检索结果-内蒙古大学图书馆

Steel surface roughness parameter prediction from laser reflection data using machine learning models

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第9-10期132卷 4645-4662页

作者： Milne, Alex Xie, Xianghua Swansea Univ Dept Comp Sci Swansea SA2 8PP Wales

Control of surface texture in strip steel is essential to meet customer requirements during galvanizing and temper rolling processes. Traditional methods rely on post-production stylus measurements, while on-line techniques offer non-contact and real-time measurements of the entire strip. However, ensuring accurate measurement is imperative for their effective utilization in the manufacturing pipeline. Moreover, accurate on-line measurements enable real-time adjustments of manufacturing processing parameters during production, ensuring consistent quality and the possibility of closed-loop control of the temper mill. In this study, we formulate the manufacturing issue into a time Series Extrinsic Regression problem and a Machine Vission problem and leverage state-of-the-art machine learning models to enhance the transformation of on-line measurements into a significantly more accurate Ra surface roughness metric. By comparing a selection of data-driven approaches, including both deep learning such as convolutional, recurrent, and transformer networks and non-deep learning methods such as Rocket and XGBoost, to the close-form transformation, we evaluate their potential using Root Mean Squared Error (RMSE) and correlation for improving surface texture control in temper strip steel manufacturing.

关键词： Machine learning On-line measurement Surface roughness Temper rolling time Series Extrinsic Regression (TSER)

来源：评论

学校读者我要写书评

暂无评论

Detection of Moiré pattern in high-resolution images

引用

SIGNAL image AND VIDEO processing 2024年第1期18卷 561-568页

作者： Zhang, Yu Shen, Li-Yong Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China

Despite the improvements in the quality of digital images brought about by the development of digital cameras and smartphones, taking clean photographs remains a challenge due to the presence of noise such as moire patterns, especially for high-resolution images commonly used in today's real life. To more efficiently and accurately identify and remove moires in high-resolution images, this paper proposes an improved network based on the high-resolution network HRDN. We conducted experiments on the FHDMi dataset to evaluate the effectiveness of our method. The results demonstrate that the improved algorithm significantly enhances the performance of moire detection in high-resolution images, making it better suited to meeting the demands of real-world applications.

关键词： image process Demoireing High-resolution deep learning

来源：评论

学校读者我要写书评

暂无评论

Scanning imaging through the scattering medium based on the guided filter assisted by a WTMM and NLM

引用

APPLIED OPTICS 2025年第10期64卷 2408-2418页

作者： Zhang, Yiyang Ding, Zhewen Li, Runze Meng, Xianglei Shi, Yan Zhan, Chunlian Zhao, Chunliu China Jiliang Univ Coll Opt & Elect Technol Hangzhou 310018 Zhejiang Peoples R China State Adm Market Regulat Key Lab Light Sensing & Image Metrol Hangzhou 310018 Zhejiang Peoples R China Zhejiang Prov Key Lab Optoelect Metrol & Precis In Hangzhou 310018 Zhejiang Peoples R China

In a scanning imaging system through a scattering medium, the quality of the imaging result is related to the energy distribution of the focusing point. In actual imaging, the energy of the focusing point cannot be perfectly concentrated. The scattering noise is always surrounded by the focusing point, which reduces the signal-to-noise ratio and results in poor image quality. To improve the quality of imaging, further processing of the imaging results is required, while most conventional image processing methods can only achieve one of the goals such as noise reduction, image smoothing, edge sharpening, or maintaining image integrity. In this paper, a scanning imaging system through the scattering medium based on an adaptive guided filter assisted by the wavelet transform modulus maximum (WTMM) and non-local mean (NLM) is proposed for the first time, to the best of our knowledge, which can make the imaging results simultaneously have the advantages of low noise, high contrast, and clear details. To verify the validity of the proposed method, a scanning imaging system through the scattering medium was setup. Transmissive imaging was performed at different positions from the focal plane. The experimental results show that the background noise is significantly restrained, single-pixel response and edge continuity are good, and details are clear with the proposed method. Compared with the traditional methods and the deep learning methods, the proposed method can improve the PSNR and SSIM by up to 10.68 dB, 0.75 and 5.34 dB, 0.72, respectively. Finally, the performance of the proposed method in high-noise environments, its application in the field of real-time imaging, and future improvements are discussed. The method proposed in this paper can effectively improve the quality of scanning imaging results through the scattering medium, which is expected to promote the application of this technology in endoscopic imaging and other fields. (c) 2025 Optica Publishing Group. A

