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Search Results (848)

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Keywords = hybrid real-time simulator

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14 pages, 1081 KB  
Article
Hybrid Deep Learning Approach for Secure Electric Vehicle Communications in Smart Urban Mobility
by Abdullah Alsaleh
Vehicles 2025, 7(4), 112; https://doi.org/10.3390/vehicles7040112 - 2 Oct 2025
Abstract
The increasing adoption of electric vehicles (EVs) within intelligent transportation systems (ITSs) has elevated the importance of cybersecurity, especially with the rise in Vehicle-to-Everything (V2X) communications. Traditional intrusion detection systems (IDSs) struggle to address the evolving and complex nature of cyberattacks in such [...] Read more.
The increasing adoption of electric vehicles (EVs) within intelligent transportation systems (ITSs) has elevated the importance of cybersecurity, especially with the rise in Vehicle-to-Everything (V2X) communications. Traditional intrusion detection systems (IDSs) struggle to address the evolving and complex nature of cyberattacks in such dynamic environments. To address these challenges, this study introduces a novel deep learning-based IDS designed specifically for EV communication networks. We present a hybrid model that integrates convolutional neural networks (CNNs), long short-term memory (LSTM) layers, and adaptive learning strategies. The model was trained and validated using the VeReMi dataset, which simulates a wide range of attack scenarios in V2X networks. Additionally, an ablation study was conducted to isolate the contribution of each of its modules. The model demonstrated strong performance with 98.73% accuracy, 97.88% precision, 98.91% sensitivity, and 98.55% specificity, as well as an F1-score of 98.39%, an MCC of 0.964, a false-positive rate of 1.45%, and a false-negative rate of 1.09%, with a detection latency of 28 ms and an AUC-ROC of 0.994. Specifically, this work fills a clear gap in the existing V2X intrusion detection literature—namely, the lack of scalable, adaptive, and low-latency IDS solutions for hardware-constrained EV platforms—by proposing a hybrid CNN–LSTM architecture coupled with an elastic weight consolidation (EWC)-based adaptive learning module that enables online updates without full retraining. The proposed model provides a real-time, adaptive, and high-precision IDS for EV networks, supporting safer and more resilient ITS infrastructures. Full article
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24 pages, 9060 KB  
Article
Uncertainty Propagation for Vibrometry-Based Acoustic Predictions Using Gaussian Process Regression
by Andreas Wurzinger and Stefan Schoder
Appl. Sci. 2025, 15(19), 10652; https://doi.org/10.3390/app151910652 - 1 Oct 2025
Abstract
Shell-like housing structures for motors and compressors can be found in everyday products. Consumers significantly evaluate acoustic emissions during the first usage of products. Unpleasant sounds may raise concerns and cause complaints to be issued. A prevention strategy is a holistic acoustic design, [...] Read more.
Shell-like housing structures for motors and compressors can be found in everyday products. Consumers significantly evaluate acoustic emissions during the first usage of products. Unpleasant sounds may raise concerns and cause complaints to be issued. A prevention strategy is a holistic acoustic design, which includes predicting the emitted sound power as part of end-of-line testing. The hybrid experimental-simulative sound power prediction based on laser scanning vibrometry (LSV) is ideal in acoustically harsh production environments. However, conducting vibroacoustic testing with laser scanning vibrometry is time-consuming, making it difficult to fit into the production cycle time. This contribution discusses how the time-consuming sampling process can be accelerated to estimate the radiated sound power, utilizing adaptive sampling. The goal is to predict the acoustic signature and its uncertainty from surface velocity data in seconds. Fulfilling this goal will enable integration into a product assembly unit and final acoustic quality control without the need for an acoustic chamber. The Gaussian process regression based on PyTorch 2.6.0 performed 60 times faster than the preliminary reference implementation, resulting in a regression estimation time of approximately one second for each frequency bin. In combination with the Equivalent Radiated Power prediction of the sound power, a statistical measure is available, indicating how the uncertainty of a limited number of surface velocity measurement points leads to predictions of the uncertainty inside the acoustical signal. An adaptive sampling algorithm reduces the prediction uncertainty in real-time during measurement. The method enables on-the-fly error analysis in production, assessing the risk of violating agreed-upon acoustic sound power thresholds, and thus provides valuable feedback to the product design units. Full article
24 pages, 11005 KB  
Article
Hybrid Finite Control Set Model Predictive Control and Universal Droop Control for Enhanced Power Sharing in Inverter-Based Microgrids
by Devarapalli Vimala, Naresh Kumar Vemula, Bhamidi Lokeshgupta, Ramesh Devarapalli and Łukasz Knypiński
Energies 2025, 18(19), 5200; https://doi.org/10.3390/en18195200 - 30 Sep 2025
Abstract
This paper proposes a novel hybrid control strategy integrating a Finite Control Set Model Predictive Controller (FCS-MPC) with a universal droop controller (UDC) for effective load power sharing in inverter-fed microgrids. Traditional droop-based methods, though widely adopted for their simplicity and decentralized nature, [...] Read more.
