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Artificial Intelligence for Engineering Applications, 2nd Edition
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Artificial Intelligence for Engineering Applications, 2nd Edition

A special issue of Eng (ISSN 2673-4117).

Deadline for manuscript submissions: 31 March 2026 | Viewed by 18173

Special Issue Editors

Prof. Dr. Juvenal Rodriguez-Resendiz

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Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Queretaro 76010, Mexico
Interests: robotics; biomedical applications; instrumentation and measurement; signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marco Antonio Aceves-Fernandez

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Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas, Las Campanas, Queretaro 76010, Mexico
Interests: machine learning; neural networks and artificial intelligence; air pollution; particulate matter
Special Issues, Collections and Topics in MDPI journals
Dr. Akos Odry

E-Mail Website
Guest Editor
Department of Mechatronics and Automation, Faculty of Engineering, University of Szeged, 6275 Szeged, Hungary
Interests: inertial sensors; sensor fusion; localization; intelligent control; robotics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. José Manuel Álvarez-Alvarado

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Queretaro, Queretaro 76010, Mexico
Interests: artificial intelligence; embedded systems
Special Issues, Collections and Topics in MDPI journals
Dr. Marcos Aviles

E-Mail Website
Guest Editor Assistant
Facultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Queretaro 76010, Mexico
Interests: EMG; EEG; machine learning; metaheuristics; signal and image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to the Special Issue focused on the advances in artificial intelligence applied to comprehensive engineering solutions. These techniques range from machine learning models that apply accurate prediction and decision-making to image processing, which improves visual analysis and pattern detection.

In an increasingly technologically advanced world, integrating artificial intelligence into engineering has offered prominent results. This has motivated efforts to create comprehensive solutions by optimizing processes and improving the design and functionality of electronics to enhance different systems. From the health applications of engineering, such as biomedical technology, to the efficiency of energy systems, AI has been fundamental in revolutionizing these areas. This is why our SI aims to compile AI advances applied to innovative, technological, and scientific solutions in the engineering field.

The main areas of engineering that our Special Issue focuses on are as follows:

  • Automation;
  • Electronics;
  • Electric power;
  • Sustainability;
  • Biomedical;
  • Mechatronic;
  • Computer systems;
  • Multidisciplinary engineering.

Prof. Dr. Juvenal Rodriguez-Resendiz
Prof. Dr. Marco Antonio Aceves-Fernandez
Dr. Akos Odry
Prof. Dr. José Manuel Álvarez-Alvarado
Guest Editors

Dr. Marcos Aviles
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Eng is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • image processing
  • AI in embedded systems
  • optimization algorithms
  • autonomous robotics
  • system control
  • computational optimization
  • neural networks for engineering
  • IoT

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (15 papers)