关键词： deep learning Imaging systems Imaging techniques real time imaging Scattering media Wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

learning deep Representations for Photo Retouching

引用

IEEE TRANSACTIONS ON MULtimeDIA 2024年 26卷 3153-3163页

作者： Li, Di Rahardja, Susanto Northwestern Polytech Univ Ctr Intelligent Acoust & Immers Commun Sch Marine Sci & Technol Xian 710072 Peoples R China Singapore Inst Technol Singapore 138683 Singapore

Photo enhancement is a long-standing and challenging problem in image processing community. Despite having witnessed significant achievements in recent years, many of them are built upon supervised learning theories and thus required expertise in constructing a huge collection of paired data, which is well-known to be a problem as the acquisition of such data in real life can be impractical. We address this issue by proposing a multi-scale GAN framework that can be trained in an unsupervised fashion. Notably, we unify the design principle of the generator and discriminator in our framework so as to maximize the ability to learn deep latent representations. Specifically, rather than maintaining the content consistency through complicated two-way loss, we present a one-way loss that measures the content distance between multi-scale latent representations of inputs and outputs to speed up the training by 1.7x. Furthermore, we redesign the discriminator into a multi-scale-multi-stage manner to strengthen the adversarial learning, where the multiple latent features with varying scales are produced by the main discriminator and these features are then sent to auxiliary discriminators for final recognition. Extensive experiments have been conducted in the well-known MIT-Adobe-fivek and HDR+ datasets, and the results demonstrated that the proposed multi-scale representation learning framework shows outstanding performance in photo enhancement task.

关键词： Contrastive learning deep learning generative adversarial nets image enhancement unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

deep learning Approaches for Continuous Sign Language Recognition: A Comprehensive Review

引用

IEEE ACCESS 2025年 13卷 55524-55544页

作者： Khan, Asma Jin, Seyong Lee, Geon-Hee Arzu, Gul E. Dang, L. Minh Nguyen, Tan N. Choi, Woong Moon, Hyeonjoon Sejong Univ Dept Comp Sci & Engn Seoul 05006 South Korea Sejong Univ Dept Artificial Intelligence Seoul South Korea Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Informat Technol Da Nang 550000 Vietnam Sejong Univ Dept Informat & Commun Engn & Convergence Engn Int Seoul 05006 South Korea Sejong Univ Dept Architectural Engn Seoul 05006 South Korea Kangnam Univ Div ICT Convergence Engn Yongin 16979 South Korea

Sign language uses hand gestures as a visual mode of communication, along with body actions and facial expressions. Due to the increasing incidence of hearing deficiencies, the field of Continuous Sign Language Recognition (CSLR) has seen a considerable increase in research, which involves identifying consecutive signs in video streams without previous information of their sequential limitations. However, existing research often lacks a unified framework for integrating spatial, temporal, and alignment approaches, while critical challenges such as real-time processing, diverse datasets, and signer variability remain unresolved. This survey uniquely contributes by presenting a novel framework that integrates these dimensions into a unified taxonomy for CSLR systems. It critically analyzes numerous studies, organizing them into a comprehensive taxonomy covering aspects such as sign language, data collection, input method, gesture signals, identification methods, applied data collections, and comprehensive efficiency. The article further categorizes deep-learning CSLR models according to spatial, temporal, and alignment approaches, highlighting their benefits and drawbacks. Furthermore, it explores various research aspects, such as the challenges of CSLR, the significance of nonverbal elements in CSLR systems, and the gaps in the body of current research. By emphasizing the role of real-time processing and diverse datasets, this survey provides actionable insights for advancing CSLR systems in practical applications. This classification serves as a helpful tool for researchers developing and organizing cutting-edge CSLR methods. The study highlights the effectiveness of deep learning systems in capturing different sign language signals. On the other hand, several challenges remain, such as the need for diverse, naturalistic datasets, improved signer diversity, and real-time CSLR systems. Addressing these gaps will be essential for advancing CSLR's real-world applicatio

关键词： Sign language Feature extraction Assistive technologies Hands Accuracy deep learning Training Skeleton Hidden Markov models Visualization Continuous sign language recognition deep learning hand gesture recognition (HGR) computer vision

来源：评论

学校读者我要写书评

暂无评论

SLTM Network: Efficient Application of Lightweight image Segmentation Technology in Detecting Drivable Areas for Unmanned Line-Marking Machines

