Engineering Applications of Artificial Intelligence最新文献

英文中文

Neighborhood search with heuristic-based feature selection for click-through rate prediction

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

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-17 DOI: 10.1016/j.engappai.2025.110261

Dogukan Aksu , Ismail Hakki Toroslu , Hasan Davulcu

Click-through-rate (CTR) prediction is crucial in online advertising and recommender systems. Maximizing CTR has been a major focus, leading to the development of numerous models designed to capture implicit and explicit feature interactions. However, extracting both low-order and high-order interactions remains challenging, as irrelevant features can increase computational costs and reduce prediction accuracy. Feature performance may also vary across predictive models and fluctuate due to traffic changes, making efficient feature selection essential in live environments where both training and inference times are critical. Traditional filter-based feature selection methods often fail to dynamically identify the most impactful features. This paper introduces a greedy heuristic, called Neighborhood Search with Heuristic-based Feature Selection (NeSHFS), to enhance CTR prediction by iteratively refining the feature set. NeSHFS employs a grid-search-like strategy to identify and retain the most relevant features, effectively reducing dimensionality and computational costs. Comprehensive experiments on several public datasets validate this approach, demonstrating improved prediction performance and reduced training times.

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引用次数: 0

Context-aware inverse reinforcement learning for modeling individuals’ daily activity schedules

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

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-17 DOI: 10.1016/j.engappai.2025.110279

Dongjie Liu , Dawei Li , Kun Gao , Yuchen Song , Zijie Zhou

Understanding individual and crowd dynamics in urban environments is critical for numerous applications, such as urban planning, traffic forecasting, and location-based services. Therefore, accurately modeling individuals' daily activity schedules is essential. Traditional methods, like utility-based and rule-based approaches, rely on expert knowledge and presumed model structures. While machine learning methods offer flexibility, they often ignore explicit behavioral mechanisms, particularly comprehensive discussion and integration of context related to individuals' daily travel. To address these, we propose a framework that integrates travel context with deep Inverse Reinforcement Learning (IRL), learning temporal patterns from sociodemographics, start time and duration of the current activity, travel modes, and land use. Specifically, individuals' activity schedules are initially formulated as a Markov Decision Process to simulate travelers’ sequential decision-making processes, laying the groundwork for the IRL framework; Then, a context-aware IRL method is proposed to model individuals' travel decision-making from observed temporal trajectories. Finally, we validate the proposed model by demonstrating its superior performance over discrete choice model and several well-known imitation learning benchmarks in tasks such as policy performance comparison, reward recovery, model generalizability, and computational efficiency using travel behavior datasets. This approach provides meaningful behavioral insights and paves the way for Artificial Intelligence-driven activity schedulers modeling.

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引用次数: 0

A human-robot interaction system for automated chemical experiments based on vision and natural language processing semantics

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

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-17 DOI: 10.1016/j.engappai.2025.110226

Zhuang Yang , Yu Du , Dong Liu , Kesong Zhao , Ming Cong

Using collaborative robots to replace researchers in performing repetitive and hazardous chemical experiments can effectively enhance experimental efficiency. However, this technology still faces several challenges, including understanding researchers' natural language instructions, autonomously generating action sequences, and more. Therefore, we developed a general control framework for robots in automated chemical experiments based on visual and natural language semantic information. Firstly, starting with the recognition of keywords within Chinese language instructions, we established a domain dictionary for chemical experiment operations and proposed an instruction understanding model based on the bidirectional long-short-term memory and conditional random field(BiLSTM-CRF), enhancing the robot's cognitive ability towards user instructions. Then, a rule matching method for chemical experimental information and a multimodal information feature matching mechanism were established for command content verification and the automatic generation of multiple types of structured language. At the same time, a robot feedback mechanism was added, enabling human-computer interaction and establishing closed-loop control of the system. Finally, propose a robot action sequence generation mechanism based on hierarchical finite state machines(HFSM), transforming structured language into operational strategies for chemical experiments required by the robot. Experimental results show that on the instruction task comprehension dataset created in this paper, the proposed method improves the F1 score by up to 4.44% in the instruction keyword extraction task compared to other models. In addition, compared to traditional manual teaching control, this method significantly reduces time costs. This verifies that the method effectively enhances the robot's ability to comprehend Chinese instructions and generates reliable executable action sequences.

