Engineering Applications of Artificial Intelligence最新文献

{"title":"Informer learning framework based on secondary decomposition for multi-step forecast of ultra-short term wind speed","authors":"Zihao Jin,&nbsp;Xiaomengting Fu,&nbsp;Ling Xiang,&nbsp;Guopeng Zhu,&nbsp;Aijun Hu","doi":"10.1016/j.engappai.2024.109702","DOIUrl":"10.1016/j.engappai.2024.109702","url":null,"abstract":"<div><div>Accurate and dependable wind speed prediction holds paramount importance in facilitating the dispatch and safe operation of power systems. Nonetheless, the inherent instability of wind speed makes wind speed prediction challenging. Consequently, a short-term wind speed prediction framework, amalgamating secondary decomposition (SD)-Informer, has been proposed in this paper. Initially, the variational mode decomposition (VMD) is applied to decompose the primary wind speed sequence. Through the VMD feature decomposition module, it effectively filters and eliminates superfluous noise from wind speed data. Subsequently, the complete ensemble empirical mode decomposition with adaptive noise technique is introduced for a secondary decomposition targeting the high-frequency components derived from the initial decomposition. To address the limitation of neural network models in capturing essential information from lengthy sequential data concurrently, a predictive model based on Informer is proposed as wind speed prediction module, thereby enhancing prediction accuracy. The validation of this hybrid model encompasses four distinct time ranges. Multiple models are scrutinized through comparative analysis to ascertain the superior performance of the proposed hybrid model. The root mean square error of the proposed method is reduced by 33.02%、25.46%、24.26%, and 23.12% compared to gate recurrent unit (GRU), vision Transformer (ViT), attention (AT)-ViT, and CNN-atteneion (CA)-Bi-directional long short-term memory (BiLSTM) respectively. The mean absolute error of the proposed method in the first quarter is 0.432, with model comparison values reduction of 36.19%、22.99%、20.44%, and17.71% respectively. The experimental results indicate that the proposed model exhibits a strong capability in capturing the long-term dependencies between the input and output sequences of wind speed. It can perform multi-step predictions while ensuring high prediction accuracy.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109702"},"PeriodicalIF":7.5,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720679","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":"The nexus of intelligent transportation: A lightweight Bi-input fusion detection model for autonomous-rail rapid transit","authors":"Hongjie Tang ,&nbsp;Jirui Wang ,&nbsp;Jiaoyi Wu ,&nbsp;Yanni Zhao ,&nbsp;Jiangfan Chen ,&nbsp;Fujian Liang ,&nbsp;Zutao Zhang","doi":"10.1016/j.engappai.2024.109705","DOIUrl":"10.1016/j.engappai.2024.109705","url":null,"abstract":"<div><div>Autonomous-rail Rapid Transit (ART) possesses various advantages in intelligent transportation, but it does not effectively recognize road conditions caused solely by deploying single-modal cameras. In this paper, a lightweight fusion-based object detection neural network was designed with multi-modal sensors for the ART. Firstly, the Light Detection and Ranging (LiDAR) applied additional encoding and preprocessing to the point cloud. Secondly, a backbone and a detection head of the network structure were proposed through re-parameterization and pruning techniques. Furthermore, a fusion module was designed with a selective soft attention mechanism to fuse the extracted features. The proposed model was tested on the open autonomous driving dataset; it achieved a 7.38% improvement in mean average precision (mAP) compared to the original you only look once (YOLO) as well as other state-of-the-art (SOTA) models. Finally, practical experiments were conducted in the maintenance center of ART to simulate the operational scenarios and validate the feasibility of the proposed method in this study. By fully utilizing the information in different modalities and addressing the limitations of single-modal recognition, efforts were made to improve the robustness of road object detection for ART under different road conditions. Consequently, our method provides effective solutions which benefit intelligent transportation with advanced algorithms and strategies.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109705"},"PeriodicalIF":7.5,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720676","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":"Multilingual entity alignment by abductive knowledge reasoning on multiple knowledge graphs","authors":"Muhammad Usman Akhtar ,&nbsp;Jin Liu ,&nbsp;Zhiwen Xie ,&nbsp;Xiaohui Cui ,&nbsp;Xiao Liu ,&nbsp;Bo Huang","doi":"10.1016/j.engappai.2024.109660","DOIUrl":"10.1016/j.engappai.2024.109660","url":null,"abstract":"<div><h3>Objectives:</h3><div>Entity alignment (EA) seeks to identify similar real-world objects in different multilingual knowledge graphs (KGs), also known as ontology alignment. EA assists in handling a wide range of language semantics and in building integrated knowledge bases. However, most mainstream studies have focused on structural information, paying little attention to insufficient contextual information and limited handling of complex relationships. This paper aims to address these limitations and improve EA performance and efficiency.