引用

IEEE ACCESS 2024年 12卷 169001-169012页

作者： Wang, Chao Chen, Xiangkai Wang, Bingtao Zhang, Liang Liu, Bing Shandong Univ Sch Mech Elect & Informat Engn Weihai 264209 Peoples R China Shandong Univ Off Acad Affairs Weihai 264209 Peoples R China

image segmentation plays a crucial role in the roadwork operations of autonomous line-painting machines. However, the limited resources of mobile platforms in intelligent line-painting applications pose a dual challenge of ensuring both accuracy and real-time performance in road segmentation. To address this issue, this study introduces a lightweight yet efficient image segmentation model, termed the SLTM Network. Central to this network is the lightweight SLTM module, which significantly reduces the model's parameter count and lowers the computational overhead of the decoder. To enhance the interplay of information at different spatial resolutions, the network incorporates an SE attention-enhanced upsampling module (SAUM) and employs a Spatial Attention Sequence (SAS) unit to improve global environment perception at a low computational cost. Comprehensive experimental evaluations on the Cityscapes dataset demonstrate that the SLTM Network excels in balancing speed and accuracy, achieving an mIoU of 70.5% with only 4.07M parameters and an impressive inference speed of 267.1 FPS. On the embedded device Jetson Xavier NX, it achieves an inference speed of 34.2 FPS. Compared to existing lightweight image segmentation models, the SLTM Network exhibits significant advantages in both processing speed and accuracy, making it particularly suitable for real-time autonomous line-painting machine applications.

关键词： Roads image segmentation Feature extraction Computer architecture Computational efficiency Synthetic aperture sonar Semantics Lightweight image segmentation unmanned line-marking machines deep learning in autonomous driving embedded systems optimization SLTM network real-time road segmentation

来源：评论

学校读者我要写书评

暂无评论

Signal to image Conversion and Convolutional Neural Networks for Physiological Signal processing: A Review

引用

IEEE ACCESS 2024年 12卷 66726-66764页

作者： Vidyasagar, K. E. Ch Kumar, K. Revanth Sai, G. N. K. Anantha Ruchita, Munagala Saikia, Manob Jyoti Univ North Florida Biomed Sensors & Syst Lab Jacksonville FL 32224 USA Osmania Univ Univ Coll Engn Dept Biomed Engn Hyderabad 500007 India Univ North Florida Dept Elect Engn Jacksonville FL 32224 USA

Physiological signals obtained from electroencephalography (EEG), electromyography (EMG), and electrocardiography (ECG) provide valuable clinical information but pose challenges for analysis due to their high-dimensional nature. Traditional machine learning techniques, relying on hand-crafted features from fixed analysis windows, can lead to the loss of discriminative information. Recent studies have demonstrated the effectiveness of deep convolutional neural networks (CNNs) for robust automated feature learning from raw physiological signals. However, standard CNN architectures require two-dimensional image data as input. This has motivated research into innovative signal-to-image (STI) transformation techniques to convert one-dimensional time series into images preserving spectral, spatial, and temporal characteristics. This paper reviews recent advances in strategies for physiological signal-to-image conversion and their applications using CNNs for automated processing tasks. A systematic analysis of EEG, EMG, and ECG signal transformation and CNN-based analysis techniques spanning diverse applications, including brain-computer interfaces, seizure detection, motor control, sleep stage classification, arrhythmia detection, and more, are presented. Key insights are synthesized regarding the relative merits of different transformation approaches, CNN model architectures, training procedures, and benchmark performance. Current challenges and promising research directions at the intersection of deep learning and physiological signal processing are discussed. This review aims to catalyze continued innovations in effective end-to-end systems for clinically relevant information extraction from multidimensional physiological data using convolutional neural networks by providing a comprehensive overview of state-of-the-art techniques.

关键词： Biomedical signal analysis convolutional neural networks deep learning machine learning physiological signals signal-to-image conversion

来源：评论

学校读者我要写书评

暂无评论

A real-time Tracking Approach for Moving Objects Based on an Integrated Algorithm of YOLOv7 and SORT

引用

JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2025年第4期34卷

作者： Wang, Lei Liu, Tongtong Yellow River Conservancy Tech Inst Coll Elect Engn Kaifeng 475004 Henan Peoples R China

Mobile target tracking remains a significant issue in smart cities. Due to complex changes in time and space of targets, real-time tracking remains a challenging problem. As a result, this paper proposes a real-time tracking approach for moving objects by combining the advantages of YOLOv7 and SORT algorithms. First, we use the YOLOv7 algorithm for object detection, which has the characteristics of high accuracy and efficiency. Then, we apply the SORT algorithm to the target tracking stage, which estimates and updates the target state through Kalman filtering. The collaborative function of the two parts is expected to achieve high-quality tracking of moving targets. Besides, this paper also demonstrates experiments and analysis on image datasets. The experimental results show that the proposed algorithm has achieved good performance in real-time tracking of moving targets. Compared with traditional methods, it can more accurately predict the position and trajectory of targets and has better real-time performance. In addition, the proposed algorithm is equally effective for target tracking in complex scenes, such as multi-target tracking and target occlusion. Future research can further optimize the performance of algorithms to cope with more complex scenarios and problems.