This paper proposes a novel hybrid control strategy integrating a Finite Control Set Model Predictive Controller (FCS-MPC) with a universal droop controller (UDC) for effective load power sharing in inverter-fed microgrids. Traditional droop-based methods, though widely adopted for their simplicity and decentralized nature, suffer from limitations such as steady-state inaccuracies and poor transient response, particularly under mismatched impedance conditions. To overcome these drawbacks, the proposed scheme incorporates detailed modeling of inverter and source dynamics within the predictive controller to enhance accuracy, stability, and response speed. The UDC complements the predictive framework by ensuring coordination among inverters with different impedance characteristics. Simulation results under various load disturbances demonstrate that the proposed approach significantly outperforms conventional PI-based droop control in terms of voltage and frequency regulation, transient stability, and balanced power sharing. The performance is further validated through real-time simulations, affirming the scheme’s potential for practical deployment in dynamic microgrid environments. Full article
(This article belongs to the Special Issue Planning, Operation and Control of Microgrids: 2nd Edition)
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37 pages, 905 KB  
Review
Application of Fuzzy Logic Techniques in Solar Energy Systems: A Review
by Siviwe Maqekeni, KeChrist Obileke, Odilo Ndiweni and Patrick Mukumba
Appl. Syst. Innov. 2025, 8(5), 144; https://doi.org/10.3390/asi8050144 - 30 Sep 2025
Abstract
Fuzzy logic has been applied to a wide range of problems, including process control, object recognition, image and signal processing, prediction, classification, decision-making, optimization, and time series analysis. These apply to solar energy systems. Though experts in renewable energy prefer fuzzy logic techniques, [...] Read more.
Fuzzy logic has been applied to a wide range of problems, including process control, object recognition, image and signal processing, prediction, classification, decision-making, optimization, and time series analysis. These apply to solar energy systems. Though experts in renewable energy prefer fuzzy logic techniques, their contribution to the decision-making process of solar energy systems lies in the possibility of illustrating risk factors and introducing the concepts of linguistic variables of data from solar energy applications. In solar energy systems, the primary beneficiaries and audience of the fuzzy logic techniques are solar energy policy makers, as it concerns decision-making models, ranking of criteria or weights, and assessment of the potential location of the installation of solar energy plants, depending on the case. In a real-world scenario, fuzzy logic allows easy and efficient controller configuration in a non-linear control system, such as a solar panel. This study attempts to review the role and contribution of fuzzy logic in solar energy based on its applications. The findings from the review revealed that the fuzzy logic application identifies and detects faults in solar energy systems as well as in the optimization of energy output and the location of solar energy plants. In addition, fuzzy model (predicting), hybrid model (simulating performance), and multi-criteria decision-making (MCDM) are components of fuzzy logic techniques. As the review indicated, these are useful as a solution to the challenges of solar energy systems. Importantly, the integration and incorporation of fuzzy logic and neural networks should be recommended for the efficient and effective performance of solar energy systems. Full article
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22 pages, 2085 KB  
Article
Trends in Using Microalgae as a Green Energy Source: Conventional, Machine Learning, and Hybrid Modeling Methods
by Ángel Darío González-Delgado, Segundo Rojas-Flores and Anibal Alviz-Meza
Processes 2025, 13(10), 3134; https://doi.org/10.3390/pr13103134 - 29 Sep 2025
Abstract
This study analyzes, quantifies, and maps, from a bibliometric perspective, scientific research on microalgae energy production. It includes traditional simulation, machine learning, and hybrid approaches, covering 500 original articles from 2005 to 2024 in Scopus. We used Biblioshiny 4.1.2 software in RStudio 4.3.0 [...] Read more.
