Engineering Applications of Artificial Intelligence最新文献_第4页

Few-shot machine reading comprehension for bridge inspection via domain-specific and task-aware pre-tuning approach

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-22 DOI: 10.1016/j.engappai.2025.110361

Ren Li , Luyi Zhang , Qiao Xiao , Jianxi Yang , Yu Chen , Shixin Jiang , Di Wang

With the wide application of information technologies in the field of bridge engineering, many electronic bridge inspection reports have been generated. However, due to insufficient research on machine reading comprehension (MRC) in this field, a lot of bridge inspection information, e.g., structural basic data, inspected defects, and maintenance suggestions, has not been fully used. Especially, it is time-consuming and labor-intensive to pre-train a domain-specific language model from scratch or annotate large-scale question answering corpora, which also brings challenges to the MRC research in this field. To tackle the problems, this paper proposes a novel few-shot MRC approach for bridge inspection based on the idea of data augmentation. The proposed model uses a pre-trained model as backbone, along with introducing a pre-tuning stage to bridge the gaps between general-purpose pre-training and domain-specific MRC tasks. In order to reduce the workload of manual annotation, we present a novel pre-tuning data generation algorithm which is based on the domain-specific question classification and answer prediction neural models. After pre-tuning and fine-tuning, the proposed model achieves efficient bridge inspection MRC. The experimental results show that the proposed model outperforms the mainstream fine-tuning-based approaches and few-shot MRC baseline models in various settings. With 1024 fine-tuning samples, the F1 value and Exact Match (EM) value are 86.42%, 74.65%, respectively. Our research work can serve as a foundation for the construction of automatic question answering systems for intelligent bridge management and maintenance.

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引用次数: 0

On-board detection of rail corrugation using improved convolutional block attention mechanism 利用改进的卷积块注意机制对轨道波纹进行车载检测

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-22 DOI: 10.1016/j.engappai.2025.110349

Yang Wang , Hong Xiao , Chaozhi Ma , Zhihai Zhang , Xuhao Cui , Aimin Xu

Leveraging acceleration sensors affixed to the train body enables continuous surveillance of rail corrugation, delivering cost-effectiveness, operational efficiency, and portability. Establishing the correlation between vertical body acceleration and rail corrugation poses a substantial challenge. To ensure uninterrupted monitoring of rail corrugation, an initial development involved constructing a train-track integrated simulation model that accounted for the dynamics of flexible wheelsets and tracks, thereby generating a simulated dataset of vertical body acceleration. Subsequent improvements were made to the conventional Convolutional Block Attention Module (CBAM) architecture, culminating in the proposal of a deep one-dimensional convolutional residual network model named Train Body Vertical Acceleration Network (TBVA-Net), founded on an improved CBAM framework. Training was conducted using the simulated dataset, showcasing the reduced model complexity and total parameter count of the improved CBAM architecture, which notably amplified classification accuracy. The TBVA-Net, employing the refined CBAM, consistently achieved test accuracies exceeding 95%, averaging at 98.6% on the simulated dataset. Validation through field-measured data corroborated the rationale behind the proposed TBVA-Net architecture. Fine-tuning with a limited subset of labeled field data led to a transfer accuracy of 98.5%. This paper presents an innovative approach for detecting rail corrugation through vertical acceleration signals obtained from operational vehicles.

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引用次数: 0

SPARDA: Sparsity-constrained dimensional analysis via convex relaxation for parameter reduction in high-dimensional engineering systems

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-22 DOI: 10.1016/j.engappai.2025.110307

Kuang Yang, Qiang Li, Zhenghui Hou, Haifan Liao, Chaofan Yang, Haijun Wang

Effective analysis of high-dimensional systems with intricate variable interactions is crucial for accurate modeling and engineering applications. Previous methods using sparsity techniques or dimensional analysis separately often face limitations when handling complex, large-scale systems. This study introduces a sparsity-constrained dimensional analysis framework that integrates the classical Buckingham Pi theorem with sparse optimization techniques, enabling precise nondimensionalization. The framework, formulated as a convex optimization problem, addresses computational challenges associated with sparsity in high-dimensional spaces. Rigorously tested across various datasets, including the Fanning friction factor for rough pipe flow, an international standards-based dataset of physical quantities and units, and experimental data from flow boiling studies, this method successfully identified critical dimensionless groups that encapsulate core system dynamics. This approach not only offers a more compact and interpretable representation than conventional methods but also retains more characteristics of function variability. It proves particularly effective in systems governed by high-dimensional interactions, demonstrating a lower failure rate and mean relative error compared to an algorithm for comparison. The methodology is applicable to the modeling and analysis of complex engineering physical systems such as nuclear power, wind tunnel design, and marine engineering, as well as in designing scaled verification experiments.