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28 pages, 4833 KB  
Article
Hybrid Smart Energy Community and Machine Learning Approaches for the AI Era in Energy Transition
by Helena M. Ramos, Ignac Gazur, Oscar E. Coronado-Hernández and Modesto Pérez-Sánchez
Eng 2026, 7(4), 146; https://doi.org/10.3390/eng7040146 - 25 Mar 2026
Abstract
The Hybrid Smart Energy Community (HySEC) model is an integrated framework for optimizing hybrid renewable energy systems, unifying BIM, IoT, and data-driven modeling, as an innovative approach for the energy transition. A Revit—Twinmotion BIM model, enriched with topographic, CAD, and real-image data, enhances [...] Read more.
The Hybrid Smart Energy Community (HySEC) model is an integrated framework for optimizing hybrid renewable energy systems, unifying BIM, IoT, and data-driven modeling, as an innovative approach for the energy transition. A Revit—Twinmotion BIM model, enriched with topographic, CAD, and real-image data, enhances spatial accuracy and stakeholder communication, while a digital–physical architecture linking sensors, gateways, edge devices, and cloud platforms enables decentralized peer-to-peer communication and real-time monitoring. The framework is applied to a smart energy community composed of a hydropower–wind–solar PV system serving six buildings (48.8 MWh/year), supported by high-resolution hourly Open-Meteo data. A NARX neural network trained on 8760 hourly observations achieves an MSE of 2.346 at epoch 16, providing advanced predictive capability. Benchmarking against HOMER demonstrates clear advantages in grid exports (15,130 vs. 8274 kWh/year), battery cycling (445 vs. 9181 kWh/year), LCOE (€0.09 vs. €0.180/kWh), IRR (9% vs. 6%), payback (8.7 vs. 10.5 years), and CO2 emissions (−9.4 vs. 101 tons). These results confirm HySEC as a conceptually flexible solution that strengthens energy autonomy, supports heritage site rehabilitation, and promotes sustainable rural development. Full article
(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications, 2nd Edition)
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28 pages, 8905 KB  
Article
A Deep Recurrent Learning Framework for Multi-Class Microgrid Fault Classification Using LSTM and Bi-LSTM Models
by Rakesh Sahu, Pratap Kumar Panigrahi, Deepak Kumar Lal, Rudranarayan Pradhan and Chandrakanta Mahanty
Eng 2026, 7(3), 143; https://doi.org/10.3390/eng7030143 - 23 Mar 2026
Viewed by 83
Abstract
Fault detection in microgrids is a critical element of system stability and uninterrupted power delivery. Herein, a comparative study using LSTM and bidirectional LSTM networks is performed based on three-phase current data for multi-class fault classification. Five major fault types, namely LG, LL, [...] Read more.
Fault detection in microgrids is a critical element of system stability and uninterrupted power delivery. Herein, a comparative study using LSTM and bidirectional LSTM networks is performed based on three-phase current data for multi-class fault classification. Five major fault types, namely LG, LL, LLG, LLL, and LLLG, were simulated using a Real-Time Digital Simulator (RTDS) under grid-connected and islanded modes. Collected current signals were preprocessed, normalized, and segmented for sequence learning. Later, both models were trained using the best hyperparameter setting to enhance their capabilities and classify faults. To measure how well they identified faults, evaluation metrics, like accuracy, precision, recall, F1-score, and ROC-AUC, were calculated. The results revealed that the Bi-LSTM outperformed the LSTM and classical machine learning models consistently, with more than 99% accuracy for most fault types. More importantly, the proposed framework also checked classification performance for LLLG faults, with the Bi-LSTM model having a test accuracy of 98.8%. These results confirm that the Bi-LSTM model can robustly and precisely classify and detect faults in real time within specific phases of microgrids; therefore, it provides a scalable foundation for the development of intelligent protection in smart power systems. Full article
(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications, 2nd Edition)
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19 pages, 10235 KB  
Article
High-Fidelity 3D Reconstruction for Open-Pit Mine Digital Twins Using UAV Data and an Integrated 3D Gaussian Splatting Pipeline
by Laixin Zhang, Yuhong Tang and Zhuo Wang
Eng 2026, 7(3), 136; https://doi.org/10.3390/eng7030136 - 16 Mar 2026
Viewed by 246
Abstract
Addressing the challenges in 3D reconstruction of large-scale open-pit mines, such as dramatic terrain undulations, complex texture features, and the difficulty of balancing geometric accuracy with real-time rendering efficiency using traditional methods, this paper proposes a high-fidelity reconstruction framework integrating UAV multi-modal data [...] Read more.