This study analyzes, quantifies, and maps, from a bibliometric perspective, scientific research on microalgae energy production. It includes traditional simulation, machine learning, and hybrid approaches, covering 500 original articles from 2005 to 2024 in Scopus. We used Biblioshiny 4.1.2 software in RStudio 4.3.0 to categorize and evaluate the contributions of authors and journals. The studied field underwent an exponential growth in publications from 2004 to 2022, with an average annual increase of approximately 21%. Moreover, recent research focuses on photobioreactors, computational fluid dynamics, carbon dioxide capture, bio-oils, biodiesel, and hydrothermal liquefaction, increasingly integrating machine learning algorithms and hybrid methods. Since 2020, we have identified a clear trend toward combining modeling approaches to predict and improve energy efficiency, particularly for biodiesel, bio-derived hydrogen, and crude bio-oil produced via pyrolysis or hydrothermal liquefaction, which is often influenced by factors such as light, carbon dioxide, nutrients, and blending operations. Finally, recent advancements involve combining physical models with data to enable real-time optimization and control, supporting microalgae-based circular biorefining strategies. This review serves as a guide for future research in green energy materials and process modeling, inspiring colleagues to explore new ways for microalgae energy production and modeling. Full article
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22 pages, 1797 KB  
Article
A Novel Hybrid Deep Learning–Probabilistic Framework for Real-Time Crash Detection from Monocular Traffic Video
by Reşat Buğra Erkartal and Atınç Yılmaz
Appl. Sci. 2025, 15(19), 10523; https://doi.org/10.3390/app151910523 - 29 Sep 2025
Abstract
The rapid evolution of autonomous vehicle technologies has amplified the need for crash detection that operates robustly under complex traffic conditions with minimal latency. We propose a hybrid temporal hierarchy that augments a Region-based Convolutional Neural Network (R-CNN) with an adaptive time-variant Kalman [...] Read more.
The rapid evolution of autonomous vehicle technologies has amplified the need for crash detection that operates robustly under complex traffic conditions with minimal latency. We propose a hybrid temporal hierarchy that augments a Region-based Convolutional Neural Network (R-CNN) with an adaptive time-variant Kalman filter (with total-variation prior), a Hidden Markov Model (HMM) for state stabilization, and a lightweight Artificial Neural Network (ANN) for learned temporal refinement, enabling real-time crash detection from monocular video. Evaluated on simulated traffic in CARLA and real-world driving in Istanbul, the full temporal stack achieves the best precision–recall balance, yielding 83.47% F1 offline and 82.57% in real time (corresponding to 94.5% and 91.2% detection accuracy, respectively). Ablations are consistent and interpretable: removing the HMM reduces F1 by 1.85–2.16 percentage points (pp), whereas removing the ANN has a larger impact of 2.94–4.58 pp, indicating that the ANN provides the largest marginal gains—especially under real-time constraints. The transition from offline to real time incurs a modest overall loss (−0.90 pp F1), driven more by recall than precision. Compared to strong single-frame baselines, YOLOv10 attains 82.16% F1 and a real-time Transformer detector reaches 82.41% F1, while our full temporal stack remains slightly ahead in real time and offers a more favorable precision–recall trade-off. Notably, integrating the ANN into the HMM-based pipeline improves accuracy by 2.2%, while the time-variant Kalman configuration reduces detection lag by approximately 0.5 s—an improvement that directly addresses the human reaction time gap. Under identical conditions, the best RCNN-based configuration yields AP@0.50 ≈ 0.79 with an end-to-end latency of 119 ± 21 ms per frame (~8–9 FPS). Overall, coupling deep learning with probabilistic reasoning yields additive temporal benefits and advances deployable, camera-only crash detection that is cost-efficient and scalable for intelligent transportation systems. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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36 pages, 6811 KB  
Article
A Hierarchical Two-Layer MPC-Supervised Strategy for Efficient Inverter-Based Small Microgrid Operation
by Salima Meziane, Toufouti Ryad, Yasser O. Assolami and Tawfiq M. Aljohani
Sustainability 2025, 17(19), 8729; https://doi.org/10.3390/su17198729 - 28 Sep 2025
Abstract
This study proposes a hierarchical two-layer control framework aimed at advancing the sustainability of renewable-integrated microgrids. The framework combines droop-based primary control, PI-based voltage and current regulation, and a supervisory Model Predictive Control (MPC) layer to enhance dynamic power sharing and system stability [...] Read more.