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引用次数: 0

A novel enhanced Superlet Synchroextracting transform ensemble learning for structural health monitoring using nonlinear wave modulation

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-22 DOI: 10.1016/j.engappai.2025.110341

Naserodin Sepehry , Mohammad Ehsani , Hamdireza Amindavar , Weidong Zhu , Firooz Bakhtiari Nejad

This study investigates the application of nonlinear wave modulation (NWM) using chirp signals for structural health monitoring (SHM). The implementation of NWM with monoharmonic signals (periodic signals that consist of a single frequency component) poses significant challenges due to the complexity of selecting optimal pump and carrier frequencies, leading to time-intensive processes. In contrast, analyzing NWM with chirp signals introduces additional complexities regarding signal processing compared to monoharmonic excitations. Time-frequency analysis (TFA) has been identified as a crucial method for examining non-stationary signals; however, many existing techniques face limitations in resolution, particularly in the context of chirp signals, as dictated by the Heisenberg uncertainty principle. To address these challenges, the superlet synchroextracting transform (SLSET) is introduced as an innovative TFA approach that combines the strengths of superlet (SL) and synchroextracting transforms, resulting in improved resolution. This research utilizes NWM alongside SLSET to detect boundary loosening in sandwich beams, demonstrating the method's effectiveness in identifying structural damage while maintaining robustness against noise. Results indicate that SLSET significantly enhances the damage index compared to traditional TFA methods. The high resolution achieved allows for the detection of sidebands in vibro-acoustic modulation (VAM) tests conducted at low pump frequencies. Furthermore, three machine learning (ML) models including support vector machine (SVM), Adaptive Boosting (AdaBoost), and Random Forest (RF) were trained. The stack ensemble method combined the outputs of these models, resulting in an overall accuracy of 99.2%. This approach effectively leveraged the strengths of individual models, enhancing generalization and robustness in detecting damage across complex data scenarios. The features extracted using SLSET for VAM data of faulty structure attains a classification accuracy of 98.9%. In contrast, features derived from conventional time-frequency methods fail to identify damage, even in noise-free conditions.

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引用次数: 0

Recent advances in flotation froth image analysis via deep learning

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-22 DOI: 10.1016/j.engappai.2025.110283

Xin Chen , Dan Liu , Longzhou Yu , Ping Shao , Mingyan An , Shuming Wen

Flotation froth image analysis with computer vision systems has witnessed a transformative evolution through the integration of deep learning. Deep learning outperforms traditional feature design by effectively learning intricate feature representations, thus enhancing the assessment of froth flotation processes' operational performance. Flotation froth image analysis via deep learning facilitates real-time monitoring of dynamic flotation processes, guiding the adjustment of operational variables through predicting performance indicators, recognizing froth states and segmenting foam edges, which promotes resource efficiency and supports the sustainable development of beneficiation. Despite the vast potential of deep learning for time-series forecasting within the multistage flotation cycle, its capabilities remain underexplored. To fill this gap, based on recent research, we discuss the application of temporal and multistage information in flotation cycle. We introduce the development trends of deep learning in various processes of flotation froth image analysis, including data collection, dataset preprocessing, feature extraction, and modeling. We particularly discuss advanced techniques for extracting time-series features, and developing multistage models and innovative data collection methods, so as to emphasize the importance of using temporal information. Eventually, the review explores several trends and challenges for future research. This review is expected to leave readers with deeper thoughts about algorithm design and data collection in the flotation domain, thereby promoting further research and development in beneficiation automation.

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引用次数: 0

Post-earthquake structural damage detection with tunable semi-synthetic image generation

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-22 DOI: 10.1016/j.engappai.2025.110302

Piercarlo Dondi , Alessio Gullotti , Michele Inchingolo , Ilaria Senaldi , Chiara Casarotti , Luca Lombardi , Marco Piastra

In the aftermath of an earthquake, conducting rapid structural safety assessments is essential. A Deep Learning-based damage detector capable of automatically analyzing videos from Unmanned Aircraft Systems (UAS) surveys would be highly beneficial for this purpose. Despite significant advancements in object detection using Deep Convolutional Neural Networks (DCNNs), developing an effective post-earthquake damage detector remains challenging due to the scarcity of large, annotated image datasets. In this work, we present a system to create a large number of images where artificial damage instances are applied to real-world three-dimensional (3D) models of buildings and bridges. We defined such images as semi-synthetic. The proposed method relies on the definition, made by human experts, of meta-annotations from which a variety of damage instances can be generated in a controlled way. Semi-synthetic images are designed to augment real-world datasets, enhancing the training process of a DCNN-based damage detector. This semi-synthetic image augmentation can be iteratively refined to target the most critical cases. Experiments conducted on the ‘Image Database for Earthquake damage Annotation’ (IDEA) dataset shown that a detector trained on a combination of real and semi-synthetic images performs better than one trained on real images alone. A damage detector trained using the proposed strategy was then incorporated into a system that analyzes and tracks multiple damage instances in UAS-acquired videos, generating concise summaries of the findings. The effectiveness of the system was validated by the analysis of post-earthquake UAS videos and the production of reports that were reviewed by structural engineering experts.