Addressing the challenges in 3D reconstruction of large-scale open-pit mines, such as dramatic terrain undulations, complex texture features, and the difficulty of balancing geometric accuracy with real-time rendering efficiency using traditional methods, this paper proposes a high-fidelity reconstruction framework integrating UAV multi-modal data with the state-of-the-art 3D Gaussian Splatting (3DGS) architecture. First, an integrated air-ground multi-modal data acquisition system is established. Using a UAV equipped with LiDAR and a high-resolution camera, high-quality geometric and textural data of the mining area are acquired through terrain-adaptive flight planning. Second, to tackle the VRAM bottlenecks and loose geometric structures inherent in original 3DGS for large scenes, we adopt the advanced CityGaussianV2 architecture as our core reconstruction engine. By leveraging its divide-and-conquer parallel training strategy, 2DGS planar geometric constraints, and Decomposed Gradient Densification (DGD) mechanism, this framework effectively overcomes memory limitations and significantly enhances the geometric sharpness of slope crests and toes. Finally, engineering validation was conducted at Kambove Mining. Experimental results demonstrate that the proposed method achieves centimeter-level geometric accuracy, a real-time web rendering frame rate exceeding 60 FPS, and a model storage compression rate of over 90%. The digital twin control platform built upon this model successfully achieves deep fusion and visual scheduling of multi-source heterogeneous data, providing a novel technical path for constructing high-precision reality-based foundations for smart mines. Full article
(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications, 2nd Edition)
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21 pages, 4018 KB  
Article
HPO-Optimized Bidirectional LSTM for Gas Concentration Prediction in Coal Mine Working Faces
by Xiaoliang Zheng, Shilong Liu and Lei Zhang
Eng 2026, 7(3), 112; https://doi.org/10.3390/eng7030112 - 1 Mar 2026
Viewed by 280
Abstract
An HPO (Hunter–Prey Optimizer)-optimized Bidirectional LSTM (HPO-BiLSTM) model is introduced to address the challenges in predicting gas concentration within coal mining working faces. This study aims to adaptively adjust the key hyperparameters (such as learning rate and number of hidden layer units) of [...] Read more.
An HPO (Hunter–Prey Optimizer)-optimized Bidirectional LSTM (HPO-BiLSTM) model is introduced to address the challenges in predicting gas concentration within coal mining working faces. This study aims to adaptively adjust the key hyperparameters (such as learning rate and number of hidden layer units) of the BiLSTM network through intelligent optimization algorithms. While the BiLSTM architecture inherently mitigates gradient vanishing and exploding problems through its gating mechanisms, the proposed HPO method focuses on addressing the inefficiency of manual parameter tuning and the risk of trapping in local optima that traditional methods encounter when dealing with nonlinear and non-stationary gas concentration time series. The experiment utilized the actual methane monitoring data from the 15117 working face of Jishazhuang Coal Mine in Jinzhong City, Shanxi Province (with a sampling interval of 2 min). The proposed HPO-BiLSTM model was compared with baseline models such as LSTM, BiLSTM, GA-BiLSTM, and PSO-BiLSTM in terms of performance. This study systematically compares the performance of LSTM, BiLSTM, and BiLSTM models optimized with GA, PSO, and HPO. Results demonstrate that all optimized models outperform the baselines, with HPO-BiLSTM achieving the best overall performance. It attained the lowest RMSE and highest R2 across the training, validation, and test sets, showcasing superior fitting and generalization capabilities. Furthermore, HPO-BiLSTM converged to the lowest loss value (0.00062) in only 15 iterations, demonstrating significantly greater efficiency and stability than both GA-BiLSTM (loss 0.00072, 25 iterations) and PSO-BiLSTM (loss 0.00071, 30 iterations). The experiments confirm that the HPO algorithm effectively configures BiLSTM hyperparameters, mitigates overfitting, and provides a more accurate and robust solution for gas concentration prediction in coal mines. Full article
(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications, 2nd Edition)
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20 pages, 23733 KB  
Article
Fault Diagnosis of Power-Shift Systems in Agricultural Continuously Variable Transmissions Using Generative Adversarial Networks
by Kuan Liu, Xue Li, Ying Kong, Yangting Liu, Yanqiang Yang, Yehui Zhao, Qingjiang Li and Guangming Wang
Eng 2026, 7(3), 111; https://doi.org/10.3390/eng7030111 - 1 Mar 2026
Viewed by 230
Abstract