This study proposes a hierarchical two-layer control framework aimed at advancing the sustainability of renewable-integrated microgrids. The framework combines droop-based primary control, PI-based voltage and current regulation, and a supervisory Model Predictive Control (MPC) layer to enhance dynamic power sharing and system stability in renewable-integrated microgrids. The proposed method addresses the limitations of conventional control techniques by coordinating real and reactive power flow through an adaptive droop formulation and refining voltage/current regulation with inner-loop PI controllers. A discrete-time MPC algorithm is introduced to optimize power setpoints under future disturbance forecasts, accounting for state-of-charge limits, DC-link voltage constraints, and renewable generation variability. The effectiveness of the proposed strategy is demonstrated on a small hybrid microgrid system that serve a small community of buildings with a solar PV, wind generation, and a battery storage system under variable load and environmental profiles. Initial uncontrolled scenarios reveal significant imbalances in resource coordination and voltage deviation. Upon applying the proposed control, active and reactive power are equitably shared among DG units, while voltage and frequency remain tightly regulated, even during abrupt load transitions. The proposed control approach enhances renewable energy integration, leading to reduced reliance on fossil-fuel-based resources. This contributes to environmental sustainability by lowering greenhouse gas emissions and supporting the transition to a cleaner energy future. Simulation results confirm the superiority of the proposed control strategy in maintaining grid stability, minimizing overcharging/overdischarging of batteries, and ensuring waveform quality. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Energy Sustainability)
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20 pages, 5249 KB  
Article
Research on Anomaly Detection in Wastewater Treatment Systems Based on a VAE-LSTM Fusion Model
by Xin Liu, Zhengxuan Gong and Xing Zhang
Water 2025, 17(19), 2842; https://doi.org/10.3390/w17192842 - 28 Sep 2025
Abstract
This study addresses the problem of anomaly detection in water treatment systems by proposing a hybrid VAE–LSTM model with a combined loss function that integrates reconstruction and prediction errors. Following the signal flow of wastewater treatment systems, data acquisition, transmission, and cyberattack scenarios [...] Read more.
This study addresses the problem of anomaly detection in water treatment systems by proposing a hybrid VAE–LSTM model with a combined loss function that integrates reconstruction and prediction errors. Following the signal flow of wastewater treatment systems, data acquisition, transmission, and cyberattack scenarios were simulated, and a dual-dimensional learning framework of “feature space—temporal space” was designed: the VAE learns latent data distributions and computes reconstruction errors, while the LSTM models temporal dependencies and computes prediction errors. Anomaly decisions are made through feature extraction and weighted scoring. Experimental comparisons show that the proposed fusion model achieves an accuracy of approximately 0.99 and an F1-Score of about 0.75, significantly outperforming single models such as Isolation Forest and One-Class SVM. It can accurately identify attack anomalies in devices such as the LIT101 sensor and MV101 actuator, e.g., water tank overflow and state transitions, with reconstruction errors primarily beneath 0.08 ensuring detection reliability. In terms of time efficiency, Isolation Forest is suitable for real-time preliminary screening, while VAE-LSTM adapts to high-precision detection scenarios with an “offline training (423 s) + online detection (1.39 s)” mode. This model provides a practical solution for intelligent monitoring of industrial water treatment systems. Future research will focus on model lightweighting, enhanced data generalization, and integration with edge computing to improve system applicability and robustness. The proposed approach breaks through the limitations of traditional single models, demonstrating superior performance in detection accuracy and scenario adaptability. It offers technical support for improving the operational efficiency and security of water treatment systems and serves as a paradigm reference for anomaly detection in similar industrial systems. Full article
(This article belongs to the Section Wastewater Treatment and Reuse)
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29 pages, 1730 KB  
Article
Explaining Corporate Ratings Transitions and Defaults Through Machine Learning
by Nazário Augusto de Oliveira and Leonardo Fernando Cruz Basso
Algorithms 2025, 18(10), 608; https://doi.org/10.3390/a18100608 - 28 Sep 2025
Abstract
Credit rating transitions and defaults are critical indicators of corporate creditworthiness, yet their accurate modeling remains a persistent challenge in risk management. Traditional models such as logistic regression (LR) and structural approaches (e.g., Merton’s model) offer transparency but often fail to capture nonlinear [...] Read more.
