Engineering Applications of Artificial Intelligence最新文献

{"title":"An interpretable precursor-driven hierarchical model for predictive aircraft safety","authors":"","doi":"10.1016/j.engappai.2024.109322","DOIUrl":"10.1016/j.engappai.2024.109322","url":null,"abstract":"<div><p>Predicting high-risk anomalous events in flight is crucial for ensuring in-time aviation safety and reducing potential incidents. This paper proposes a precursor-driven hierarchical predictive model for early warnings and actionable insights before incidents occur. The model uses an unsupervised learning network to construct latent event sequences from discrete variables, guiding a weakly supervised learning network for feature extraction from continuous variables. This hierarchical fusion captures the influence of discrete control variables on continuous flight states, enhancing its prediction performance of anomalous events. Guided by event sequences, the model can detect different anomalous patterns through identified precursors, thus providing a comprehensive understanding of events with interpretation. Quantitative evaluations further support the model’s rationale in interpretation, encompassing self-explanation and post-hoc analysis. A real case study on unstable approach events, using data from enhanced flight recorders, validates the model’s effectiveness in prediction and interpretation from precursors. The study explains imminent unstable approaches and offers an in-depth analysis of error cases, providing insights for model refinement and risk analysis, contributing to ongoing improvement in aviation safety.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142240194","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":"Correlation mining of multimodal features based on higher-order partial least squares for emotion recognition in conversations","authors":"","doi":"10.1016/j.engappai.2024.109350","DOIUrl":"10.1016/j.engappai.2024.109350","url":null,"abstract":"<div><p>In fields requiring an understanding of emotions, such as digital human interaction and public opinion analysis, achieving a dependable and interpretable model for mining correlations among multimodal features remains a primary objective. However, current deep learning methods often lack transparency and suffer from low interpretability. To address these challenges, we propose a novel Correlation Mining method based on Higher-Order Partial Least Squares (HOPLS) for multimodal Emotion Recognition in conversations (CMHER) in this paper. CMHER innovatively combines HOPLS with transformers and Gated Recurrent Units (GRUs) to compute correlation matrices within unimodal data streams and between cross-modal sources. HOPLS projects source data into a latent space to predict target data via correlation matrix computations, eliminating the need for Graphical Processing Unit (GPU) acceleration and making it suitable for experimental and edge systems. The integration of HOPLS with deep neural networks involves preprocessing multimodal features into suitable dimensions and latent representations, followed by HOPLS computing correlation matrices for cross-modal latent vectors and final labels through optimal joint subspace approximation, which aims at the improvements of both interpretability and reliability. Additionally, a generalization error fitting module further refines the predicted correlation matrices to improve predictive capability and overall model performance. Experiments on two public datasets validate the superiority of our proposed method.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142240197","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":"Solving the imbalanced dataset problem in surveillance image blur classification","authors":"","doi":"10.1016/j.engappai.2024.109345","DOIUrl":"10.1016/j.engappai.2024.109345","url":null,"abstract":"<div><p>Surveillance videos taken in unconstrained environments can be tampered with due to different environmental factors and malicious human activities. They often blur the video content and introduce difficulty in identifying the events in the scene. The problem is particularly acute for smart surveillance systems that need to make real-time decisions based on the video. Automatic detection and classification of the blur anomalies in the video are crucial to these systems. Traditional learning-based classification methods often face imbalance problems in the sample numbers and distributions among the data classes in the dataset that severely affect their training and hence the classification performance. In this paper, a new learning-based approach for surveillance image blur classification is proposed. The imbalanced dataset problem is tackled both at the data and algorithm levels. At the data level, two synthesizers are developed to generate the required negative surveillance images to balance the sample numbers for all classes. At the algorithm level, an attention-based structure making use of the special feature of the minority class is proposed to improve the classification accuracy. Our experiment results show that the proposed approach significantly outperforms state-of-the-art methods for blur classification while keeping the model size small for edge applications.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239988","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":"Predictive resilience assessment featuring diffusion reconstruction for road networks under rainfall disturbances","authors":"","doi":"10.1016/j.engappai.2024.109317","DOIUrl":"10.1016/j.engappai.2024.109317","url":null,"abstract":"<div><p>The ability of road networks to withstand external disturbances is a crucial measure of transportation system performance, where resilience distinctly emerges as an effective perspective for its unique insights into the system’s resistance and recovery capabilities. In the face of unforeseen resilience disturbance events, predictive and accurate assessment of road network resilience is essential for better traffic regulation and emergency response management. However, existing resilience assessment methods of road networks are insufficient: they lack reliable real-time big-data analysis, do not possess predictive capabilities for guiding decision-making, and have a narrow view with single-dimensional resilience indicators. To address these issues, focusing on rainfall disturbance scenarios, this work introduces a novel resilience assessment method, which is