The power-shift system employed in agricultural multi-range continuously variable transmissions (CVTs) features a complex structure and control logic, presenting significant challenges to the reliability of agricultural machinery. To enable timely detection of faults, constructing an intelligent fault diagnosis classifier to monitor the system’s [...] Read more.
The power-shift system employed in agricultural multi-range continuously variable transmissions (CVTs) features a complex structure and control logic, presenting significant challenges to the reliability of agricultural machinery. To enable timely detection of faults, constructing an intelligent fault diagnosis classifier to monitor the system’s health status is essential. Typically, fault samples utilized for classifier development originate from ideal bench tests, characterized by uniform patterns and limited diversity, thereby hindering the algorithm’s generalization capability. This study addresses this issue by proposing a generative adversarial network (GAN) model, integrated with a triple loss function and a novel generator architecture, to augment the fault dataset under laboratory conditions. The generator architecture comprises a variational autoencoder module and an oil pressure point attention mechanism, enabling the generation of diverse and fluctuating virtual samples. Building on this augmented dataset, a fault classifier based on one-dimensional ConvNeXt was developed. Experimental results indicate that the classifier achieves an accuracy of 99.73%. While classifier accuracy decreases with increasing noise levels, the GAN-generated dataset provides more comprehensive training, resulting in an accuracy approximately 3% higher than that achieved using the original dataset. Full article
(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications, 2nd Edition)
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17 pages, 1189 KB  
Article
Prediction of Reverse Osmosis Membrane Fouling Using Machine Learning: MLR, ANN, and SVM at a Seawater Desalination Plant
by Siham Kherraf, Fatima-Zahra Abahdou, Maria Benbouzid, Zakaria Izouaouen, Abdellatif Aarfane, Abdoullatif Baraket, Hamid Nasrellah, Meryem Bensemlali, Soumia Ziti, Najoua Labjar and Souad El Hajjaji
Eng 2026, 7(3), 106; https://doi.org/10.3390/eng7030106 - 28 Feb 2026
Viewed by 417
Abstract
Membrane fouling remains a major obstacle to the performance of the reverse osmosis (RO) desalination processes. Artificial intelligence (AI) is now a promising approach for the reliable modeling of these complex systems. This study evaluates three modeling techniques—multiple linear regression (MLR), artificial neural [...] Read more.
Membrane fouling remains a major obstacle to the performance of the reverse osmosis (RO) desalination processes. Artificial intelligence (AI) is now a promising approach for the reliable modeling of these complex systems. This study evaluates three modeling techniques—multiple linear regression (MLR), artificial neural networks (ANNs), and support vector regression (SVR)—for predicting transmembrane pressure (TMP) at the Boujdour desalination plant, based on five input parameters: temperature, turbidity, pH, conductivity, and feedflow. The analysis is based on an original dataset of 195 daily measurements, and due to the absence of timestamps, the study focuses on state-to-TMP prediction rather than chronological forecasting, with no temporal generalization claimed. Approximately 2000 augmented training samples generated using a conservative SMOGN approach were used for model development, while performance evaluation relied exclusively on 39 independent real test observations. Two modeling strategies were adopted: (i) a minimalist approach based on significant variables identified by an ordinary least squares (OLS) model (pH and conductivity), and (ii) a multivariate approach integrating all parameters to capture non-linear interactions. A rigorous validation framework was put in place to avoid information leakage and ensure the robustness and generalizability of the models. Performance was evaluated using R2, RMSE, and MAE metrics, supplemented by robustness and significance analyses including bootstrap confidence intervals, paired statistical comparisons, and interpretability analyses based on permutation importance, partial dependence plots (PDPs), and individual conditional expectation (ICE) curves. The results indicate that the SVR model achieves the best average predictive accuracy among the tested models, albeit with moderate explanatory power. Full article
(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications, 2nd Edition)
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33 pages, 1844 KB  
Article
A Prototypical Fuzzy Similarity-Based Classification Framework for Ultrasonic Defect Detection in Concrete
by Matteo Cacciola, Giovanni Angiulli, Pietro Burrascano, Filippo Laganà and Mario Versaci
Eng 2026, 7(2), 88; https://doi.org/10.3390/eng7020088 - 14 Feb 2026