Credit rating transitions and defaults are critical indicators of corporate creditworthiness, yet their accurate modeling remains a persistent challenge in risk management. Traditional models such as logistic regression (LR) and structural approaches (e.g., Merton’s model) offer transparency but often fail to capture nonlinear relationships, temporal dynamics, and firm heterogeneity. This study proposes a hybrid machine learning (ML) framework to explain and predict corporate rating transitions and defaults, addressing key limitations in existing literature. We benchmark four classification algorithms—LR, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machines (SVM)—on a structured corporate credit dataset. Our approach integrates segment-specific modeling across rating bands, out-of-time validation to simulate real-world applicability, and SHapley Additive exPlanations (SHAP) values to ensure interpretability. The results demonstrate that ensemble methods, particularly XGBoost and RF, significantly outperform LR and SVM in predictive accuracy and early warning capability. Moreover, SHAP analysis reveals differentiated drivers of rating transitions across credit quality segments, highlighting the importance of tailored monitoring strategies. This research contributes to the literature by bridging predictive performance with interpretability in credit risk modeling and offers practical implications for regulators, rating agencies, and financial institutions seeking robust, transparent, and forward-looking credit assessment tools. Full article
(This article belongs to the Special Issue AI Applications and Modern Industry)
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23 pages, 348 KB  
Review
Machine Learning-Based Quality Control for Low-Cost Air Quality Monitoring: A Comprehensive Review of the Past Decade
by Yong-Hyuk Kim and Seung-Hyun Moon
Atmosphere 2025, 16(10), 1136; https://doi.org/10.3390/atmos16101136 - 27 Sep 2025
Abstract
Air pollution poses major risks to public health, driving the adoption of low-cost sensor (LCS) networks for fine-grained and real-time monitoring. However, the variable accuracy of LCS data compared with reference instruments necessitates robust quality control (QC) frameworks. Over the past decade, machine [...] Read more.
Air pollution poses major risks to public health, driving the adoption of low-cost sensor (LCS) networks for fine-grained and real-time monitoring. However, the variable accuracy of LCS data compared with reference instruments necessitates robust quality control (QC) frameworks. Over the past decade, machine learning (ML) has emerged as a powerful tool to calibrate sensors, detect anomalies, and mitigate drift in large-scale deployment. This survey reviews advances in three methodological categories: traditional ML models, deep learning architectures, and hybrid or unsupervised methods. We also examine spatiotemporal QC frameworks that exploit redundancies across time and space, as well as real-time implementations based on edge–cloud architectures. Applications include personal exposure monitoring, integration with atmospheric simulations, and support for policy decision making. Despite these achievements, several challenges remain. Traditional models are lightweight but often fail to generalize across contexts, while deep learning models achieve higher accuracy but demand large datasets and remain difficult to interpret. Spatiotemporal approaches improve robustness but face scalability constraints, and real-time systems must balance computational efficiency with accuracy. Broader adoption will also require clear standards, reliable uncertainty quantification, and sustained trust in corrected data. In summary, ML-based QC shows strong potential but is still constrained by data quality, transferability, and governance gaps. Future work should integrate physical knowledge with ML, leverage federated learning for scalability, and establish regulatory benchmarks. Addressing these challenges will enable ML-driven QC to deliver reliable, high-resolution data that directly support science-based policy and public health. Full article
(This article belongs to the Special Issue Emerging Technologies for Observation of Air Pollution (2nd Edition))
21 pages, 4655 KB  
Article
A Geometric Distortion Correction Method for UAV Projection in Non-Planar Scenarios
by Hao Yi, Sichen Li, Feifan Yu, Mao Xu and Xinmin Chen
Aerospace 2025, 12(10), 870; https://doi.org/10.3390/aerospace12100870 - 27 Sep 2025
Abstract
Conventional projection systems typically require a fixed spatial configuration relative to the projection surface, with strict control over distance and angle. In contrast, UAV-mounted projectors overcome these constraints, enabling dynamic, large-scale projections onto non-planar and complex environments. However, such flexible scenarios introduce a [...] Read more.