predictive and real-time, consisting of two components: a deep learning traffic indicator prediction model and a comprehensive resilience assessment model. Firstly, we propose a two-stage traffic indicator prediction model, namely the Conditional Diffusion-Reconstruction-based Graph Neural Network (CDRGNN), which particularly enhances disturbance-scenario prediction accuracy, thereby providing reliable foresight in aid of the following assessments. Subsequently, we develop a resilience assessment model featuring structural-functional comprehensive resilience indicators established through shortest-path aggregation, and the overall resilience assessment is performed through comparative analysis using indicators obtained in real-time with historical non-disruptive resilience benchmarks. In a case study focusing on heavy rainfall disturbances on a road network in California, the United States, abundant experiments and visualizations are conducted to demonstrate the rationality of our proposed comprehensive resilience indicators as well as the precision and reliability of these predictive resilience assessment outcomes.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0952197624014751/pdfft?md5=09841aa605194b94efa858a8232e21d1&pid=1-s2.0-S0952197624014751-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142240195","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":"A novel solution for routing a swarm of drones operated on a mobile host","authors":"","doi":"10.1016/j.engappai.2024.109337","DOIUrl":"10.1016/j.engappai.2024.109337","url":null,"abstract":"<div><p>The increasing use of drones across various sectors demands optimized deployment strategies under diverse constraints. This paper tackles the Multiple Capacitated Mobile Depot Vehicles Routing Problem (mCMoD-VRP), a challenging variant of the Vehicle Routing Problem (VRP) where multiple drones with limited flight range operate from a mobile depot. The goal is to maximize target coverage while considering flight endurance, depot mobility, and drone multiplicity. We introduce a novel evolutionary algorithm, Evolutionary Optimization for Synchronized Routing Problem (EOSRP), which constructs synchronized routes for the drone swarm, accounting for all constraints. EOSRP distinguishes itself with specialized genetic operators, specifically designed to efficiently handle the constraints of mCMoD-VRP, enhancing both exploration and exploitation of the search space. EOSRP also facilitates collaborative planning among drones, enabling them to share targets and optimize routes collectively, resulting in more efficient use of flight range capacity. Comprehensive simulations on benchmark problems demonstrate that EOSRP consistently outperforms a serialized version of our previous single-drone algorithm, Genetic Algorithm for Capacitated Mobile Depot (GA-CMoD), achieving an average of 8.7% higher target coverage and 7.28% more efficient use of flight range capacity. EOSRP’s ability to generate synchronized solutions through collaborative planning leads to significantly improved mission efficiency.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142240196","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":"When guided diffusion model meets zero-shot image super-resolution","authors":"","doi":"10.1016/j.engappai.2024.109336","DOIUrl":"10.1016/j.engappai.2024.109336","url":null,"abstract":"<div><p>Existing deep learning-based single-image super-resolution (SR) methods typically rely on vast quantities of paired data. As an essential solution, zero-shot SR methods require only a single image to handle image-specific degradation. However, these methods still struggle to recover fine-grained details due to the lack of supervised information. In this work, we propose a novel guided <strong>Diff</strong>usion model for <strong>Zero</strong>-shot image SR (<strong>ZeroDiff</strong>) to explicitly direct image quality enhancement. Specifically, we elaborate two key guidance strategies: (1) high-frequency guidance and (2) content-consistent guidance. The former concentrates on boosting fine-grained textures by embedding high-frequency information into the cross-attention mechanism of the noise estimator. The latter avoids the sampling deviating from the original image in terms of structure and low-frequency content. Specifically, the noisy images at each diffusion step are injected into the corresponding sampling step, encouraging the sampled image to be consistent with that of the corresponding diffusion step. Moreover, we design a progressive zoom-in paradigm by gradually enlarging the image size and enriching the image details to boost the sampling efficiency of diffusion models, while enabling high-quality image reconstruction. Extensive experiments reveal that our method achieves comparable results with other state-of-the-art methods in quantitative and qualitative evaluations on both face and natural images from synthetic and real-world datasets.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239660","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":"RailEINet:A novel scene segmentation network for automatic train operation based on feature alignment","authors":"","doi":"10.1016/j.engappai.2024.109295","DOIUrl":"10.1016/j.engappai.2024.109295","url":null,"abstract":"<div><p>The primary prerequisite for realizing automatic train operation is endowing trains with the capability of independent environmental perception. The railway scene is notably intricate, encompassing elements such as tracks, poles and more. Scene segmentation aims to make a pixel-wise classification for a full perspective analysis of railway scene, which is geared to build a powerful automatic train perception system. The existing methods primarily emphasize the creation of multi-scale feature interaction mechanisms, where features at different levels are aggregated after the sampling operation. This operation neglects the differences in data among various features, which leads to the production of semantically ambiguous and unaligned features. This can significantly impact the segmentation results. To tackle this problem, we design two neural modules. Concretely, the Explicit Boundary Alignment (EBA) module is designed to utilize edge supervision to constrain direct alignment within the boundary regions among objects. This enables the refinement of edge details. Then, the Implicit Pyramid Alignment (IPA) module is designed to dynamically learn an offset map. This map, when combined with bilinear sampling operations, effectively mitigates the misalignment issues between multi-scale features. The two modules described above constitute a novel scene segmentation network tailored for railway scene perception, known as the Rail Scene-oriented Explicit-Implicit Feature Alignment Network (RailEINet). Extensive experiments are conducted to demonstrate the effectiveness of RailEINet. In particular, we achieve 66.22% mIoU on the wildly-used RailSem19 dataset and experimental results show that RailEINet can achieve excellent segmentation of various targets in railway scenarios.