Cited by 1 | Viewed by 341
Abstract
In this study, we present an extension of the Takagi–Sugeno fuzzy inference system (TS-FIS) framework based on prototypical fuzzy similarity (PFS) for defect detection in concrete. The key novelty lies in integrating the PFS mechanism into the TS-FIS+ANFIS architecture, thus enabling a hybrid [...] Read more.
In this study, we present an extension of the Takagi–Sugeno fuzzy inference system (TS-FIS) framework based on prototypical fuzzy similarity (PFS) for defect detection in concrete. The key novelty lies in integrating the PFS mechanism into the TS-FIS+ANFIS architecture, thus enabling a hybrid rule–activation mechanism, bringing together fuzzy interpretability with data-driven similarity learning. To describe the ultrasonic concrete defect scenario, a high-fidelity finite element method (FEM) model that combines solid mechanics with fluid acoustics has been developed. From this numerical model, a synthetic dataset of about 36.8 million samples has been generated. The performance of the proposed TS-FIS+ANFIS+PFS classification system has been compared with that of a conventional FIS+ANFIS model, its particle-swarm-optimized (PSO) version and a Decision Tree (DT) classifier. The proposed model achieved the best performance, with a classification accuracy of 85.4% and an inference time of approximately 0.2 ms per sample. In contrast, the conventional, the PSO and the DT classifiers yielded accuracies of 60.5%, 62.0%, and 76.0%, respectively. These results confirm that PFS improves sensitivity and alleviates the computational effort, representing a potential candidate toward the realization of a defect abacus for concrete, an atlas conceived as a systematic collection of defect configurations associated with specific ultrasonic responses. Full article
(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications, 2nd Edition)
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31 pages, 3607 KB  
Article
Hybrid AI–Taguchi–ANOVA Approach for Thermographic Monitoring of Electronic Devices
by Filippo Laganà, Danilo Pratticò, Marco F. Quattrone, Salvatore A. Pullano and Salvatore Calcagno
Eng 2026, 7(1), 28; https://doi.org/10.3390/eng7010028 - 6 Jan 2026
Cited by 4 | Viewed by 574
Abstract
Defects in printed circuit boards (PCBs), if not detected promptly, may persist over time until they cause the failure of critical components. Traditional monitoring methods, which are limited to simulations or superficial measurements, obstruct predictive maintenance and real-time fault detection. To address these [...] Read more.
Defects in printed circuit boards (PCBs), if not detected promptly, may persist over time until they cause the failure of critical components. Traditional monitoring methods, which are limited to simulations or superficial measurements, obstruct predictive maintenance and real-time fault detection. To address these issues and enhance real-time diagnostics of thermal anomalies in PCBs, this work proposes an integrated system that combines infrared thermography (IRT), artificial intelligence (AI) algorithms, and Taguchi–ANOVA statistical techniques. IR thermography was employed to identify thermal stresses in the devices during normal operation. The IR acquisitions were used to build a dataset for specialized AI model’s training, which combines thermal anomalies segmentation using U-Net with a Multilayer Perceptron (MLP) classifier for heat distribution patterns. The Taguchi method determines the optimal configuration of the selected parameters, while Analysis of Variance (ANOVA) evaluates the effect of each factor on the F1-score response. These techniques statistically validated the AI performance, confirming the optimal set of selected hyperparameters and quantifying their contribution to F1-score. The novelty of the study lies in the integration of real-time infrared thermography with an interpretable AI pipeline and a Taguchi–ANOVA statistical framework, which enables both optimisation and rigorous validation of AI performance under real-time operating conditions. Full article
(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications, 2nd Edition)
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18 pages, 6849 KB  
Article
Neuro-Fuzzy Framework with CAD-Based Descriptors for Predicting Fabric Utilization Efficiency
by Anastasios Tzotzis, Prodromos Minaoglou, Dumitru Nedelcu, Simona-Nicoleta Mazurchevici and Panagiotis Kyratsis
Eng 2025, 6(12), 368; https://doi.org/10.3390/eng6120368 - 16 Dec 2025
Viewed by 598
Abstract
This study presents an intelligent modeling framework for predicting fabric nesting efficiency (NE) based on geometric descriptors of garment patterns, offering a rapid alternative to conventional nesting software. A synthetic dataset of 1000 layouts was generated using a custom Python algorithm that simulates [...] Read more.
