计算机科学最新文献

英文中文

IF： -

A Strong PUF Based Security Protocol to Protect AI Model Parameters against Privacy Information Leakage

IF 10.6 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Internet of Things Journal

Pub Date : 2025-02-21 DOI: 10.1109/jiot.2025.3544555

Ziyu Zhou, Gang Li, Yuejun Zhang, Ziyang Zheng, Tengfei Yuan, Pengjun Wang

引用次数: 0

Socially Acceptable Human-like Behavior Planning for Connected Cars on Signalized Road Network

IF 6.8 2区计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Vehicular Technology

Pub Date : 2025-02-21 DOI: 10.1109/tvt.2025.3543155

Solyeon Kwon, Tam W. Nguyen, Kyoungseok Han

引用次数: 0

ILNet: Low-Level Matters for Salient Infrared Small Target Detection

IF 4.4 2区计算机科学 Q1 ENGINEERING, AEROSPACE

IEEE Transactions on Aerospace and Electronic Systems

Pub Date : 2025-02-21 DOI: 10.1109/taes.2025.3544613

Haoqing Li, Jinfu Yang, Runshi Wang, Yifei Xu

引用次数: 0

Intelligent chatter detection in high-speed milling using successive variational mode decomposition and a multi-channel feature fusion network

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-02-21 DOI: 10.1016/j.compind.2025.104266

Liangshi Sun , Xianzhen Huang , Jiatong Zhao , Zhiyuan Jiang , Fusheng Jiang

In high-speed milling, chatter detection plays an important role in ensuring surface quality and safe machining. Traditionally, chatter detection is performed by manually setting the feature threshold, which is unreliable. In this paper, an intelligent chatter detection method is proposed based on deep learning. The proposed method is featured by automatic chatter detection based on multi-channel features, and it is applicable in different milling conditions. To adaptively obtain the chatter signal and avoid the problem of modal mixing, the successive variational mode decomposition method is first used to extract the chatter frequency components without selecting parameters. Then, multi-channel features are extracted from the reconstructed chatter signal, and sensitive features strongly related to the milling chatter are selected based on mutual information metric. Next, a novel multi-channel feature fusion network, composed of the gated attention mechanism, ResNet module, CapsNet module, and classification module, is constructed to mine feature information and implement chatter detection. Finally, the signal data are acquired through a series of milling experiments. The identification performance of the model is evaluated in three scenarios, and an average accuracy of 0.9887 is achieved. In addition, ablation experiments and comparative studies with other detection methods are performed. The results show that the proposed method can improve the accuracy and generalization of chatter detection.

{"title":"Intelligent chatter detection in high-speed milling using successive variational mode decomposition and a multi-channel feature fusion network","authors":"Liangshi Sun , Xianzhen Huang , Jiatong Zhao , Zhiyuan Jiang , Fusheng Jiang","doi":"10.1016/j.compind.2025.104266","DOIUrl":"10.1016/j.compind.2025.104266","url":null,"abstract":"<div><div>In high-speed milling, chatter detection plays an important role in ensuring surface quality and safe machining. Traditionally, chatter detection is performed by manually setting the feature threshold, which is unreliable. In this paper, an intelligent chatter detection method is proposed based on deep learning. The proposed method is featured by automatic chatter detection based on multi-channel features, and it is applicable in different milling conditions. To adaptively obtain the chatter signal and avoid the problem of modal mixing, the successive variational mode decomposition method is first used to extract the chatter frequency components without selecting parameters. Then, multi-channel features are extracted from the reconstructed chatter signal, and sensitive features strongly related to the milling chatter are selected based on mutual information metric. Next, a novel multi-channel feature fusion network, composed of the gated attention mechanism, ResNet module, CapsNet module, and classification module, is constructed to mine feature information and implement chatter detection. Finally, the signal data are acquired through a series of milling experiments. The identification performance of the model is evaluated in three scenarios, and an average accuracy of 0.9887 is achieved. In addition, ablation experiments and comparative studies with other detection methods are performed. The results show that the proposed method can improve the accuracy and generalization of chatter detection.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"167 ","pages":"Article 104266"},"PeriodicalIF":8.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global polynomial synchronization of proportional delay memristive neural networks with uncertain parameters and its application to image encryption

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-21 DOI: 10.1016/j.engappai.2025.110290

Yan Wan, Liqun Zhou, Jiapeng Han

This article explores the global polynomial synchronization (GPS) for a type of proportional delay memristive neural networks (PDMNNs), uncertain parameters are considered. First, the theory of differential inclusion is utilized, and then the error system is obtained. Secondly, combining the principles of sliding mode control (SMC) and adaptive control, two different controllers are designed to achieve GPS between the obtained drive–response system. Then, two GPS criteria are obtained through the application of Lyapunov stability theory and inequality analysis techniques. Ultimately, we offer three numerical exemplifications to corroborate the efficacy of the obtained results, along with a demonstration of an application pertaining to image encryption.