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239657","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":"An Artificial Neural Network approach to assess road roughness using smartphone-based crowdsourcing data","authors":"","doi":"10.1016/j.engappai.2024.109308","DOIUrl":"10.1016/j.engappai.2024.109308","url":null,"abstract":"<div><p>Monitoring road surface conditions is a crucial task for road authorities to develop effective infrastructure maintenance programs. Despite smartphones have been introduced as cost-effective and real-time solution for this purpose, several challenges must be addressed before their real-world application. This study investigates the utilization of smartphone-based crowdsourcing data and Artificial Neural Networks (ANN) to enhance the precision of road surface condition estimation. Initially, data are collected from four different smartphone models mounted in various vehicles, including vertical acceleration, geographic location, and speed. The root mean square of the vertical acceleration data, along with vehicle speed, is then employed as input features for the ANN, while the true International Roughness Index (IRI) values serve as the corresponding output features. Comparative analysis between ANN and regression models based on statistical metrics such as Mean Squared Error (MSE) and Pearson correlation revealed that ANN outperforms regression models. The obtained MSE and Pearson correlation values for ANN (0.56 and 0.91) surpass those of regression models (0.72 and 0.88). Moreover, results indicated that utilizing crowdsourcing smartphone data yielded superior outcomes compared to using a single smartphone for this purpose.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239987","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":"An adaptive gravitational search algorithm for optimizing mechanical engineering design and machining problems","authors":"","doi":"10.1016/j.engappai.2024.109298","DOIUrl":"10.1016/j.engappai.2024.109298","url":null,"abstract":"<div><p>The gravitational search algorithm (GSA) is widely used for solving optimization problems because it performs in a superior manner as compared to various competing evolutionary and swarm-based metaheuristics. However, GSA frequently gets trapped in local optima due to a lack of solution diversity. Although chaotic gravitational search algorithm (CGSA) can resolve this issue to some extent but its degraded exploitation rate and convergence speed may not result in desired outcome. To this end, diversity-based chaotic GSA (DCGSA) has exhibited its capability to resolve the issues encountered with GSA and CGSA to a certain extent for unconstrained problems. DCGSA achieves this characteristic through the use of an adaptive gravitational constant, which varies according to the diversity values of the population. Since most real-world problems are subjected to some constraints, it is prudent to improve the performance of GSA in solving constrained optimization problems. The present study integrates the enhanced search capability of DCGSA with a generalized constraint handling mechanism to improve the performance of GSA in solving constrained problems. It is observed that DCGSA significantly outperforms GSA on both CEC (Congress on evolutionary computation) 2006, 2010 and 2017 functions and competes strongly with CGSA. Diversity analysis shows that the capability to balance exploration and exploitation rates is enhanced using CGSA and DCGSA. Furthermore, DCGSA algorithms outperform GSA and CGSA on real-world machining and CEC 2020 mechanical design problems. Comparison with state-of-the-art algorithms is made to analyze the performance of the algorithms from a larger perspective.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239656","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":"Online model-based anomaly detection in multivariate time series: Taxonomy, survey, research challenges and future directions","authors":"","doi":"10.1016/j.engappai.2024.109323","DOIUrl":"10.1016/j.engappai.2024.109323","url":null,"abstract":"<div><p>Time-series anomaly detection plays an important role in engineering processes, like development, manufacturing and other operations involving dynamic systems. These processes can greatly benefit from advances in the field, as state-of-the-art approaches may aid in cases involving, for example, highly dimensional data. To provide the reader with understanding of the terminology, this survey introduces a novel taxonomy where a distinction between online and offline, and training and inference is made. Additionally, it presents the most popular data sets and evaluation metrics used in the literature, as well as a detailed analysis. Furthermore, this survey provides an extensive overview of the state-of-the-art model-based online semi- and unsupervised anomaly detection approaches for multivariate time-series data, categorising them into different model families and other properties. The biggest research challenge revolves around benchmarking, as currently there is no reliable way to compare different approaches against one another. This problem is two-fold: on the one hand, public data sets suffers from at least one fundamental flaw, while on the other hand, there is a lack of intuitive and representative evaluation metrics in the field. Moreover, the way most publications choose a detection threshold disregards real-world conditions, which hinders the application in the real world. To allow for tangible advances in the field, these issues must be addressed in future work.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0952197624014817/pdfft?md5=78aaf78728e6be137ef00bb75f95fe3b&pid=1-s2.0-S0952197624014817-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239658","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}