This study presents an intelligent modeling framework for predicting fabric nesting efficiency (NE) based on geometric descriptors of garment patterns, offering a rapid alternative to conventional nesting software. A synthetic dataset of 1000 layouts was generated using a custom Python algorithm that simulates realistic garment-like shapes within a fixed fabric size. Each layout was characterized by five geometric descriptors: number of pieces (NP), average piece area (APA), average aspect ratio (AAR), average compactness (AC), and average convexity (CVX). The relationship between these descriptors and NE was modeled using a Sugeno-type Adaptive Neuro-Fuzzy Inference System (ANFIS). Various membership function (MF) structures were examined, and the configuration 3-3-2-2-2 was identified as optimal, yielding a mean relative error of −0.1%, with high coefficient of determination (R2 > 0.98). The model was validated through comparison between predicted NE values and results obtained from an actual nesting process performed with Deepnest.io, demonstrating strong agreement. The proposed method enables efficient estimation of NE directly from CAD-based parameters, without requiring computationally intensive nesting simulations. This approach provides a valuable decision-support tool for fabric and apparel designers, facilitating rapid assessment of material utilization and supporting design optimization toward reduced fabric waste. Full article
(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications, 2nd Edition)
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22 pages, 3621 KB  
Article
Predictive Maintenance in Underground Mining Equipment Using Artificial Intelligence
by Nelson Chambi, Celso Sanga, Jorge Ortiz, Alejandra Sanga, Piero Sanga, Rosiand Manrique and Julio Lu-Chang-Say
Eng 2025, 6(10), 261; https://doi.org/10.3390/eng6100261 - 3 Oct 2025
Cited by 2 | Viewed by 5599
Abstract
Underground mining faces unique challenges in equipment maintenance due to extreme operating conditions and intensive use, which limit the effectiveness of traditional methods. This study proposes a predictive maintenance (PdM) framework based on artificial intelligence (AI) to optimize efficiency and reduce costs, focusing [...] Read more.
Underground mining faces unique challenges in equipment maintenance due to extreme operating conditions and intensive use, which limit the effectiveness of traditional methods. This study proposes a predictive maintenance (PdM) framework based on artificial intelligence (AI) to optimize efficiency and reduce costs, focusing on early fault detection. The methodology integrates IoT sensors to monitor key parameters (temperature, pressure, oil analysis, and wear) in real time, combined with machine learning models to identify predictive patterns. The results demonstrate an 8% reduction in maintenance costs and a 10% increase in equipment availability, validating the system’s ability to anticipate failures and minimize unplanned downtime. It is concluded that this approach not only enhances productivity but also raises safety standards, offering a scalable model for critical industrial environments. The findings are supported by empirical data collected from actual operations, with no theoretical extrapolations. Full article
(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications, 2nd Edition)
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23 pages, 4446 KB  
Article
A Modular Framework for RGB Image Processing and Real-Time Neural Inference: A Case Study in Microalgae Culture Monitoring
by José Javier Gutiérrez-Ramírez, Ricardo Enrique Macias-Jamaica, Víctor Manuel Zamudio-Rodríguez, Héctor Arellano Sotelo, Dulce Aurora Velázquez-Vázquez, Juan de Anda-Suárez and David Asael Gutiérrez-Hernández
Eng 2025, 6(9), 221; https://doi.org/10.3390/eng6090221 - 2 Sep 2025
Cited by 1 | Viewed by 1225
Abstract
Recent progress in computer vision and embedded systems has facilitated real-time monitoring of bioprocesses; however, lightweight and scalable solutions for resource-constrained settings remain limited. This work presents a modular framework for monitoring Chlorella vulgaris growth by integrating RGB image processing with multimodal sensor [...] Read more.