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引用次数: 0

Polarization-based Camouflaged Object Detection with high-resolution adaptive fusion Network

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

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-17 DOI: 10.1016/j.engappai.2025.110245

Xin Wang , Junfeng Xu , Jiajia Ding

In comparison to traditional object detection or segmentation tasks, Camouflaged Object Detection (COD) poses greater challenges, as humans are often perplexed or deceived by the inherent similarities between foreground objects and their background surroundings. Polarization information serves as a valuable asset for discerning the attributes of objects with varied characteristics and surface texture. Taking inspiration from the polarization vision systems observed in animals, this study presents the High-Resolution Intensity & Polarization Fusion (HIPF) Net, a high-efficiency cross-modal fusion network that leverages trichromatic intensity and linear orthogonal polarization cues to produce a scene representation that is rich in texture and edge details. Specifically, the Early Adaptive Stokes Fusion (EASF) module maximizes the utilization of information from linear orthogonal polarization images. Subsequently, the Mix-Attention Feature Interaction Module (MAI) is introduced to facilitate complementary interaction among low-level features. Additionally, the Attentional Receptive Field Block (ARFB) enables the model to uncover concealed cues effectively, capturing objects of various sizes. Finally, the Weighted Cross-Level Decoder(WCFD) is designed to dynamically fuse and assign weights to cross-level contextual information for robust detection. Training and extensive validation of our model are performed on the polarization-based dataset as well as non-polarization-based datasets, with experimental results demonstrating that HIPFNet consistently outperforms state-of-the-art methods. Source codes are available at https://github.com/CVhfut/HIPFNet.

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引用次数: 0

Multi-scale patch transformer with adaptive decomposition for carbon emissions forecasting

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

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-17 DOI: 10.1016/j.engappai.2025.110153

Xiang Li , Lei Chu , Yujun Li , Fengqian Ding , Zhenzhen Quan , Fangx Qu , Zhanjun Xing

Rapid urbanization and industrialization have led to a significant increase in carbon emissions, posing a challenge for sustainable environmental management. However, current research predominantly focuses on traditional forecasting models that often overlook the complexity and dynamic nature of environmental data. To address this, a novel multi-scale patch transformer with adaptive decomposition (MPDformer) has been developed specifically for forecasting carbon emissions. This model introduces an adaptive decomposition method that dynamically assesses the noise level, trend, and stationarity of data to select the most appropriate decomposition technique. In addition, the use of a Transformer with multi-scale patches can optimize the use of information at different granularities in the decomposed sub-series for time series prediction of carbon emissions. Experimental validations have shown that this method possesses an exceptional capability to discern complex temporal dependencies within fluctuating environmental data, consistently outperforming comparative models across a range of carbon emissions datasets and various forecasting horizons. These results indicate the potential for more accurate and reliable carbon emissions forecasts, which can contribute to better-informed decisions in sustainable energy planning and environmental management.

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引用次数: 0

Artificial intelligence based-improving reservoir management: An Attention-Guided Fusion Model for predicting injector–producer connectivity

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

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-17 DOI: 10.1016/j.engappai.2025.110205

Ahmed Saihood , Tariq Saihood , Sabah Abdulazeez Jebur , Christine Ehlig-Economides , Laith Alzubaidi , Yuantong Gu

The existing oil reservoir demonstrated suboptimal inter-well connectivity, leading to irregular depletion and reduced overall production efficiency. This article demonstrates the Attention-Guided Fusion Model for Injector–Producer Connectivity Estimation (AGFM). The model has an attention mechanism in the first path, pulling discernment from the relationships between injectors and producers through the training phase, extracting the attention weight. This attention weight is then devoted to the second path, utilising a Long-Short-Term Memory (LSTM)-based architecture. The first path is only to the training stage. In contrast, the second path is used during training and testing, improving the ability of the model to find a more significant representation of the data. This makes the model robust enough to predict reservoir performance and interconnectivity, giving valuable insights to optimise field operations. The AGFM undergoes an evaluation with two different injection liquids (carbon dioxide (

