计算机科学最新文献

A bidirectional bi-objective graph search model for sustainable urban railway alignment optimization

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-04-26 DOI: 10.1016/j.engappai.2025.110943

Tianlong Zhang , Yan Gao , Shuangting Xu , Ting Deng , Qing He , Paul Schonfeld , Yang Zou , Dong Liang , Ping Wang

{"title":"A bidirectional bi-objective graph search model for sustainable urban railway alignment optimization","authors":"Tianlong Zhang , Yan Gao , Shuangting Xu , Ting Deng , Qing He , Paul Schonfeld , Yang Zou , Dong Liang , Ping Wang","doi":"10.1016/j.engappai.2025.110943","DOIUrl":"10.1016/j.engappai.2025.110943","url":null,"abstract":"<div><div>Designing railway alignments in building-dense urban areas is a challenging task, requiring consideration of both costs and impacts on existing buildings and the environment. Achieving a viable solution necessitates the application of computer-aided techniques for three-dimensional (3D) global path searches while simultaneously optimizing multiple objectives. To tackle this challenge, this study proposes a bidirectional bi-objective graph search model. This model efficiently searches the 3D space to generate high-quality railway alignment solutions that simultaneously consider both comprehensive costs (including railway construction, ecological, and affected building costs) and carbon emissions (covering emissions from railways and buildings). It provides valuable reference solutions for designers, enhancing the design efficiency. The model includes two main innovations: (1) the ability to quickly search the entire 3D space using a graph-based strategy, generating multiple alignment solutions that meet design constraints in a single optimization process, and (2) the ability to accurately and efficiently account for the impact of railway alignments on existing buildings during optimization. Testing the model on a real-world urban case demonstrates its capability to generate multiple alternative railway alignments within minutes. The Pareto balanced solution achieves an 18.91 % reduction in comprehensive costs and a 13.46 % decrease in carbon emissions compared to manual design. The estimation error of affected building areas is approximately 2 %–4 % along the approximately 40 km alignment. Overall, the significance of this study lies in exploring the application of efficient graph search algorithms in the multi-objective optimization design of railway alignments in urban areas, advancing ongoing research in this field.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"153 ","pages":"Article 110943"},"PeriodicalIF":7.5,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874358","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

Mean-shift-based robust distributed set-membership fusion filtering for sensor network systems with outliers

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-04-26 DOI: 10.1016/j.automatica.2025.112345

Hongbo Zhu , Minane Joel Villier Amuri , Jinzhong Shen , Xueyang Li

{"title":"Mean-shift-based robust distributed set-membership fusion filtering for sensor network systems with outliers","authors":"Hongbo Zhu , Minane Joel Villier Amuri , Jinzhong Shen , Xueyang Li","doi":"10.1016/j.automatica.2025.112345","DOIUrl":"10.1016/j.automatica.2025.112345","url":null,"abstract":"<div><div>Outliers can contaminate the communication and measurement processes of many sensor network systems, which may be induced by environmental disturbances, model uncertainties, sensor faults or errors, subnetwork faults or malicious cyberattacks. Once the distributed set-membership filter (DSMF) is used into such sensor network systems with outliers for distributed state estimation, the estimation performance can be seriously degraded in each sensor node. To address this problem, this article proposes a mean-shift-based trust set and zonotope extraction mechanism to modify the zonotopic DSMF toward building resilience and robustness against outliers. The proposed mechanism is capable of sifting out the outlier-contaminated local corrected zonotopes, and a sufficient condition for the full effectiveness of it is given and proved. Based on the proposed mechanism, the mean-shift-based outliers-robust zonotopic DSMF (MSRDSMF) is derived for estimating the state of a sensor network system in a distributed way. Simulation experiment results demonstrate the practical validity and superiority of the MSRDSMF in effectively suppressing the effects of outliers.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"177 ","pages":"Article 112345"},"PeriodicalIF":4.8,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874523","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

Energy-based control approaches for weakly coupled electromechanical systems

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-04-26 DOI: 10.1016/j.automatica.2025.112336

Najmeh Javanmardi , Pablo Borja , Mohammad Javad Yazdanpanah , Jacquelien M.A. Scherpen

引用次数: 0

ATTD and ATDS detecting abnormal trajectory detection for urban traffic data

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-04-26 DOI: 10.1007/s10489-025-06370-z