Recent progress in computer vision and embedded systems has facilitated real-time monitoring of bioprocesses; however, lightweight and scalable solutions for resource-constrained settings remain limited. This work presents a modular framework for monitoring Chlorella vulgaris growth by integrating RGB image processing with multimodal sensor fusion. The system incorporates a Logitech C920 camera and low-cost pH and temperature sensors within a compact photobioreactor. It extracts RGB channel statistics, luminance, and environmental data to generate a 10-dimensional feature vector. A feedforward artificial neural network (ANN) with ReLU activations, dropout layers, and SMOTE-based data balancing was trained to classify growth phases: lag, exponential, and stationary. The optimized model, quantized to 8 bits, was deployed on an ESP32 microcontroller, achieving 98.62% accuracy with 4.8 ms inference time and a 13.48 kB memory footprint. Robustness analysis confirmed tolerance to geometric transformations, though variable lighting reduced performance. Principal component analysis (PCA) retained 95% variance, supporting the discriminative power of the features. The proposed system outperformed previous vision-only methods, demonstrating the advantages of multimodal fusion for early detection. Limitations include sensitivity to lighting and validation limited to a single species. Future directions include incorporating active lighting control and extending the model to multi-species classification for broader applicability. Full article
(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications, 2nd Edition)
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24 pages, 3254 KB  
Article
Ghost-YOLO-GBH: A Lightweight Framework for Robust Small Traffic Sign Detection via GhostNet and Bidirectional Multi-Scale Feature Fusion
by Jingyi Tang, Bu Xu, Jue Li, Mengyuan Zhang, Chao Huang and Feng Li
Eng 2025, 6(8), 196; https://doi.org/10.3390/eng6080196 - 7 Aug 2025
Cited by 3 | Viewed by 1330
Abstract
Traffic safety is a significant global concern, and traffic sign recognition (TSR) is essential for the advancement of intelligent transportation systems. Traditional YOLO11s-based methods often struggle to balance detection accuracy and processing speed, particularly in the context of small traffic signs within complex [...] Read more.
Traffic safety is a significant global concern, and traffic sign recognition (TSR) is essential for the advancement of intelligent transportation systems. Traditional YOLO11s-based methods often struggle to balance detection accuracy and processing speed, particularly in the context of small traffic signs within complex environments. To address these challenges, this study presents Ghost-YOLO-GBH, an innovative lightweight model that incorporates three key enhancements: (1) the integration of a GhostNet backbone, which substitutes the conventional YOLO11s architecture and utilizes Ghost modules to exploit feature redundancy, resulting in a 40.6% reduction in computational load while ensuring effective feature extraction for small targets; (2) the development of a HybridFocus module that combines large separable kernel attention with multi-scale pooling, effectively minimizing background interference and improving contextual feature aggregation by 4.3% in isolated tests; and (3) the implementation of a Bidirectional Dynamic Multi-Scale Feature Pyramid Network (BiDMS-FPN) that allows for bidirectional cross-stage feature fusion, significantly enhancing the accuracy of small target detection. Experimental results on the TT100K dataset indicate that Ghost-YOLO-GBH achieves an impressive 81.10% mean Average Precision (mAP) at a threshold of 0.5, along with an 11.7% increase in processing speed (45 FPS) and an 18.2% reduction in model parameters (7.74 M) compared to the baseline YOLO11s. Overall, Ghost-YOLO-GBH effectively balances accuracy, efficiency, and lightweight deployment, demonstrating superior performance in real-world applications characterized by small signs and cluttered backgrounds. This research provides a novel framework for resource-constrained TSR applications, contributing to the evolution of intelligent transportation systems. Full article
(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications, 2nd Edition)
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19 pages, 3119 KB  
Article
Distress-Based Pavement Condition Assessment Using Artificial Intelligence: A Case Study of Egyptian Roads
by Mostafa M. Radwan, Sundus A. Faris, Ahmed Y. Barakat and Ahmad Mousa
Eng 2025, 6(6), 114; https://doi.org/10.3390/eng6060114 - 28 May 2025
Cited by 2 | Viewed by 2756
Abstract
The pavement is a complex construction subject to a range of environmental and loading conditions. Transportation organizations use pavement management systems (PMSs) to maintain satisfactory pavement performance. The pavement condition index (PCI) is a commonly used performance indicator, yet PCI evaluation is costly [...] Read more.