C O_{2}

) and water) in three scenarios: all water and all CO2 alternating between water and CO2 as a flooding liquid. The evaluation emphasises the efficacy of the model in all scenarios, making it a practical tool for estimating reservoir connectivity and enhancing oil recovery strategies. The water alternating gas (WAG) process performed high accuracy rates, with 82.1% for oil production, 86.8% for water production, and 86.9% for gas production. Our proposed method consistently demonstrates superior performance through comprehensive experimentation and rigorous analysis compared to existing approaches. The results reveal spatial interwell connectivity, confirming the efficacy and potential of our method as a more effective solution for reservoir recovery.

{"title":"Artificial intelligence based-improving reservoir management: An Attention-Guided Fusion Model for predicting injector–producer connectivity","authors":"Ahmed Saihood , Tariq Saihood , Sabah Abdulazeez Jebur , Christine Ehlig-Economides , Laith Alzubaidi , Yuantong Gu","doi":"10.1016/j.engappai.2025.110205","DOIUrl":"10.1016/j.engappai.2025.110205","url":null,"abstract":"<div><div>The existing oil reservoir demonstrated suboptimal inter-well connectivity, leading to irregular depletion and reduced overall production efficiency. This article demonstrates the Attention-Guided Fusion Model for Injector–Producer Connectivity Estimation (AGFM). The model has an attention mechanism in the first path, pulling discernment from the relationships between injectors and producers through the training phase, extracting the attention weight. This attention weight is then devoted to the second path, utilising a Long-Short-Term Memory (LSTM)-based architecture. The first path is only to the training stage. In contrast, the second path is used during training and testing, improving the ability of the model to find a more significant representation of the data. This makes the model robust enough to predict reservoir performance and interconnectivity, giving valuable insights to optimise field operations. The AGFM undergoes an evaluation with two different injection liquids (carbon dioxide (<span><math><mrow><mi>C</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></math></span>) and water) in three scenarios: all water and all CO2 alternating between water and CO2 as a flooding liquid. The evaluation emphasises the efficacy of the model in all scenarios, making it a practical tool for estimating reservoir connectivity and enhancing oil recovery strategies. The water alternating gas (WAG) process performed high accuracy rates, with 82.1% for oil production, 86.8% for water production, and 86.9% for gas production. Our proposed method consistently demonstrates superior performance through comprehensive experimentation and rigorous analysis compared to existing approaches. The results reveal spatial interwell connectivity, confirming the efficacy and potential of our method as a more effective solution for reservoir recovery.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"146 ","pages":"Article 110205"},"PeriodicalIF":7.5,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429485","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}

引用次数: 0

Periodic event-triggered adaptive neural output feedback tracking control of unmanned surface vehicles under replay attacks

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

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-17 DOI: 10.1016/j.engappai.2025.110237

Guibing Zhu , Zhengyue Xu , Yun Gao , Yalei Yu , Lei Li

This paper proposes a periodic event-triggered adaptive neural output feedback tracking control scheme for unmanned surface vehicles under replay attacks, where actuator saturation constraint and internal/external uncertainties are involved. To reduce attack signals entering the control system, an independent adaptive neural state observer is developed to recover the unavailable real velocities and mismatched compound uncertainties. Under the backstepping design framework, the adaptive neural-based single-parameter-learning method is involved to reconstruct the internal/external uncertainties, and an anti-replay-attacks output feedback tracking control law is devised. Furthermore, in the controller-actuator channel, a smooth saturation model is introduced and a periodic event-triggering mechanism is established to relieve the physical constraint of actuators. Theoretical analysis and simulation results verify the effectiveness of the developed scheme.

