IF 8 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-07-31 DOI: 10.1016/j.engappai.2025.111902

Jiahao Liu , Yiming Zhang , Liang Song , Zheng Tong

{"title":"SCB-ADAE: An attention-based deep autoencoder for ground penetrating radar signal denoising","authors":"Jiahao Liu , Yiming Zhang , Liang Song , Zheng Tong","doi":"10.1016/j.engappai.2025.111902","DOIUrl":"10.1016/j.engappai.2025.111902","url":null,"abstract":"<div><div>In buried object detection, recorded signals of a ground penetrating radar (GPR) inevitably include noise interference owing to complex underground environments. Existing rule- and data-driven denoising methods struggle to handle non-Gaussian and real-world noise because the rule-driven ones rely on the assumptions of simplified noise characteristics and the data-driven ones cannot capture fine- and global-scale features of a GPR signal well. To address the problem, this study proposes an attention-based denoising model called the Swin-Conv Block with Attention Denoising Autoencoder (SCB-ADAE). The model first feeds a GPR signal into a SCB module, which extracts a tensor with the fine-scale features in the signal, such as sharp reflective interfaces and abrupt amplitude variations. The feature tensor then passes through an ADAE module that uses encoder-decoder structure with the self-attention to enhances the representation of the global-scale signal features. Finally, the feature tensor from the ADAE module is decoded by another SCB module to generate a denoised GPR signal, where the tensor includes the fine-scale and global features of the raw signal. An experiment with three types of GPR signals demonstrates the effectiveness of the proposed model: radar signals with Gaussian noise, radar signals with inhomogeneous-material noise, and real-world signals. radar signals with Gaussian noise, radar signals with inhomogeneous-material noise, and real-world signals. Experimental results demonstrate that the proposed model outperforms other state-of-the-art denoising methods on denosing the three types of GPR signals, where the signal-to-noise ratio, peak signal-to-noise ratio, and structural similarity index are improved to 20.64, 14.59, and 0.366, respectively.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111902"},"PeriodicalIF":8.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Semi-Supervised Risk Control via Prediction-Powered Inference 基于预测推理的半监督风险控制

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-31 DOI: 10.1109/tpami.2025.3594263

Bat-Sheva Einbinder, Liran Ringel, Yaniv Romano

引用次数: 0

Robust Motion Control of DC Servo Systems With Saturation Nonlinearity and Control Effort Constraint 具有饱和非线性和控制力约束的直流伺服系统鲁棒运动控制

IF 12.3 1区计算机科学

IEEE Transactions on Industrial Informatics Pub Date : 2025-07-31 DOI: 10.1109/tii.2025.3588599

S. Sai Lakshmi, R. Jeyasenthil, Tarakanath Kobaku, Vivek Agarwal

引用次数: 0

DPL++: Advancing the Network Performance via Image and Label Perturbations dpl++：通过图像和标签扰动提高网络性能

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-31 DOI: 10.1109/tpami.2025.3594149

Zifan Song, Xiao Gong, Guosheng Hu, Shuguang Dou, Qingsong Zhao, Cairong Zhao

引用次数: 0

Fine-Grained Transductive Prototypical Network Based Few-Shot Signal Modulation Classification Using Coarse Labels 基于粗标签的细粒度转导原型网络的少射信号调制分类

IF 8.6 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2025-07-31 DOI: 10.1109/tccn.2025.3594331

Shuai Feng, Yatong Wang, Zhongyi Wen, Luyan Xu, Mu Yan

引用次数: 0

Energy-Efficient Task Offloading and DNN Inference in Dynamic STAR-RIS Assisted MEC With Decomposition Based DRL 基于分解DRL的动态STAR-RIS辅助MEC的节能任务卸载和DNN推理

IF 10.4 1区计算机科学

IEEE Transactions on Wireless Communications Pub Date : 2025-07-31 DOI: 10.1109/twc.2025.3592255

Yiting Huang, Baoshan Lu, Yuling Luo, Junli Fang, Qiang Fu, Sheng Qin, Junxiu Liu