The pavement is a complex construction subject to a range of environmental and loading conditions. Transportation organizations use pavement management systems (PMSs) to maintain satisfactory pavement performance. The pavement condition index (PCI) is a commonly used performance indicator, yet PCI evaluation is costly and time-consuming. Machine and deep learning algorithms have recently been more instrumental for forecasting pavement conditions. This research uses AI tools to develop a correlation between PCI and collected distress in urban road networks. The distresses for 15,000 pavement segments in Egypt were investigated through a desk study and field data collection. To this end, several machine learning (ML) and deep learning approaches were developed. The ML techniques include random forest (RF), support vector machine (SVM), decision tree (DT), and the deep learning approach entails artificial neural networks (ANN). The proposed techniques provide precise PCI estimates and can be seamlessly integrated with PMCs using ubiquitous spreadsheet programs. The results have shown excellent predictions of the ANN model, as demonstrated in the high coefficient of determination (R2  = 0.939) and the low root mean squared error (RMSE = 7.20) and the mean absolute error (MAE = 2.94). This study sets out to provide a reliable and affordable alternative to specialized tools like MicroPAVER. The ANN model exhibited greater prediction accuracy than the other developed models and can also reliably forecast PCI values by using only measured distress data. Full article
(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications, 2nd Edition)
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23 pages, 3009 KB  
Article
Parametric Optimization of Train Brake Pad Using Reverse Engineering with Digital Photogrammetry 3D Modeling Method
by P Paryanto, Muhammad Faizin and R Rusnaldy
Eng 2025, 6(5), 96; https://doi.org/10.3390/eng6050096 - 12 May 2025
Cited by 1 | Viewed by 1247
Abstract
Reverse engineering (RE) is essential in recreating 3D models of existing manufactured parts. It is widely used for repairing damaged components, improving used parts, and designing new items based on older models. One of the most common methods in RE is photogrammetry, which [...] Read more.
Reverse engineering (RE) is essential in recreating 3D models of existing manufactured parts. It is widely used for repairing damaged components, improving used parts, and designing new items based on older models. One of the most common methods in RE is photogrammetry, which enables 3D reconstruction by capturing multiple images. Therefore, this study aimed to explore the application of mobile photogrammetry to obtain a 3D model of a train brake pad. The process started with capturing images of objects in a quick and professional manner to ensure visualization of data. This was followed by processing 2D images using Agisoft Metashape 2.2.1 software and Artificial Intelligence (AI) algorithms to create a precise 3D model. Subsequently, assessment was performed using feasibility in terms of cost, time, and accuracy. The results show that mobile photogrammetry provided an accessible and cost-effective method, alongside maximum contact stress after reducing optimization by approximately 28.42%, with maximum error value measured by the virtual model with the reference value of 0.30 mm (on Metashape) and 0.46 mm (on AI). This suggested that reverse parameterization significantly accelerated computer-aided design (CAD) model reconstruction and reduced the part redesign development cycle. By using photogrammetry and parametric modeling, engineers could accurately analyze and optimize train brake pads, ensuring safety as well as sustainability in railway operations. Additionally, RE and parametric modeling could assist in creating durable, cost-effective alternatives, and predicting appropriate replacements. Full article
(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications, 2nd Edition)
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32 pages, 6141 KB  
Perspective
A Brief Perspective on Deep Learning Approaches for 2D Semantic Segmentation
by Shazia Sulemane, Nuno Fachada and João P. Matos-Carvalho
Eng 2025, 6(7), 165; https://doi.org/10.3390/eng6070165 - 18 Jul 2025
Viewed by 1670
Abstract
Semantic segmentation is a vast field with many contributions, which can be difficult to organize and comprehend due to the amount of research available. Advancements in technology and processing power over the past decade have led to a significant increase in the number [...] Read more.
Semantic segmentation is a vast field with many contributions, which can be difficult to organize and comprehend due to the amount of research available. Advancements in technology and processing power over the past decade have led to a significant increase in the number of developed models and architectures. This paper provides a brief perspective on 2D segmentation by summarizing the mechanisms of various neural network models and the tools and datasets used for their training, testing, and evaluation. Additionally, this paper discusses methods for identifying new architectures, such as Neural Architecture Search, and explores the emerging research field of continuous learning, which aims to develop models capable of learning continuously from new data. Full article
(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications, 2nd Edition)
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