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Toward High-Accuracy 3D Creation of Aviation Tools via Analysis-by-Synthesis

IEEE Access 2025年 13卷 87208-87219页

作者： Gundu Mohamed Hassan Yoo Sung Kim Geun Sik Jo Department of Artificial Intelligence Engineering Knowledge Management System Laboratory Inha University Incheon South Korea Department of Computer Science Engineering Artificial Intelligence Laboratory Inha University Incheon South Korea Augmented Knowledge Corporation Incheon South Korea

The field of education is rapidly shifting toward digital platforms; however, aviation education continues to rely heavily on traditional paper-based manuals. This persistence is largely due to the inherent complexity, scale, and high costs associated with aviation equipment, making digital transformation particularly challenging. In this paper, we utilizing the analysis-by-synthesis method is presented for multi-view 3D reconstruction of solid objects, specifically focusing on aviation tools. To facilitate this research, a custom dataset was generated using Unity, enabling controlled experimentation and performance evaluation. Comparative analysis with existing benchmarks demonstrates that, despite the intricate nature of aviation tools compared to simpler objects. The proposed architecture effectively reconstructs detailed and accurate digital replicas with 97% accuracy. This advancement has the potential to significantly enhance aviation training by providing immersive, interactive 3D content, bridging the gap between traditional training materials and cutting-edge digital education tools.

关键词： Training Maintenance Rendering (computer graphics) Aircraft Three-dimensional displays Lighting Image reconstruction Atmospheric modeling Accuracy Pipelines

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artificial intelligence propels lung cancer screening:innovations and the challenges of explainability and reproducibility

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Signal Transduction and Targeted Therapy 2025年第2期10卷 492-494页

作者： Mario Mascalchi Chiara Marzi Stefano Diciotti Experimental and Clinical Biomedical Sciences“Mario Serio” University of FlorenceFirenzeItaly Department of Statistics Computer ScienceApplications“G.Parenti”University of FirenzeFirenzeItaly Department of Electrical Electronicand Information Engineering“Guglielmo Marconi”-DEIUniversity of BolognaCesenaItaly Alma Mater Research Institute for Human-Centered Artificial Intelligence University of BolognaBolognaItaly

In a recent study published in Nature Medicine,Wang,Shao,and colleagues successfully addressed two critical issues of lung cancer(LC)screening with low-dose computed tomography(LDCT)whose widespread implementation,despite its capacity to decrease LC mortality,remains challenging:(1)the difficulty in accurately distinguishing malignant nodules from the far more common benign nodules detected on LDCT,and(2)the insufficient coverage of LC screening in resource-limited areas.1 To perform nodule risk stratification,Wang et *** and validated a multi-step,multidimensional artificial intelligence(AI)-based system(Fig.1)and introduced a data-driven Chinese Lung Nodules Reporting and Data System(C-Lung-RADS).1 A Lung-RADS system was developed in the US to stratify lung nodules into categories of increasing risk of LC and to provide corresponding management recommendations.

关键词： screening low dosecomputedtomography explainability reproducibility nodule risk stratificatio noduleriskstratification malignant nodules artificialintelligence

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PolypSegDiff: Dynamic Multi-scale Conditional Diffusion Model for Polyp Segmentation 27th

PolypSegDiff: Dynamic Multi-scale Conditional Diffusion Mode...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Du, Xiaogang Jiao, Yipeng Lei, Tao Zhang, Xuejun Wang, Yingbo Nandi, Asoke K. Shaanxi Joint Laboratory of Artificial Intelligence Shaanxi University of Science and Technology Xi’an China School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China Department of Electronic and Computer Engineering Brunel University London London United Kingdom

ISBN: (纸本)9783031801358

Diffusion models have demonstrated impressive potential in semantic segmentation tasks. However, these models cannot accurately segment hidden polyps with complex structures owing to the absence of multi-scale conditional features for guiding the reverse process of diffusion models. To address this issue, we propose a dynamic multi-scale conditional diffusion model (PolypSegDiff). First, we design a dynamic multi-scale integration module to fuse the noise segmentation mask and the original image, dynamically extract multi-scale conditional features, and strengthen the network’s ability of identifying polyp areas. Second, we design a hierarchical feature enhancement module to extract and combine image features at different levels. This module significantly enriches the semantic diversity of conditional features, enabling the denoising network to more accurately understand the semantic relationships between polyps and the surrounding normal tissues. Experimental results across five publicly available polyp segmentation datasets demonstrate that PolypSegDiff outperforms existing popular methods in segmentation accuracy, achieving outstanding performance and robust generalization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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The Analysis of Short Duration Power Quality Disturbances Using Short Time Fourier Transform 1

The Analysis of Short Duration Power Quality Disturbances Us...