</div></div><div><h3>Methods:</h3><div>This paper investigates multilingual EA techniques and proposes a novel Abductive Knowledge Reasoning (AKR) model to address these issues. AKR can compute complex relationship semantics context by reasoning and enrich counterpart entity contextual information through centrality calculation, which helps connect distant entities in multilingual KGs.</div></div><div><h3>Novelty:</h3><div>The proposed AKR model introduces a new approach to EA by integrating centrality calculation and relational semantics reasoning. This method overcomes the limitations of existing EA techniques by effectively handling insufficient contextual information and complex relationships in multilingual KGs.</div></div><div><h3>Findings:</h3><div>AKR outperforms all state-of-the-art EA models across five datasets. AKR achieves <span><math><mrow><mi>H</mi><mi>i</mi><mi>t</mi><mi>@</mi><mn>1</mn></mrow></math></span> score of 79.4%, for entity alignment between Chinese-to-English knowledge graphs representing 19.9% improvement over the best-performing translation-based model, Neighborhood-Aware Attentional Representation Entity Alignment, and a 5.0% improvement over the best-performing graph neural network-based model, Relational Semantics Augmentation.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109660"},"PeriodicalIF":7.5,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720682","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":"Workload-based adaptive decision-making for edge server layout with deep reinforcement learning","authors":"Shihua Li ,&nbsp;Yanjie Zhou ,&nbsp;Bing Zhou ,&nbsp;Zongmin Wang","doi":"10.1016/j.engappai.2024.109662","DOIUrl":"10.1016/j.engappai.2024.109662","url":null,"abstract":"<div><div>Mobile edge computing (MEC) is crucial in applications such as intelligent transportation, innovative healthcare, and smart cities. By deploying servers with computing and storage capabilities at the network edge, MEC enables low-latency services close to end users. However, the configuration of edge servers needs to meet the low-latency requirements and effectively balance the servers’ workloads. This paper proposes an adaptive layout and dynamic optimization method, modeling the edge server layout problem as a Markov decision process. It introduces a workload-based server placement rule that adjusts the locations of edge servers according to the load of base stations, enabling the learning of low-latency and load-balanced server layout strategies. Experimental validation on a real dataset from Shanghai Telecom shows that the proposed algorithm improves average latency performance by about 40% compared to existing technologies, and enhances workload balancing performance by about 17%.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109662"},"PeriodicalIF":7.5,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720681","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":"Anomaly detection in Smart-manufacturing era: A review","authors":"Iñaki Elía,&nbsp;Miguel Pagola","doi":"10.1016/j.engappai.2024.109578","DOIUrl":"10.1016/j.engappai.2024.109578","url":null,"abstract":"<div><div>Manufacturing downtime due to faults is costly and disruptive. With the increasing availability of real-time data in modern Smart Manufacturing (SM) environments, effective anomaly detection (AD) has become crucial but challenging due to diverse scenarios and methods. This paper aims to present a comprehensive review of state-of-the-art AD methods tailored for SM, to facilitate their implementation in real manufacturing environments while providing a foundation for future research. First, it introduces a structured SM classification framework highlighting recent and successful AD algorithms applied in real-world scenarios with a valuable repository of over 100 manufacturing datasets to support further research. Second, an extensive experimental evaluation of 16 AD algorithms across 29 SM datasets covering a broad and diverse spectrum of techniques, including supervised, unsupervised, and semi-supervised approaches, encompassing classic, deep, and ensemble methods. Finally, insights gained from these experiments are presented providing practical guidance on the most suitable methods for various manufacturing contexts, identifying key challenges and opportunities for future developments.