引用次数: 0

DeclareAligner: A leap towards efficient optimal alignments for declarative process model conformance checking DeclareAligner：向声明性流程模型一致性检查的有效最佳对齐的飞跃

IF 8 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-07-31 DOI: 10.1016/j.engappai.2025.111683

Jacobo Casas-Ramos, Manuel Lama, Manuel Mucientes

{"title":"DeclareAligner: A leap towards efficient optimal alignments for declarative process model conformance checking","authors":"Jacobo Casas-Ramos, Manuel Lama, Manuel Mucientes","doi":"10.1016/j.engappai.2025.111683","DOIUrl":"10.1016/j.engappai.2025.111683","url":null,"abstract":"<div><div>Conformance checking is a crucial aspect of process mining, enabling organizations to identify deviations between actual process behavior and modeled expectations. At the heart of conformance checking lies the concept of optimal alignments, which provide a detailed, cost-minimized mapping of observed behavior to expected behavior. Optimal alignments facilitate the identification of root causes of non-conformity and guide corrective actions. This is a critical area where Artificial Intelligence (AI) plays a pivotal role in driving effective process improvement. However, computing optimal alignments poses significant computational challenges due to the vast search space inherent in declarative process models. Consequently, existing approaches often struggle with scalability and efficiency, limiting their applicability in real-world settings. This paper introduces <span>DeclareAligner</span>, a novel algorithm that uses the A* search algorithm, an established AI pathfinding technique, to tackle the problem from a fresh perspective leveraging the flexibility of declarative models. Key features of <span>DeclareAligner</span> include only performing actions that actively contribute to fixing constraint violations, utilizing a tailored heuristic to navigate towards optimal solutions, and employing early pruning to eliminate unproductive branches, while also streamlining the process through preprocessing and consolidating multiple fixes into unified actions. The proposed method is evaluated using 8054 synthetic and real-life alignment problems, demonstrating its ability to efficiently compute optimal alignments by significantly outperforming the current state of the art. By enabling process analysts to more effectively identify and understand conformance issues, <span>DeclareAligner</span> has the potential to drive meaningful process improvement and management.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111683"},"PeriodicalIF":8.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Reversible data hiding in encrypted images based on bidirectional encoding for payload enhancing 基于双向编码的有效载荷增强加密图像中可逆数据隐藏

IF 3.7 2区计算机科学

Journal of Information Security and Applications Pub Date : 2025-07-31 DOI: 10.1016/j.jisa.2025.104183

Cheng-Ta Huang , Thi Thu-Ha Dang , Chi-Yao Weng

{"title":"Reversible data hiding in encrypted images based on bidirectional encoding for payload enhancing","authors":"Cheng-Ta Huang , Thi Thu-Ha Dang , Chi-Yao Weng","doi":"10.1016/j.jisa.2025.104183","DOIUrl":"10.1016/j.jisa.2025.104183","url":null,"abstract":"<div><div>Reversible data hiding in encrypted images (RDHEI) has received substantial attention as a pivotal research domain in the field of secure data embedding. This paper presents a novel RDHEI approach using bidirectional flag encoding. Our method substantially enhances embedding capacity while ensuring both lossless image recovery and accurate extraction of hidden data. The original image is processed using a hybrid prediction model to derive prediction error values. Subsequently, the image undergoes encryption through a combination of a stream cipher technique and block scrambling to guarantee robust security. Finally, the bidirectional flag encoding technique is employed to vacate the room for data embedding. By leveraging spatial correlation among pixels, this scheme achieves an improved embedding rate. Experimental evaluations reveal that the proposed method achieves an enhanced payload capacity in comparison to existing state-of-the-art RDHEI techniques. The average embedding rates on datasets of BOSSbase and BOWS-2 are 3.76 bpp and 3.37 bpp, respectively.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"93 ","pages":"Article 104183"},"PeriodicalIF":3.7,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Proving the stability of cycle navigation using capture sets 利用捕获集证明循环导航的稳定性