Xi-Te Wang, Zheng Xu, Xiao-Yue Liao, Mei Bai, Qian Ma

{"title":"ATTD and ATDS detecting abnormal trajectory detection for urban traffic data","authors":"Xi-Te Wang, Zheng Xu, Xiao-Yue Liao, Mei Bai, Qian Ma","doi":"10.1007/s10489-025-06370-z","DOIUrl":"10.1007/s10489-025-06370-z","url":null,"abstract":"<div><p>Abnormal trajectory detection is pivotal for ensuring safety and optimizing operations in urban traffic management. Despite the progress in this field, current anomaly detection methods, such as the Spatial-Temporal Relationship (STR) algorithm, face limitations including high computational complexity due to simultaneous model calculations, delayed anomaly detection, and an inability to estimate anomalies in the remaining route during online detection. These limitations can lead to inefficiencies and reduced safety in real-world applications. In this paper, we address these limitations by introducing two novel algorithms: Anomaly Trajectory Detection based on Temporal model (ATTD) and Abnormal Trajectory Detection based on Dual Standards (ATDS). The ATTD algorithm simplifies the detection process by integrating a unified spatio-temporal model, which reduces computational complexity and accelerates the detection of anomalies. Furthermore, the ATDS algorithm introduces a proactive approach to anomaly detection that not only identifies anomalies in real-time but also predicts potential deviations in the remaining trajectory, thus providing a more comprehensive and timely detection mechanism. Through extensive experiments on real taxi trajectory datasets, we demonstrate that our algorithms significantly outperform the STR algorithm and other existing methods in terms of detection accuracy and computational efficiency. Our work contributes to the field by providing a more robust and efficient approach to anomaly trajectory detection.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875405","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

Longitudinal and lateral control of vehicle platoons: A unifying framework to prevent corner cutting

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-04-26 DOI: 10.1016/j.automatica.2025.112340

Paul Wijnbergen , Mark Jeeninga , Redmer de Haan , Erjen Lefeber

{"title":"Longitudinal and lateral control of vehicle platoons: A unifying framework to prevent corner cutting","authors":"Paul Wijnbergen , Mark Jeeninga , Redmer de Haan , Erjen Lefeber","doi":"10.1016/j.automatica.2025.112340","DOIUrl":"10.1016/j.automatica.2025.112340","url":null,"abstract":"<div><div>The formation of platoons, where groups of vehicles follow each other at close distances, has the potential to increase road capacity. In this paper, a decentralized control approach is presented that extends the well-known constant headway vehicle following approach to the two-dimensional case, <em>i.e.</em>, lateral control is included in addition to the longitudinal control. The presented control scheme employs a direct vehicle-following approach where each vehicle in the platoon is responsible for following the directly preceding vehicle according to a nonlinear spacing policy. The proposed spacing policy is motivated by an approximation of a delay-based spacing policy and results in a generalization of the constant-headway spacing policy to the two-dimensional case. By input–output linearization, necessary and sufficient conditions for the tracking of the nonlinear spacing policy are obtained, which motivate the synthesis of the lateral and longitudinal controllers of each vehicle in the platoon. By deriving an internal state representation of the follower vehicle and showing input-to-state stability, the internal dynamics for each leader–follower subsystem are shown to be well-behaved. Furthermore, the spacing policy results in string-stable behavior of the platoon when driving in the longitudinal direction. The results are illustrated by a simulation.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"177 ","pages":"Article 112340"},"PeriodicalIF":4.8,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874521","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

INN/ENNS/JNNS - Membership Applic. Form

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-26 DOI: 10.1016/S0893-6080(25)00414-9

引用次数: 0

Parallel multi-layer sensor fusion for pipe leak detection using multi-sensors and machine learning

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-04-26 DOI: 10.1016/j.engappai.2025.110923

Nicholas Satterlee , Xiaowei Zuo , Chang-Whan Lee , Choon-Wook Park , John S. Kang

{"title":"Parallel multi-layer sensor fusion for pipe leak detection using multi-sensors and machine learning","authors":"Nicholas Satterlee , Xiaowei Zuo , Chang-Whan Lee , Choon-Wook Park , John S. Kang","doi":"10.1016/j.engappai.2025.110923","DOIUrl":"10.1016/j.engappai.2025.110923","url":null,"abstract":"<div><div>Effective pipe leak detection is critical for maintaining the structural integrity and efficiency of water distribution systems and preventing damage such as sinkholes. Traditional leak detection methods often rely on single sensors, overlooking the advantages of multi-sensor configurations that capture diverse leak-related phenomena. To address this limitation, the study proposes an innovative machine learning-based sensor fusion approach called Parallel Multi-Layer Sensor Fusion (PMLSF), which leverages Convolutional Neural Networks (CNN) and Few-Shot Learning (FSL) to enhance leak detection. PMLSF integrates data from multiple sensors, including hydrophone, acoustic emission, and vibration sensors. The comparative analysis demonstrates that the PMLSF with multi-sensor systems substantially outperforms the CNN-based FSL (CNN-FSL) approach with single-sensor systems, achieving a leak detection accuracy of 97.1 % and leak location classification accuracy between 95.5 % and 97.4 %. Additionally, the study investigates the use of the acoustic emission sensor combined with CNN-FSL for early detection of material failure in pipes, demonstrated by a Pencil Test that achieved 92.3 % accuracy in detecting pencil breakage on the pipe. These results indicate that combination of CNN-FSL for the acoustic emission sensor and PMLSF offers a comprehensive solution for detecting and localizing existing leaks while predicting potential failures, thus laying a robust foundation for the development of reliable and efficient water distribution monitoring systems.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"153 ","pages":"Article 110923"},"PeriodicalIF":7.5,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874491","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