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1st IEEE International Conference on Smart and Sustainable Developments in electrical engineering, SSDEE 2025

作者： Satyanrayana, Madgula Veeramsetty, Venkataramana Rajababu, Durgam SR University Department of Electrical and Electronics Engineering Telangana Warangal506371 India School of Computer Science and Artificial Intelligence SR University Center for AI and Deep Learning Telangana Warangal506371 India

ISBN: (纸本)9798331542108

The present power system have been charac-terized by high dynamics due to advances such as the accessorial loads, switching power supply equipment and enhancement of power system regulations. The power quality (PQ) analysis was earlier performed using Fourier Transform (FT) where parameters such as time-amplitude variations and frequency-amplitude variations were used. However, these parameters focus on the stationary power quality disturbances(PQDs) and cannot capture non-stationary or short duration PQ disturbances. Modern power systems need a broader viewpoint based on the amplitude, duration, and frequency of interruptions. In this paper, Short Time Fourier Transform(STFT) is proposed for identification of short duration PQDs which include voltage sags, swells, interruptions, transient oscillations and harmonic. In this paper, power quality disturbances will be assessed by Short-Time Fourier Transform which enhances PQ analysis under dynamic conditions. © 2025 IEEE.

关键词： Wavelet transforms

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II-NVM: Enhancing Map Accuracy and Consistency With Normal Vector-Assisted Mapping

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IEEE Robotics and Automation Letters 2025年第6期10卷 5465-5472页

作者： Zhao, Chengwei Li, Yixuan Jian, Yina Xu, Jie Wang, Linji Ma, Yongxin Jin, Xinglai Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an710049 China Hangzhou Qisheng Intelligent Techology Company Ltd Hangzhou311217 China Columbia University in the City of New York Department of Computer Science New YorkNY10027 United States Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin150001 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore

SLAM technology plays a crucial role in indoor mapping and localization. A common challenge in indoor environments is the "double-sided mapping issue", where closely positioned walls, doors, and other surfaces are mistakenly identified as a single plane, significantly hindering map accuracy and consistency. To addressing this issue this letter introduces a SLAM approach that ensures accurate mapping using normal vector consistency. We enhance the voxel map structure to store both point cloud data and normal vector information, enabling the system to evaluate consistency during nearest neighbor searches and map updates. This process distinguishes between the front and back sides of surfaces, preventing incorrect point-to-plane constraints. Moreover, we implement an adaptive radius KD-tree search method that dynamically adjusts the search radius based on the local density of the point cloud, thereby enhancing the accuracy of normal vector calculations. To further improve real-time performance and storage efficiency, we incorporate a Least Recently Used (LRU) cache strategy, which facilitates efficient incremental updates of the voxel map. The code is released as open-source and validated in both simulated environments and real indoor scenarios. Experimental results demonstrate that this approach effectively resolves the "double-sided mapping issue" and significantly improves mapping precision. Additionally, we have developed and open-sourced the first simulation and real-world dataset specifically tailored for the "double-sided mapping issue". © 2016 IEEE.

关键词： Mapping

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Damage-Free Neutral Beam Etching for Gate Recess in E-Mode AlGaN/GaN HEMTs

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年第5期46卷 705-708页

作者： Chen, Yi-Ho Chu, Fu-Chuan Aslam, Muhammad Lee, Yao-Jen Li, Yiming Samukawa, Seiji National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Graduate Degree Program of College of Electrical and Computer Engineering Hsinchu300093 Taiwan National Chung-Shan Institute of Science and Technology Solid State Devices Section Electronic System Research Division Taoyuan325 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

Recess gate etching is a critical technique for achieving enhancement-mode (E-mode) AlGaN/GaN high-electron mobility transistors (HEMTs) because the interface is susceptible to the etching damage. This study fabricates recess gates using the neutral beam etching (NBE) technique. By adjusting the aperture thickness in the NBE apparatus, we simulate both NB-mode and plasma-mode etching. The electrical characteristics of E-mode HEMTs fabricated using these two modes are analyzed and compared through DC, noise, and pulsed IV measurements. The results demonstrate that NB-recessed HEMTs exhibit superior performance. © 1980-2012 IEEE.