Conventional projection systems typically require a fixed spatial configuration relative to the projection surface, with strict control over distance and angle. In contrast, UAV-mounted projectors overcome these constraints, enabling dynamic, large-scale projections onto non-planar and complex environments. However, such flexible scenarios introduce a key challenge: severe geometric distortions caused by intricate surface geometry and continuous camera–projector motion. To address this, we propose a novel image registration method based on global dense matching, which estimates the real-time optical flow field between the input projection image and the target surface. The estimated flow is used to pre-warp the image, ensuring that the projected content appears geometrically consistent across arbitrary, deformable surfaces. The core idea of our method lies in reformulating the geometric distortion correction task as a global feature matching problem, effectively reducing 3D spatial deformation into a 2D dense correspondence learning process. To support learning and evaluation, we construct a hybrid dataset that covers a wide range of projection scenarios, including diverse lighting conditions, object geometries, and projection contents. Extensive simulation and real-world experiments show that our method achieves superior accuracy and robustness in correcting geometric distortions in dynamic UAV projection, significantly enhancing visual fidelity in complex environments. This approach provides a practical solution for real-time, high-quality projection in UAV-based augmented reality, outdoor display, and aerial information delivery systems. Full article
(This article belongs to the Section Aeronautics)
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39 pages, 13889 KB  
Review
Machine Learning for Design Optimization and PCM-Based Storage in Plate Heat Exchangers: A Review
by Fatemeh Isania and Antonio Galgaro
Energies 2025, 18(19), 5115; https://doi.org/10.3390/en18195115 - 25 Sep 2025
Abstract
This review critically examines the intersection of machine learning (ML), plate heat exchangers (PHEs), and latent heat thermal energy storage (LHTES) using phase-change materials (PCMs)—a combination not comprehensively addressed in the existing literature. Covering more than 120 peer-reviewed studies published between 2015 and [...] Read more.
This review critically examines the intersection of machine learning (ML), plate heat exchangers (PHEs), and latent heat thermal energy storage (LHTES) using phase-change materials (PCMs)—a combination not comprehensively addressed in the existing literature. Covering more than 120 peer-reviewed studies published between 2015 and 2025, we analyze the deployment of ML methods—including artificial neural networks, ensemble models, physics-informed neural networks, and hybrid optimization techniques—for modeling, performance enhancement, and real-time control of PCM-integrated PHE systems. Particular attention is given to ML-driven geometry optimization, flow prediction, and surrogate modeling for computational fluid dynamics (CFD) simulations. The review also explores digital twin development and nanofluid-enhanced storage strategies. By addressing key gaps in dataset availability, model interpretability, and integration challenges, we provide a structured roadmap for future research, emphasizing hybrid ML–physics models, explainable AI, and standardized benchmarking. This work offers a data-driven and focused perspective on advancing the design of intelligent and sustainable thermal systems. Full article
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29 pages, 3717 KB  
Article
Inverse Procedure to Initial Parameter Estimation for Air-Dropped Packages Using Neural Networks
by Beata Potrzeszcz-Sut and Marta Grzyb
Appl. Sci. 2025, 15(19), 10422; https://doi.org/10.3390/app151910422 - 25 Sep 2025
Abstract
This paper presents a neural network–driven framework for solving the inverse problem of initial parameter estimation in air-dropped package missions. Unlike traditional analytical methods, which are computationally intensive and often impractical in real time, the proposed system leverages the flexibility of multilayer perceptrons [...] Read more.