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109578"},"PeriodicalIF":7.5,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720594","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":"Time-lagged relation graph neural network for multivariate time series forecasting","authors":"Xing Feng,&nbsp;Hongru Li,&nbsp;Yinghua Yang","doi":"10.1016/j.engappai.2024.109530","DOIUrl":"10.1016/j.engappai.2024.109530","url":null,"abstract":"<div><div>Recently, Graph Neural Network-based approaches (GNNs) have been widely studied in Multivariate Time Series (MTS) prediction, which could extract information from the closely related variables for prediction. The variables contained in MTS data are lagged correlated, and the future trends of the lagging variables are guided by the leading variables. However, as the existing approaches only focus on delay-free relations, they cannot utilize the guidance information in leading variables to achieve accurate prediction. To address this issue, we propose a novel frame called the Time-Lagged Relation Graph Neural Network (TLGNN) including two key components: the time-lagged relation graph and the time-lagged relation graph learning. The time-lagged relation graph could explicitly model the time-delay relations among MTS variables by connecting variable nodes at lag intervals. The graph learning module could adaptively extract the time-delay relations among MTS variables. Based on the novel designed graph structure, the TLGNN could extract the guidance information from previous values of leading variables to generate more efficient feature representations for prediction. In experiments, the prediction accuracy is significantly improved due to the full exploration of the time-delay relations. Compared with existing methods, the TLGNN achieves the best results in both the single-step prediction and the multi-step prediction tasks.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109530"},"PeriodicalIF":7.5,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720762","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":"A framework for Interpretable deep learning in cross-subject detection of event-related potentials","authors":"Shayan Jalilpour ,&nbsp;Gernot Müller-Putz","doi":"10.1016/j.engappai.2024.109642","DOIUrl":"10.1016/j.engappai.2024.109642","url":null,"abstract":"<div><div>Event-related potential-based Brain-Computer Interfaces are becoming widely popular due to their ability to send commands quickly with high accuracy. However, the stationary characteristics of electroencephalographic signals, coupled with their low signal-to-noise ratio, lead to variations in amplitude, time period, and latency in the patterns of event-related potentials across different trials, sessions, days and subjects. Conventional feature extraction and machine learning algorithms are not designed to handle these differences, requiring the development of methods that can address these variations. Here, we propose a novel lightweight deep neural network for event-related potential classification, consisting of three modules. In this model, we have a spatio-temporal module that learns local features simultaneously across channels and time points. Following this, there's a component extractor module comprising depthwise convolutions, inspired by mixed depthwise convolutions, to capture the event-related potential characteristics with different temporal durations. Lastly, an advanced temporal layer addresses event-related potential shape and scale variations using deformable convolutions. We conducted experiments on event-related potential detection in a subject-independent scenario using one error-related negativity potential dataset and three perturbation-evoked potential datasets. Comparisons were made with established methods including two conventional machine learning algorithms and three well-known deep learning architectures, demonstrating that our model outperformed them in terms of classification accuracy and parameter efficiency. In our analysis, we aimed to understand the model's performance using gradient-weighted class activation mapping and t-distributed stochastic neighbor embedding. These methods facilitated the visualization and interpretation of our model's effectiveness, providing insights into its relationship with the neuroscientific characteristics of event-related potentials.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109642"},"PeriodicalIF":7.5,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720894","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}

{"title":"Decision-making systems improvement based on explainable artificial intelligence approaches for predictive maintenance","authors":"Lala Rajaoarisoa ,&nbsp;Raubertin Randrianandraina ,&nbsp;Grzegorz J. Nalepa ,&nbsp;João Gama","doi":"10.1016/j.engappai.2024.109601","DOIUrl":"10.1016/j.engappai.2024.109601","url":null,"abstract":"<div><div>To maintain the performance of the latest generation of onshore and offshore wind turbine systems, a new methodology must be proposed to enhance the maintenance policy. In this context, this paper introduces an approach to designing a decision support tool that combines predictive capabilities with anomaly explanations for effective IoT predictive maintenance tasks. Essentially, the paper proposes an approach that integrates a predictive maintenance model with an explicative decision-making system. The key challenge is to detect anomalies and provide plausible explanations, enabling human operators to determine the necessary actions