引用次数: 0

Periodic decomposition and feature enhancement fusion for traffic forecasting

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

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-17 DOI: 10.1016/j.engappai.2025.110229

Xiaofei Kong , Hua Wang , Mingli Zhang , Fan Zhang

With the rapid acceleration of urbanization, traffic prediction plays a crucial role in smart city development. This paper proposes an architecture called Periodic Decomposition and Feature Enhancement Fusion (PDGM) aimed at addressing the periodicity issue overlooked in existing traffic prediction methods. PDGM utilizes downsampling techniques to decompose the original traffic data into periodic components and enhances missing data through feature enhancement fusion, thereby improving the accuracy of traffic data prediction. Experimental results of this study demonstrate that PDGM outperforms state-of-the-art baseline models on three benchmark datasets, offering new possibilities for traffic data analysis and prediction tasks.

引用次数: 0

An improved framework for breast ultrasound image segmentation with multiple branches depth perception and layer compression residual module

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

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-17 DOI: 10.1016/j.engappai.2025.110265

Ke Cui, Qichuan Tian, Haoji Wang, Chuan Ma

Breast cancer is becoming a leading cause of death among women worldwide. Early detection is essential for improving survival rates and facilitating targeted medical treatments. Automated segmentation of breast tumors from ultrasound images is vital for this early diagnosis. To tackle challenges such as low contrast, unclear lesion boundaries, and class imbalance in breast ultrasound images, a multiple branches depth perception network was introduced, using a symmetric encoder and decoder architecture. In the initial feature extraction stage, the network’s encoder employs the multiple branches depth residual block to integrate feature information from multiple branches while employing dilated convolution to capture intricate contextual details, enhancing the characterization of complex features. Subsequently, in the feature recovery stage, the network utilizes dual path depth perception block to mitigate information loss in deep networks by leveraging dual path residual connections, extracting rich textural and structural features from breast ultrasound images. Furthermore, the layer compression residual module and attention refinement module were incorporated within the skip connections to strengthen the contextual relationships between the encoder and decoder, leading to improved segmentation of breast lesions. Extensive qualitative and quantitative evaluations on two challenging public datasets were conducted to assess the effectiveness and generalizability of the proposed approach. The experimental results demonstrate the reliability of the proposed method in clinical treatment, achieving segmentation mean intersection over union scores of 91.11% and 92.28% on these respective datasets.

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引用次数: 0

A hybrid probabilistic battery health management approach for robust inspection drone operations

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

Engineering Applications of Artificial Intelligence

Pub Date : 2025-02-16 DOI: 10.1016/j.engappai.2025.110246

Jokin Alcibar , Jose I. Aizpurua , Ekhi Zugasti , Oier Peñagarikano

Monitoring the health of remote critical infrastructure poses significant challenges due to limited accessibility and harsh operational environments. Inspection drones are ubiquitous assets that enhance the reliability of critical infrastructures through improved accessibility. However, due to the harsh operation environment, it is crucial to monitor their health to ensure successful inspection operations. The battery is a key component that determines the reliability of the inspection drones and, with an appropriate health management approach, contributes to reliable and robust inspections. This paper introduces a novel hybrid probabilistic approach for predicting the end-of-discharge (EOD) voltage of lithium polymer (Li-Po) batteries in inspection drones. The proposed approach integrates Monte Carlo (MC) dropout based Convolutional Neural Networks (CNN) with electrochemistry-based battery discharge model. This integration employs an error-correction configuration that combines electrochemistry-based EOD prediction with probabilistic error correction using CNN with MC dropout. The approach is designed to infer aleatoric and epistemic uncertainty, facilitating robust battery discharge predictions through uncertainty-aware predictions. The proposed approach is empirically evaluated using a dataset comprising EOD voltage measurements under varying load conditions. The dataset, obtained from real inspection drones during offshore wind turbine inspections, underscores the practical applicability of the proposed approach. Comparative analysis with various probabilistic methods, including Quantile Linear Regression, Quantile Regression Forest, and Quantile Gradient Boosting, demonstrates a 14.8% improvement in probabilistic accuracy compared to the best-performing method. Additionally, the estimation of different uncertainties enhances the diagnosis of battery health states, contributing to more reliable inspection operations and highlighting the practical value of the work.

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