IF 3.1 3区计算机科学

Mechatronics Pub Date : 2025-07-31 DOI: 10.1016/j.mechatronics.2025.103385

Quentin Brateau, Loïck Degorre, Fabrice Le Bars, Luc Jaulin

{"title":"Proving the stability of cycle navigation using capture sets","authors":"Quentin Brateau, Loïck Degorre, Fabrice Le Bars, Luc Jaulin","doi":"10.1016/j.mechatronics.2025.103385","DOIUrl":"10.1016/j.mechatronics.2025.103385","url":null,"abstract":"<div><div>Navigating Autonomous Underwater Vehicles (AUVs) presents significant challenges due to the absence of traditional localization systems. Cycle navigation emerges as a promising paradigm, enabling reliable navigation using minimal exteroceptive measurements. This approach leverages predefined cyclic trajectories, which are stabilized based on environmental feedback, ensuring frugal and discreet operations without reliance on high computational power or extensive sensor systems. This work aims to prove the stability of the cycle navigation. As cycle navigation is a non-linear system governed by a discrete inclusion condition, conventional methods have trouble to prove its stability. For this reason, this paper focuses on set methods to prove the stability of cycle navigation. The stability is proven by exhibiting a positive invariant set, which is a set stable by application of the evolution function of the system. This ensures that the evolution function will not remove states from the positively invariant set. Then, the characterization of the capture basin is an asset when performing cycle navigation, as it represents the set of initial states for the system which leads to the positive invariant set. Once the system reaches either the capture basin or the positive invariant set, which are generalized as a capture set, it remains captured forever. This approach not only guarantees the stability of the system in the neighborhood of the equilibrium point, but also establishes that it exists an area in which the stability of the cycle navigation will lead to a stable behavior. This work offers a robust, computationally efficient alternative to traditional stability methods, particularly suited for resource-constrained AUVs, because the underwater environment lacks suitable, cheap and easy-to-use localization methods, which forces us finding alternative ways to navigate and explore this particular environment.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103385"},"PeriodicalIF":3.1,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Strategic Intelligence Management (ISO 56006)—Using AI by the Innovation Agent Task Force in the Automotive Skills Alliance (ASA) 战略情报管理(ISO 56006) -汽车技能联盟（ASA）创新代理工作组使用人工智能

IF 1.8 4区计算机科学

Journal of Software-Evolution and Process Pub Date : 2025-07-31 DOI: 10.1002/smr.70038

Laura Aschbacher, Mikus Zelmenis, Richard Messnarz, Damjan Ekert

{"title":"Strategic Intelligence Management (ISO 56006)—Using AI by the Innovation Agent Task Force in the Automotive Skills Alliance (ASA)","authors":"Laura Aschbacher, Mikus Zelmenis, Richard Messnarz, Damjan Ekert","doi":"10.1002/smr.70038","DOIUrl":"https://doi.org/10.1002/smr.70038","url":null,"abstract":"<div>\u0000 \u0000 <p>In the EU blueprint project FLAMENCO (Forward Looking Approaches for Green Mobility Ecosystem Network Collaboration), an innovation agent task force has been founded, which acts as an expert panel to elaborate a skills set of an innovation agent for automotive and establishes an innovation capability assessment model based on the ISO 560xx Innovation Management Systems norm series. In 2024, a new EU project TRIREME (Digital & Green Skills Towards Future of the Mobility Ecosystem, 2024–2027) started, which builds on this existing innovation agent task force and provides resources to elaborate MOOcs (Massive Open Online Courses) per chapter of the ISO 5600x norm applying new tools like AI. The MOOC is then configured in a European Skills Hub of the ASA (Automotive Skills Alliance). ASA represents the pact for skills partner in the EU Erasmus+ program for the automotive sector. The research work about the use of AI (artificial intelligence) for the implementation of specific ISO 560xx chapters will be published. This paper is about the results of the work on the ISO 56006 Strategic Intelligence Management implementation using AI in the TRIREME project.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 8","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144740510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

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