch Hlth Sci Niigata Japan

Ultrasound guidance has become the gold standard for obtaining vascular access. Angle information, which indicates the entry angle of the needle into the vein, is required to ensure puncture success. Although various image processing-based methods, such as deep learning, have recently been applied to improve needle visibility, these methods have limitations, in that the puncture angle to the target organ is not measured. We aim to detect the target vessel and puncture needle and to derive the puncture angle by combining deep learning and conventional image processing methods such as the Hough transform. Median cubital vein US images were obtained from 20 healthy volunteers, and images of simulated blood vessels and needles were obtained during the puncture of a simulated blood vessel in four phantoms. The U-Net architecture was used to segment images of blood vessels and needles, and various image processing methods were employed to automatically measure angles. The experimental results indicated that the mean dice coefficients of median cubital veins, simulated blood vessels, and needles were 0.826, 0.931, and 0.773, respectively. The quantitative results of angular measurement showed good agreement between the expert and automatic measurements of the puncture angle with 0.847 correlations. Our findings indicate that the proposed method achieves extremely high segmentation accuracy and automated angular measurements. The proposed method reduces the variability and time required in manual angle measurements and presents the possibility where the operator can concentrate on delicate techniques related to the direction of the needle.

关键词： Ultrasound Puncture angle deep learning U-Net Angular measurements

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Online Trajectory Optimization for Energy-Efficient Cellular-Connected UAVs With Map Reconstruction

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第3期73卷 3445-3456页

作者： Zhao, Haitao Hao, Qing Huang, Hao Gui, Guan Ohtsuki, Tomoaki Sari, Hikmet Adachi, Fumiyuki Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Peoples R China Keio Univ Dept Informat & Comp Sci Yokohama 2238522 Japan Tohoku Univ Int Res Inst Disaster Sci IRIDeS Sendai 9808577 Japan

In this article, we leverage the outage probability knowledge map to characterize the connection between unmanned aerial vehicles (UAVs) and cellular networks. The outage probability knowledge map is a database that simulates the connection between UAV and the cellular network during real hovers, which helps to enhance the UAV's awareness of the environment and reduce the connection interruption under complex real-time channel state information. We assume that the UAV roughly samples from the actual radio environment of the airspace in advance, and calculates the outage probability of the sampled points. After that, the UAV reconstructs the actual outage knowledge map, and flies in the airspace to learn the optimal UAV trajectory planning policy based on the reconstructed map. The optimization objective is to minimize the flight energy cost of the UAV performing tasks. In this article, we propose a deep image prior based radio map reconstruction (DIPRMR) method to reconstruct the map, and then propose a deep reinforcement learning based trajectory optimization algorithm. The UAV that performs the task adjusts the flight trajectory based on the outage probability knowledge obtained from the reconstructed complete map. Simulation results show that the proposed online trajectory optimization scheme based on outage probability knowledge map can obtain great returns in terms of maintaining connectivity, reducing task completion time and energy consumption.

关键词： Cellular-connected UAV deep reinforcement learning energy-efficient UAV image reconstruction radio map trajectory design

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MA-MFIF: When misaligned multi-focus image fusion meets deep homography estimation

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Multimedia Tools and Applications 2025年第12期84卷 10877-10898页

作者： Zhao, Baojun Luo, Fei Fuentes, Joel Ding, Weichao Gu, Chunhua School of Information Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Department of Computer Science and Information Technologies Universidad del Bio-Bio Chillán3780000 Chile

Multi-focus image fusion is a technique that combines multiple out-of-focus images to enhance the overall image quality. It has gained significant attention in recent years, thanks to the advancements in deep learning. However, one of the persistent challenges in this field is the processing of misaligned data, which can negatively impact the fusion results. To overcome this problem, a novel fusion framework with pre-registration is proposed for the fusion of misaligned multi-focus images. For pre-registration, content-aware deep homography estimation is used, which performs transfer learning on a real multi-focus image dataset to adapt to registration under defocused conditions. For fusion, a fusion module with dual-branch feature interaction is utilized to avoid invalid feature fusion and trained on real light field dataset to achieve better fusion performance. Qualitative and quantitative experimental results show that the proposed method has a 2-3 percentage point improvement in multiple evaluation metrics compared to existing advanced registration and fusion methods, and a maximum improvement of 4.83 percentage points in fusion performance when tested independently on the Lytro dataset. Additionally, We find that the value of the Qcv metric is greatly influenced by the alignment status of the input images, leading to its inability to reflect the fusion quality of aligned images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： image enhancement

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Compact deep neural networks for real-time speech enhancement on resource-limited devices

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SPEECH COMMUNICATION 2024年 156卷

作者： Wahab, Fazal E. Ye, Zhongfu Saleem, Nasir Ullah, Rizwan Univ Sci & Technol China Natl Engn Lab Speech & Language Informat Proc Hefei Anhui Peoples R China Gomal Univ Fac Engn & Technol Dept Elect Engn Dera Ismail Khan 29050 Pakistan Chulalongkorn Univ Dept Elect Engn Bangkok 10330 Thailand

In real-time applications, the aim of speech enhancement (SE) is to achieve optimal performance while ensuring computational efficiency and near-instant outputs. Many deep neural models have achieved optimal performance in terms of speech quality and intelligibility. However, formulating efficient and compact deep neural models for real-time processing on resource-limited devices remains a challenge. This study presents a compact neural model designed in a complex frequency domain for speech enhancement, optimized for resource-limited devices. The proposed model combines convolutional encoder-decoder and recurrent architectures to effectively learn complex mappings from noisy speech for real-time speech enhancement, enabling low-latency causal processing. Recurrent architectures such as Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Simple Recurrent Unit (SRU), are incorporated as bottlenecks to capture temporal dependencies and improve the performance of SE. By representing the speech in the complex frequency domain, the proposed model processes both magnitude and phase information. Further, this study extends the proposed models and incorporates attention-gate-based skip connections, enabling the models to focus on relevant information and dynamically weigh the important features. The results show that the proposed models outperform the recent benchmark models and obtain better speech quality and intelligibility. The proposed models show less computational load and deliver better results. This study uses the WSJ0 database where clean sentences from WSJ0 are mixed with different background noises to create noisy mixtures. The results show that STOI and PESQ are improved by 21.1% and 1.25 (41.5%) on the WSJ0 database whereas, on the VoiceBank+DEMAND database, STOI and PESQ are improved by 4.1% and 1.24 (38.6%) respectively. The extension of the models shows further improvement in STOI and PESQ in seen and unseen noisy conditions.

关键词： deep learning Speech enhancement Convolutional encoder-decoder Recurrent networks Quality and intelligibility Phase estimation Causal processing

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