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引用次数: 0

Global polynomial synchronization of proportional delay memristive neural networks with uncertain parameters and its application to image encryption

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-21 DOI: 10.1016/j.engappai.2025.110290

Yan Wan, Liqun Zhou, Jiapeng Han

This article explores the global polynomial synchronization (GPS) for a type of proportional delay memristive neural networks (PDMNNs), uncertain parameters are considered. First, the theory of differential inclusion is utilized, and then the error system is obtained. Secondly, combining the principles of sliding mode control (SMC) and adaptive control, two different controllers are designed to achieve GPS between the obtained drive–response system. Then, two GPS criteria are obtained through the application of Lyapunov stability theory and inequality analysis techniques. Ultimately, we offer three numerical exemplifications to corroborate the efficacy of the obtained results, along with a demonstration of an application pertaining to image encryption.

引用次数: 0

Task recognition integrating worker actions and machine operations: A video-based sensing approach without physical sensors

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-21 DOI: 10.1016/j.engappai.2025.110232

Shotaro Kataoka , Masashi Oba , Hirofumi Nonaka

Automating work process analysis is crucial in manufacturing to improve efficiency and productivity. However, traditional deep learning methods often fail to capture subtle temporal changes in machine operations, such as varying speeds. We propose a cost-effective approach called pseudo-sensing, which simulates sensor data by measuring machine speeds directly from video using wavelet transformation, a mathematical tool for time-frequency analysis. This approach eliminates the need for physical sensors.

We evaluated pseudo-sensing by integrating it into two task classification models. The first is a convolutional neural network-long short-term memory (CNN-LSTM) model, which extracts spatial features via a CNN and learns temporal patterns using an LSTM. The second is a three-dimensional residual network (3D ResNet, R3D), designed to process spatiotemporal data simultaneously. With pseudo-sensing, the CNN-LSTM’s micro-F1 score—an accuracy metric averaging precision and recall across all classes—improved from 0.712 to 0.736 (+2.4 points), while R3D’s score rose from 0.675 to 0.701 (+2.7 points).

To assess general applicability, we tested pseudo-sensing on another dataset featuring diverse machine motions: unidirectional movements (e.g., conveyor belts), oscillatory movements (e.g., pendulum-like motions), rotational movements (e.g., rotary presses), and intermittent movements (e.g., blinking or toggling mechanisms). The method achieved an 83% success rate in identifying machine dynamics.

By leveraging deep learning, this method integrates video-based machine operation sensing with task recognition, considering both human actions and machine states. Eliminating additional sensors while enhancing accuracy and efficiency, pseudo-sensing offers broad potential for advancing manufacturing process analysis.

自动化工作流程分析对于制造业提高效率和生产力至关重要。然而，传统的深度学习方法往往无法捕捉到机器运行中细微的时间变化，例如不同的速度。我们提出了一种经济有效的方法，称为 "伪传感"（pseudo-sensing），它通过使用小波变换（一种用于时频分析的数学工具）直接从视频中测量机器速度来模拟传感器数据。我们将伪传感集成到两个任务分类模型中，对其进行了评估。第一个模型是卷积神经网络-长短期记忆（CNN-LSTM）模型，它通过 CNN 提取空间特征，并使用 LSTM 学习时间模式。第二个是三维残差网络（3D ResNet，R3D），旨在同时处理时空数据。使用伪感知后，CNN-LSTM 的 micro-F1 分数--一种对所有类别的精确度和召回率进行平均的精确度指标--从 0.712 提高到 0.736（+2.4 分），而 R3D 的分数则从 0.675 提高到 0.701（+2.7 分）、我们在另一个数据集上测试了伪感应的普遍适用性，该数据集包含多种机器运动：单向运动（如传送带）、振荡运动（如钟摆式运动）、旋转运动（如旋转压力机）和间歇运动（如闪烁或切换机制）。该方法在识别机器动态方面取得了 83% 的成功率。通过利用深度学习，该方法将基于视频的机器运行感测与任务识别整合在一起，同时考虑了人类行为和机器状态。伪传感无需额外的传感器，同时提高了准确性和效率，为推进制造过程分析提供了广阔的潜力。