ASDS-you only look once version 8: A real-time segmentation method for cross-scale prefabricated laminated slab components

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-04-26 DOI: 10.1016/j.engappai.2025.110958

Lin Li , Qing Jiang , Guanting Ye , Xun Chong , Xinyu Zhu

{"title":"ASDS-you only look once version 8: A real-time segmentation method for cross-scale prefabricated laminated slab components","authors":"Lin Li , Qing Jiang , Guanting Ye , Xun Chong , Xinyu Zhu","doi":"10.1016/j.engappai.2025.110958","DOIUrl":"10.1016/j.engappai.2025.110958","url":null,"abstract":"<div><div>Prefabricated laminated slabs (PLS) are widely used globally due to their convenience. However, this convenience often comes with challenges in quality control. Although factories currently conduct quality inspections of PLS component arrangements, these inspections mainly rely on manual visual detection methods, which are highly inefficient. This paper proposes an improved You Only Look Once version 8 (YOLOv8) instance segmentation network for PLS inspection. To address the difficulties in detecting PLS components, we introduced multilevel auxiliary information in tandem with the main branch, designed an additional small-target feature fusion layer and segmentation header, and enhanced the original YOLOv8. These improvements allow for the extraction and segmentation of cross-scale information, reducing information gradient loss. However, this approach generates excessive cross-scale information, requiring a balance between the fusion weights of large-scale and small-scale information. To achieve this, we introduced a multilevel feature fusion module Semantic and Detail Infusion (SDI) and a dynamic upsampling module (Dysample). Experimental results show that the proposed method achieved a mean average precision (mAP<sub>50</sub>) of 93.9 % and a detection speed of 108.7 Frames Per Second. Additionally, to support future research and applications, our method provides code that allows for direct derivation of the coordinates of each component class relative to the floor slab. Thus, the proposed detection method holds significant practical application value.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"153 ","pages":"Article 110958"},"PeriodicalIF":7.5,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874318","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

Causal Attention Transformer for Video Text Retrieval

IF 2 4区计算机科学

IET Image Processing Pub Date : 2025-04-26 DOI: 10.1049/ipr2.70093

Hua Lan, Chaohui Lv

{"title":"Causal Attention Transformer for Video Text Retrieval","authors":"Hua Lan, Chaohui Lv","doi":"10.1049/ipr2.70093","DOIUrl":"https://doi.org/10.1049/ipr2.70093","url":null,"abstract":"<p>In the metaverse, video text retrieval is an urgent and challenging need for users in social entertainment. The current attention-based video text retrieval models have not fully explored the interaction between video and text, and only brute force feature embedding. Moreover, Due to the unsupervised nature of attention weight training, existing models have weak generalization performance for dataset bias. Essentially, the model learns that false relevant information in the data is caused by confounding factors. Therefore, this article proposes a video text retrieval method based on causal attention transformer. Assuming that the confounding factors affecting the performance of video text retrieval all come from the dataset, a structural causal model that conforms to the video text retrieval task is constructed, and the impact of confounding effects during data training is reduced by adjusting the front door. In addition, we use causal attention transformer to construct a causal inference network to extract causal features between video text pairs, and replace the similarity statistical probability with causal probability in the video text retrieval framework. Experiments are conducted on the MSR-VTT, MSVD, and LSMDC datasets, which proves the effectiveness of the retrieval model proposed in this paper.</p>","PeriodicalId":56303,"journal":{"name":"IET Image Processing","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ipr2.70093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Expert system for extracting keywords in educational texts and textbooks based on transformers models

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-04-26 DOI: 10.1016/j.eswa.2025.127735

Irene Cid Rico, Jordán Pascual Espada

{"title":"Expert system for extracting keywords in educational texts and textbooks based on transformers models","authors":"Irene Cid Rico, Jordán Pascual Espada","doi":"10.1016/j.eswa.2025.127735","DOIUrl":"10.1016/j.eswa.2025.127735","url":null,"abstract":"<div><div>Automated keyword extraction is widely used for tasks like classification and summarization, but generic methods often fail to address domain-specific requirements. In education, texts are designed to help students grasp and retain key concepts needed for exercises and resolve questions. Despite the variety of existing keyword extraction algorithms, none are specifically adapted to the unique structure and purpose of educational materials like textbooks or lecture notes.Supervised methods have demonstrated their effectiveness in various domains through advanced techniques like contextual embeddings and domain-specific fine-tuning, Our study proposes a novel solution leveraging pretrained transformer models, specifically BERT, to adapt to the structure of educational materials for effective keyword extraction. Our research demonstrates that by fine-tuning BERT models to the specific characteristics of educational texts, we can achieve more accurate and relevant keyword extraction. YodkW, our adapted model, outperforms traditional algorithms in identifying the key concepts that are essential for educational purposes. Performance is quantified using the F1 score relative to text books key terms list, Preliminary results demonstrate that our approach can improve the identification of key concepts pertinent to student understanding and facilitate the automatic generation of test questions.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"282 ","pages":"Article 127735"},"PeriodicalIF":7.5,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874004","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