引用次数: 0

PolarDETR: Polar Parametrization for vision-based surround-view 3D detection

IF 4.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Image and Vision Computing

Pub Date : 2025-02-21 DOI: 10.1016/j.imavis.2025.105438

Shaoyu Chen , Xinggang Wang , Tianheng Cheng , Qian Zhang , Chang Huang , Wenyu Liu

3D detection based on surround-view camera system is a critical and promising technique in autopilot. In this work, we exploit the view symmetry of surround-view camera system as inductive bias to improve optimization and boost performance. We parameterize object’s position by polar coordinate and decompose velocity along radial and tangential direction. And the perception range, label assignment and loss function are correspondingly reformulated in polar coordinate system. This new Polar Parametrization scheme establishes explicit associations between image patterns and prediction targets. Based on it, we propose a surround-view 3D detection method, termed PolarDETR. PolarDETR achieves competitive performance on nuScenes dataset. Thorough ablation studies are provided to validate the effectiveness.

引用次数: 0

State-of-the-art review and benchmarking of barcode localization methods

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-21 DOI: 10.1016/j.engappai.2025.110259

Enrico Vezzali , Federico Bolelli , Stefano Santi , Costantino Grana

Barcodes, despite their long history, remain an essential technology in supply chain management. In addition, barcodes have found extensive use in industrial engineering, particularly in warehouse automation, component tracking, and robot guidance. To detect a barcode in an image, multiple algorithms have been proposed in the literature, with a significant increase of interest in the topic since the rise of deep learning. However, research in the field suffers from many limitations, including the scarcity of public datasets and code implementations which hinders the reproducibility and reliability of published results. For this reason, we developed “BarBeR” (Barcode Benchmark Repository), a benchmark designed for testing and comparing barcode detection algorithms. This benchmark includes the code implementation of various detection algorithms for barcodes, along with a suite of useful metrics. Among the supported localization methods, there are multiple deep-learning detection models, that will be used to assess the recent contributions of Artificial Intelligence to this field. In addition, we provide a large, annotated dataset of 8 748 barcode images, combining multiple public barcode datasets with standardized annotation formats for both detection and segmentation tasks. Finally, we provide a thorough summary of the history and literature on barcode localization and share the results obtained from running the benchmark on our dataset, offering valuable insights into the performance of different algorithms when applied to real-world problems.

{"title":"State-of-the-art review and benchmarking of barcode localization methods","authors":"Enrico Vezzali , Federico Bolelli , Stefano Santi , Costantino Grana","doi":"10.1016/j.engappai.2025.110259","DOIUrl":"10.1016/j.engappai.2025.110259","url":null,"abstract":"<div><div>Barcodes, despite their long history, remain an essential technology in supply chain management. In addition, barcodes have found extensive use in industrial engineering, particularly in warehouse automation, component tracking, and robot guidance. To detect a barcode in an image, multiple algorithms have been proposed in the literature, with a significant increase of interest in the topic since the rise of deep learning. However, research in the field suffers from many limitations, including the scarcity of public datasets and code implementations which hinders the reproducibility and reliability of published results. For this reason, we developed “BarBeR” (Barcode Benchmark Repository), a benchmark designed for testing and comparing barcode detection algorithms. This benchmark includes the code implementation of various detection algorithms for barcodes, along with a suite of useful metrics. Among the supported localization methods, there are multiple deep-learning detection models, that will be used to assess the recent contributions of Artificial Intelligence to this field. In addition, we provide a large, annotated dataset of 8 748 barcode images, combining multiple public barcode datasets with standardized annotation formats for both detection and segmentation tasks. Finally, we provide a thorough summary of the history and literature on barcode localization and share the results obtained from running the benchmark on our dataset, offering valuable insights into the performance of different algorithms when applied to real-world problems.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"147 ","pages":"Article 110259"},"PeriodicalIF":7.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Corrections to “An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification”

IF 3.4 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Access

Pub Date : 2025-02-21 DOI: 10.1109/ACCESS.2025.3542202

Khaoula Taji;Ali Sohail;Tariq Shahzad;Bilal Shoaib Khan;Muhammad Adnan Khan;Khmaies Ouahada

Presents corrections to the paper, Corrections to “An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification”.