{"title":"Task recognition integrating worker actions and machine operations: A video-based sensing approach without physical sensors","authors":"Shotaro Kataoka , Masashi Oba , Hirofumi Nonaka","doi":"10.1016/j.engappai.2025.110232","DOIUrl":"10.1016/j.engappai.2025.110232","url":null,"abstract":"<div><div>Automating work process analysis is crucial in manufacturing to improve efficiency and productivity. However, traditional deep learning methods often fail to capture subtle temporal changes in machine operations, such as varying speeds. We propose a cost-effective approach called pseudo-sensing, which simulates sensor data by measuring machine speeds directly from video using wavelet transformation, a mathematical tool for time-frequency analysis. This approach eliminates the need for physical sensors.</div><div>We evaluated pseudo-sensing by integrating it into two task classification models. The first is a convolutional neural network-long short-term memory (CNN-LSTM) model, which extracts spatial features via a CNN and learns temporal patterns using an LSTM. The second is a three-dimensional residual network (3D ResNet, R3D), designed to process spatiotemporal data simultaneously. With pseudo-sensing, the CNN-LSTM’s micro-F1 score—an accuracy metric averaging precision and recall across all classes—improved from 0.712 to 0.736 (+2.4 points), while R3D’s score rose from 0.675 to 0.701 (+2.7 points).</div><div>To assess general applicability, we tested pseudo-sensing on another dataset featuring diverse machine motions: unidirectional movements (e.g., conveyor belts), oscillatory movements (e.g., pendulum-like motions), rotational movements (e.g., rotary presses), and intermittent movements (e.g., blinking or toggling mechanisms). The method achieved an 83% success rate in identifying machine dynamics.</div><div>By leveraging deep learning, this method integrates video-based machine operation sensing with task recognition, considering both human actions and machine states. Eliminating additional sensors while enhancing accuracy and efficiency, pseudo-sensing offers broad potential for advancing manufacturing process analysis.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"147 ","pages":"Article 110232"},"PeriodicalIF":7.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465112","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

State-of-the-art review and benchmarking of barcode localization methods

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-21 DOI: 10.1016/j.engappai.2025.110259

Enrico Vezzali , Federico Bolelli , Stefano Santi , Costantino Grana

Barcodes, despite their long history, remain an essential technology in supply chain management. In addition, barcodes have found extensive use in industrial engineering, particularly in warehouse automation, component tracking, and robot guidance. To detect a barcode in an image, multiple algorithms have been proposed in the literature, with a significant increase of interest in the topic since the rise of deep learning. However, research in the field suffers from many limitations, including the scarcity of public datasets and code implementations which hinders the reproducibility and reliability of published results. For this reason, we developed “BarBeR” (Barcode Benchmark Repository), a benchmark designed for testing and comparing barcode detection algorithms. This benchmark includes the code implementation of various detection algorithms for barcodes, along with a suite of useful metrics. Among the supported localization methods, there are multiple deep-learning detection models, that will be used to assess the recent contributions of Artificial Intelligence to this field. In addition, we provide a large, annotated dataset of 8 748 barcode images, combining multiple public barcode datasets with standardized annotation formats for both detection and segmentation tasks. Finally, we provide a thorough summary of the history and literature on barcode localization and share the results obtained from running the benchmark on our dataset, offering valuable insights into the performance of different algorithms when applied to real-world problems.

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引用次数: 0

Lattice-based sensor data acquisition strategy to solve sensor position drift in human gait phase recognition system with a single inertia measurement unit

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-21 DOI: 10.1016/j.engappai.2025.110286

Dianbiao Dong , Nannan Zhu , Jiehong Wang , Yuzhu Li

The inertia measurement unit (IMU) sensor units have garnered considerable utilization in gait phase recognition systems owing to their inherent data stability and compatibility with low-usage conditions. The extant scholarship concerning gait phase recognition predicated upon IMU sensors manifests a concentrated endeavor to augment the accuracy of recognition through the employment of diverse recognition algorithms and the fusion of multiple sensors. However, the impact of the drift of the IMU sensor position on the accuracy of the gait phase recognition algorithm during training and use is ignored, which is especially important for a single IMU gait recognition system. Therefore, taking convolutional neural network, deep neural network, support vector machine, and random forest algorithms as examples, this paper studies the impact of IMU sensor position drift on the accuracy of gait phase recognition algorithms. To address the quandary of position drift, this study proposes an innovative lattice-based data acquisition strategy for a single IMU sensor by selecting 9 uniformly distributed points on the posterior region of the calf. A treadmill walking test wearing an embedded IMU data acquisition system was organized to verify the practical performance of the lattice-based data acquisition strategy. By comparing the performance of different lattice combinations, a 5-point sensor acquisition strategy is proposed, which can effectively increase the accuracy of gait phase recognition with IMU sensor position drift by more than 10.29%.

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引用次数: 0