swiftly. To achieve this, the proposed approach identifies a minimal set of relevant features required to generate rules that explain the root causes of issues in the physical system. It estimates that certain features, such as the active power generator, blade pitch angle, and the average water temperature of the voltage circuit protection in the generator’s sub-components, are particularly critical to monitor. Additionally, the approach simplifies the computation of an efficient predictive maintenance model. Compared to other deep learning models, the identified model provides up to 80% accuracy in anomaly detection and up to 96% for predicting the remaining useful life of the system under study. These performance metrics and indicators values are essential for enhancing the decision-making process. Moreover, the proposed decision support tool elucidates the onset of degradation and its dynamic evolution based on expert knowledge and data gathered through Internet of Things (IoT) technology and inspection reports. Thus, the developed approach should aid maintenance managers in making accurate decisions regarding inspection, replacement, and repair tasks. The methodology is demonstrated using a wind farm dataset provided by Energias De Portugal.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109601"},"PeriodicalIF":7.5,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720678","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":"Towards salient object detection via parallel dual-decoder network","authors":"Chaojun Cen ,&nbsp;Fei Li ,&nbsp;Zhenbo Li ,&nbsp;Yun Wang","doi":"10.1016/j.engappai.2024.109638","DOIUrl":"10.1016/j.engappai.2024.109638","url":null,"abstract":"<div><div>Salient object detection, an important preprocessing step in computer vision, segments the most prominent objects in an image. However, existing research in this field utilizes transformer-based methods to capture global context information, failing to effectively obtain local spatial features. To solve this issue, we propose a parallel dual-decoder network, which consists of a novel semantic decoder and a modified salient decoder. Specifically, the proposed semantic decoder is designed to learn the local spatial details, and the salient decoder utilizes the learnable queries to establish global saliency dependencies among objects. Moreover, the two decoders establish correlations between saliency and multi-scale semantic representations through cross-attention interaction, significantly enhancing the performance of salient object detection. In other words, we obtain global context information in the decoder to prevent discriminative features from being diluted during information propagation. Extensive experiments on <em>15</em> benchmark datasets demonstrate that our model significantly outperforms other comparison methods and shows promising potential for real-world applications such as challenging optical remote sensing, underwater, low-light, and other open scenarios. In addition, our method shows excellent performance in other downstream tasks such as camouflaged object detection, transparent object detection, shadow detection, and semantic segmentation.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109638"},"PeriodicalIF":7.5,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720773","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":"A velocity adaptive steering control strategy of autonomous vehicle based on double deep Q-learning network with varied agents","authors":"Xinyou Lin,&nbsp;Jiawang Huang,&nbsp;Biao Zhang,&nbsp;Binhao Zhou,&nbsp;Zhiyong Chen","doi":"10.1016/j.engappai.2024.109655","DOIUrl":"10.1016/j.engappai.2024.109655","url":null,"abstract":"<div><div>Autonomous vehicle steering control is sensitive to the vehicle driving speed and traditional model-based approaches are limited by the accuracy of the control model in various driving speed scenarios. To address these challenges, this study proposes a model-free control strategy based on deep reinforcement learning (DRL). In this strategy, the improved double deep Q-learning network (DDQN) with varied agents is employed for steering control to minimize the tracking errors across varying speeds. According to the kinematic characteristics of the vehicle, a dynamic action space is applied to enhance the tracking capability at high speeds. Furthermore, to ensure the output of the agent is more stable, a velocity adaptive reward function is designed by incorporating an action penalty factor. The performance of the proposed strategy is evaluated through simulation and experimental comparisons with other existing algorithms at a double-lane change maneuver. The results demonstrate that the DDQN-based strategy can effectively adapt to various vehicle speeds and perform the tracking task more accurately and stably. Finally, the feasibility of this strategy is verified using an actual prototype vehicle.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109655"},"PeriodicalIF":7.5,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720886","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}