关键词： Aluminum gallium nitride

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Modified Term Frequency and Inverse Document Frequency with Optimized Deep Learning Algorithm based Fake News Detection 1

Modified Term Frequency and Inverse Document Frequency with ...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Rana Veer Samara Sihman Bharattej, R. Prashanth, V. Alabdeli, Haideer Thottan, Sunaina Sangeet Ananthi, S. Saint Leo University Department of Computer Science FL United States Nitte Meenakshi Institute of Technology Department of Electrical and Electronics Engineering Bengaluru India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Dhanalakshmi Srinivasan College of Engineering and Technology Department of Artificial Intelligence and Data Science Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331529246

The identification of real and fake news has become difficult task in the recent years due to more similar feature between them. As the issues of fake news are leading the miscommunication to the reads, various automated software has been introduced for the detection of fake news. The existing models tried to advance the identification of the fake new but failed to reach the expected outcomes due to overfitting issues and limited feature handling abilities. To overcome the existing issues the Latent Dirichlet Allocation based Term Frequency and Inverse Document Frequency with Brown-bear Optimization Algorithm-Bidirectional Long Short-Term Memory (LDA based TF-IDF with BOA-BiLSTM) is developed for the detection of fake news. The news data is collected and get cleaned by tokenization and normalization techniques. Then the LDA based TF-IDF model is used for the extraction of significant details of the news text data. Further the BOA-BiLSTM algorithm is used for the detection of fake news. The developed LDA based TF-IDF with BOA-BiLSTM algorithm has exhibited the better result having accuracy of 0.9489 and recall of 0.9012 for ISOT fake news dataset compared to the existing Knowledge Graphs for multi-domain fake news detection (KG-MFEND) model. © 2025 IEEE.

关键词： Deep learning

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Stabilizing a Class of Periodical Time-Delay Milling Systems by Adaptive Active Control Method

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IEEE Transactions on Industrial Informatics 2025年第6期21卷 4457-4465页

作者： Wu, Yue Zhang, Hai-Tao Xu, Bowen Ren, Gui-Ping Shi, Yang Chen, Guanrong Beijing Forestry University Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation School of Technology State Key Laboratory of Efficient Production of Forest Resources Beijing100083 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation The Institute of Artificial Intelligence The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems The State Key Laboratory of Digital Manufacturing Equipment and Technology Wuhan430074 China Huazhong University of Science and Technology Guangdong HUST Industrial Technology Research Institute Dongguan523808 China Guangdong Provincial Key Laboratory of Manufacturing Equipment Digitization Dongguan523808 China Northwestern Polytechnical University School of Artificial Intelligence Optics and Electronics Xi'an710072 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 2Y2 Canada City University of Hong Kong Department of Electrical Engineering Hong Kong999077 Hong Kong

Periodical time-delay scenario is often encountered in industrial manufacturing processes. However, the presence of time delays and periodical coefficients brings challenges to controller design and system analysis, which thereby hinders the performance improvement of such systems. In this work, the dynamics of milling systems are transformed into a time-invariant finite-dimensional uncertain model described by Fourier series and Padé approximation. An adaptive active control law is accordingly designed to stabilize such complex dynamics. With the assistance of LaSalle-Yoshizawa theorem, conditions are derived to guarantee sufficiently large stability regions of the corresponding closed-loop system. A numerical case study is conducted on a standard two degrees of freedom milling perturbation system to substantiate the superiority of the proposed adaptive active control technique in terms of enlarged stable operational regions. © 2025 IEEE. All rights reserved.

关键词： Milling (machining)

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A Novel Anti-rounding Image Steganography Method for Improved UNet++ 7th

A Novel Anti-rounding Image Steganography Method for Improv...

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7th Chinese Conference on Pattern Recognition and computer Vision, PRCV 2024

作者： Duan, Xintao Zhang, Meng Bai, Luwei Xu, Kaiou Bao, Mengru Wu, Yinhang Qin, Chuan College of Computer and Information Engineering Henan Normal University Henan Xinxiang453007 China Key Laboratory Foundation of Artificial Intelligence and Personalized Learning in Education of Henan Province Henan Normal University Henan Xinxiang453007 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9789819786916

Deep hiding involves embedding a secret image within a cover image so that the secret becomes imperceptible to ensure the security of steganography. However, in practical applications, accuracy loss is due to data conversion during encoding and decoding. The encoder generates the tensor matrix during training, while the practical application corresponds to the image preservation extraction process;the error caused by the rounding operation in this process will affect the accuracy of the secret information extraction during decoding. Aiming at the currently existing methods ignoring the rounding error problem, this paper proposes a novel anti-rounding image steganography method for improved UNet++. We design an anti-rounding function (ARF) to simulate the real steganographic environment, training the model to overcome the non-differentiable rounding distortion problem. When rounding error is considered, the accuracy of the extracted image decreases. To ensure the image quality in real application scenarios, in this paper, we use the improved UNet++ to ensure the quality of steganographic images by cascading convolution and ECA attention mechanisms and propose the Multiscale Mixing Module (MSMM) for feature fusion. In the extraction stage, the corresponding cover image is selected through a shared cover image library, followed by processing the stego image to obtain a different image before feeding it to the extraction network to guarantee the steganography process’s safety. The experiment results indicate the method’s capability to enhance the imperceptibility of steganography significantly, and the steganography process has better security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decoding

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