计算机科学最新文献

{"title":"ATTD and ATDS detecting abnormal trajectory detection for urban traffic data","authors":"Xi-Te Wang,&nbsp;Zheng Xu,&nbsp;Xiao-Yue Liao,&nbsp;Mei Bai,&nbsp;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}

{"title":"Longitudinal and lateral control of vehicle platoons: A unifying framework to prevent corner cutting","authors":"Paul Wijnbergen ,&nbsp;Mark Jeeninga ,&nbsp;Redmer de Haan ,&nbsp;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}

{"title":"Hybrid modelling of ruble exchange rates amidst the Russo-Ukrainian conflict using swarm and fuzzy neural networks","authors":"Raad Abdelhalim Ibrahim Alsakarneh ,&nbsp;Sagiru Mati ,&nbsp;Goran Yousif Ismael ,&nbsp;Serag Masoud ,&nbsp;Nazifi Aliyu ,&nbsp;Ahmed Samour ,&nbsp;Berna Uzun","doi":"10.1016/j.engappai.2025.110854","DOIUrl":"10.1016/j.engappai.2025.110854","url":null,"abstract":"<div><div>The existing models for predicting the Ruble exchange rate against the Chinese Yuan (CNY), Euro (EUR), British Pound (GBP), and United States Dollar (USD) may prove inadequate considering the Russia-Ukraine war. This study employs the Autoregressive Integrated Moving Average (ARIMA), the Evidential Neural Network with Gaussian Random Fuzzy Numbers (EVNN), and the Artificial Neural Network optimised with Particle Swarm Optimisation (ANN-PSO), as well as hybrids of ARIMA and EVNN (ARIMA-EVNN) and ARIMA and ANN-PSO (ARIMA-ANN-PSO), to predict CNY, EUR, GBP, and USD. When compared with ARIMA, for the training sample, ARIMA-ANN-PSO enhances the accuracy of ARIMA by 66.89%, 66.20%, 72.97%, and 66.89% for CNY, EUR, GBP, and USD respectively, while ARIMA-EVNN improves the accuracy of ARIMA for CNY by 66.21%, EUR by 78.87%, GBP by 82.43%, and USD by 80.33%. For the testing sample, the ARIMA-ANN-PSO model enhances the predictive accuracy of ARIMA by 65.60%, 64.39%, 45.74%, and 55.28% respectively, while the ARIMA-EVNN model improves the accuracy by 58.73% for CNY, 80.30% for EUR, 83.72% for GBP, and 86.18% for USD. The Russia-Ukraine war dummy was included to capture structural changes in Ruble exchange rate dynamics. Including war-related information does not change the accuracy of ARIMA model for CNY likely because China has not imposed sanctions on Russia, but improves it for EUR, GBP, and USD. However, the accuracy of EVNN decreases after integrating this information for all exchange rates. The findings can provide assistance to bureaux de change, foreign exchange traders, and governments, enabling them to make well-informed decisions.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"153 ","pages":"Article 110854"},"PeriodicalIF":7.5,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876629","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}

{"title":"Hiding region constraints for black-box optimization. Application to camera placement in a virtual industrial environment using Evolutionary Computation","authors":"N.C. Cruz ,&nbsp;M. Rouret ,&nbsp;E.M. Ortigosa ,&nbsp;E. Ros ,&nbsp;J.A. Garrido","doi":"10.1016/j.swevo.2025.101946","DOIUrl":"10.1016/j.swevo.2025.101946","url":null,"abstract":"<div><div>Many real-world optimization problems related to physical environments have heavily constrained search spaces, which hinders the direct application of meta-heuristics and similar black-box methods. This work describes how to avoid region constraints and self-adapt search spaces without renouncing competitive solutions. The proposal relies on defining a gateway function that hides environment-specific placement constraints and is compatible with regular meta-heuristics and simulation-based optimization. The function can show a standard box-constrained domain encapsulating the real places involved. It has been successfully applied to automatic camera placement for task observation in a particle accelerator. The environment and the process of interest are simulated in the Unity game engine, which defines a cutting-edge trend in the design of such facilities. The primary optimization method tested is the genetic algorithm of MATLAB’s Global Optimization Toolbox, an industry standard that achieves remarkable results. The widespread Teaching–Learning-Based Optimizer (TLBO) and a random search have also been tried to complement the study. According to the results, the proposal does not prevent the advanced optimizers from finding camera arrangements that outperform (and are validated by) a human expert. It also allows the random search to find reasonable arrangements despite the underlying intricate set of constraints.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101946"},"PeriodicalIF":8.2,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876791","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}

{"title":"INN/ENNS/JNNS - Membership Applic. Form","authors":"","doi":"10.1016/S0893-6080(25)00414-9","DOIUrl":"10.1016/S0893-6080(25)00414-9","url":null,"abstract":"","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"187 ","pages":"Article 107535"},"PeriodicalIF":6.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874192","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}

{"title":"Parallel multi-layer sensor fusion for pipe leak detection using multi-sensors and machine learning","authors":"Nicholas Satterlee ,&nbsp;Xiaowei Zuo ,&nbsp;Chang-Whan Lee ,&nbsp;Choon-Wook Park ,&nbsp;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}

{"title":"ASDS-you only look once version 8: A real-time segmentation method for cross-scale prefabricated laminated slab components","authors":"Lin Li ,&nbsp;Qing Jiang ,&nbsp;Guanting Ye ,&nbsp;Xun Chong ,&nbsp;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 (mAP₅₀) 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}

{"title":"Causal Attention Transformer for Video Text Retrieval","authors":"Hua Lan,&nbsp;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}

{"title":"Expert system for extracting keywords in educational texts and textbooks based on transformers models","authors":"Irene Cid Rico,&nbsp;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}

{"title":"Krein space-based approach to dynamic event-triggered H∞ filtering for a class of nonlinear discrete time systems","authors":"Maiying Zhong ,&nbsp;Xiaoqiang Zhu ,&nbsp;Shuai Liu ,&nbsp;Qing-Long Han ,&nbsp;Donghua Zhou","doi":"10.1016/j.automatica.2025.112343","DOIUrl":"10.1016/j.automatica.2025.112343","url":null,"abstract":"<div><div>This paper investigates the problem of <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> filtering for a class of nonlinear discrete time systems under a dynamic event-triggered scheme. First, a new general form of event-triggered filters is considered so that the filtering error system achieves full decoupling from the so-called event-triggered transmission error, which is the major concern of <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> filtering performance degradation caused by an event-triggering mechanism. Moreover, the design of the event-triggered <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> filter and event-triggering mechanism can be carried out independently. Second, the event-triggered <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> nonlinear filtering is formulated as a problem of indefinite quadratic form minimum, and a Krein space-based approach is proposed for the design of the event-triggered <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> filter. Based on the first-order Taylor approximation, sufficient and necessary conditions for the existence of a dynamic event-triggered <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> filter are derived by employing Krein space projection, and a feasible solution is given in terms of Riccati recursions for guaranteeing the prescribed <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> performance. Third, an algorithm with time-update and event-update recursions is also provided to perform the dynamic event-triggered <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> nonlinear filtering. It is shown that the proposed Krein space-based approach is computational attractive and easy to implement. Finally, a three-tank system is considered as an illustrative example to demonstrate the effectiveness of the proposed approach.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"177 ","pages":"Article 112343"},"PeriodicalIF":4.8,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874524","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}