关键词： Moving target tracking real-time image processing YOLOv7 Kalman filtering deep learning

来源：评论

学校读者我要写书评

暂无评论

Self-supervised learning for Speech Emotion Recognition Task Using Audio-visual Features and Distil Hubert Model on BAVED and RAVDESS Databases

引用

Journal of Systems Science and Systems Engineering 2024年第5期33卷 576-606页

作者： Karim Dabbabi Abdelkarim Mars Research Unite of Analyse and Processing of Electrical and Energetic Systems Faculty of Sciences of TunisTunis El-Manar University2092Tunis-Tunisia Research Laboratory in Algebra Numbers Theory and Intelligent SystemsFaculty of Sciences of Monastir90 Mohamed V street5000-MonastirTunisia

Existing pre-trained models like Distil HuBERT excel at uncovering hidden patterns and facilitating accurate recognition across diverse data types, such as audio and visual information. We harnessed this capability to develop a deep learning model that utilizes Distil HuBERT for jointly learning these combined features in speech emotion recognition (SER). Our experiments highlight its distinct advantages: it significantly outperforms Wav2vec 2.0 in both offline and real-time accuracy on RAVDESS and BAVED datasets. Although slightly trailing HuBERT’s offline accuracy, Distil HuBERT shines with comparable performance at a fraction of the model size, making it an ideal choice for resource-constrained environments like mobile devices. This smaller size does come with a slight trade-off: Distil HuBERT achieved notable accuracy in offline evaluation, with 96.33% on the BAVED database and 87.01% on the RAVDESS database. In real-time evaluation, the accuracy decreased to 79.3% on the BAVED database and 77.87% on the RAVDESS database. This decrease is likely a result of the challenges associated with real-time processing, including latency and noise, but still demonstrates strong performance in practical scenarios. Therefore, Distil HuBERT emerges as a compelling choice for SER, especially when prioritizing accuracy over real-time processing. Its compact size further enhances its potential for resource-limited settings, making it a versatile tool for a wide range of applications.

关键词： Wav2vec 2.0 Distil HuBERT HuBERT SER audio and audio-visual features

来源：评论

学校读者我要写书评

暂无评论

Interpretable and Efficient Beamforming-Based deep learning for Single-Snapshot DOA Estimation

引用

IEEE SENSORS JOURNAL 2024年第14期24卷 22096-22105页

作者： Zheng, Ruxin Sun, Shunqiao Liu, Hongshan Chen, Honglei Li, Jian Univ Alabama Dept Elect & Comp Engn Tuscaloosa AL 38457 USA Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

We introduce an interpretable deep-learning (DL) approach for direction-of-arrival (DOA) estimation with a single snapshot. Classical subspace-based methods, such as multiple signal classification (MUSIC) and estimation of parameters by rotational invariant technique (ESPRIT), use spatial smoothing on uniform linear arrays (ULAs) for single-snapshot DOA estimation but face drawbacks in reduced array aperture and inapplicability to sparse arrays. Single-snapshot methods, such as compressive sensing (CS) and iterative adaptive approach (IAA), encounter challenges with high-computational costs and slow convergence, hampering real-time use. Recent DL DOA methods offer promising accuracy and speed. However, the practical deployment of deep networks is hindered by their black-box nature. To address this, we propose a deep-minimum power distortionless response (MPDR) network translating MPDR-type beamformer into DL, enhancing generalization and efficiency. Comprehensive experiments conducted using both simulated and real-world datasets substantiate its dominance in terms of inference time and accuracy in comparison with conventional methods. Moreover, it excels in terms of efficiency, generalizability, and interpretability when contrasted with other DL DOA estimation networks.

关键词： Direction-of-arrival estimation Estimation deep learning Covariance matrices Sensors Mathematical models Array signal processing automotive radar deep learning (DL) interpretability single-snapshot direction-of-arrival (DOA) estimation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：