This paper presents a neural network–driven framework for solving the inverse problem of initial parameter estimation in air-dropped package missions. Unlike traditional analytical methods, which are computationally intensive and often impractical in real time, the proposed system leverages the flexibility of multilayer perceptrons to model both forward and inverse relationships between drop conditions and flight outcomes. In the forward stage, a trained network predicts range, flight time, and impact velocity from predefined release parameters. In the inverse stage, a deeper neural model reconstructs the required release velocity, angle, and altitude directly from the desired operational outcomes. By employing a hybrid workflow—combining physics-based simulation with neural approximation—our approach generates large, high-quality datasets at low computational cost. Results demonstrate that the inverse network achieves high accuracy across deterministic and stochastic tests, with minimal error when operating within the training domain. The study confirms the suitability of neural architectures for tackling complex, nonlinear identification tasks in precision airdrop operations. Beyond their technical efficiency, such models enable agile, GPS-independent mission planning, offering a reliable and low-cost decision support tool for humanitarian aid, scientific research, and defense logistics. This work highlights how artificial intelligence can transform conventional trajectory design into a fast, adaptive, and autonomous capability. Full article
(This article belongs to the Special Issue Application of Neural Computation in Artificial Intelligence)
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19 pages, 7670 KB  
Article
A CMOS Hybrid System for Non-Invasive Hemoglobin and Oxygen Saturation Monitoring with Super Wavelength Infrared Light Emitting Diodes
by Hyunjin Park, Seoyeon Kang, Jiwon Kim, Jeena Lee, Somi Park and Sung-Min Park
Micromachines 2025, 16(10), 1086; https://doi.org/10.3390/mi16101086 - 25 Sep 2025
Abstract
This paper presents a CMOS-based hybrid system capable of noninvasively quantifying the total hemoglobin (tHb), the oxygen saturation (SpO2), and the heart rate (HR) by utilizing five-wavelength (670, 770, 810, 850, and 950 nm) photoplethysmography. Conventional pulse oximeters are limited to [...] Read more.
This paper presents a CMOS-based hybrid system capable of noninvasively quantifying the total hemoglobin (tHb), the oxygen saturation (SpO2), and the heart rate (HR) by utilizing five-wavelength (670, 770, 810, 850, and 950 nm) photoplethysmography. Conventional pulse oximeters are limited to the measurements of SpO2 and heart rate, therefore hindering the real-time estimation of tHb that is clinically essential for monitoring anemia, chronic diseases, and postoperative recovery. Therefore, the proposed hybrid system enables us to distinguish between the concentrations of oxygenated (HbO2) and deoxygenated hemoglobin (Hb) by using the absorption characteristics of five wavelengths from the visible to near-infrared range. This CMOS hybrid mixed-signal architecture includes a light emitting diode (LED) driver as a transmitter and an optoelectronic receiver with on-chip avalanche photodiodes, followed by a field-programmable gate array (FPGA) for a real-time signal processing pipeline. The proposed hybrid system, validated through post-layout simulations and algorithmic verification, achieves high precision with ±0.3 g/dL accuracy for tHb and ±1.5% for SpO2, while the heart rate is extracted via 1024-point Fast Fourier Transform (FFT) with an error below ±0.2%. These results demonstrate the potential of a CMOS-based hybrid system as a feasible solution to achieve real-time, low-power, and high-accuracy analysis of bio-signals for clinical and home-use applications. Full article
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29 pages, 3798 KB  
Article
Hybrid Adaptive MPC with Edge AI for 6-DoF Industrial Robotic Manipulators
by Claudio Urrea
Mathematics 2025, 13(19), 3066; https://doi.org/10.3390/math13193066 - 24 Sep 2025
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Abstract
Autonomous robotic manipulators in industrial environments face significant challenges, including time-varying payloads, multi-source disturbances, and real-time computational constraints. Traditional model predictive control frameworks degrade by over 40% under model uncertainties, while conventional adaptive techniques exhibit convergence times incompatible with industrial cycles. This work [...] Read more.
Autonomous robotic manipulators in industrial environments face significant challenges, including time-varying payloads, multi-source disturbances, and real-time computational constraints. Traditional model predictive control frameworks degrade by over 40% under model uncertainties, while conventional adaptive techniques exhibit convergence times incompatible with industrial cycles. This work presents a hybrid adaptive model predictive control framework integrating edge artificial intelligence with dual-stage parameter estimation for 6-DoF industrial manipulators. The approach combines recursive least squares with a resource-optimized neural network (three layers, 32 neurons, <500 KB memory) designed for industrial edge deployment. The system employs innovation-based adaptive forgetting factors, providing exponential convergence with mathematically proven Lyapunov-based stability guarantees. Simulation validation using the Fanuc CR-7iA/L manipulator demonstrates superior performance across demanding scenarios, including precision laser cutting and obstacle avoidance. Results show 52% trajectory tracking RMSE reduction (0.022 m to 0.012 m) under 20% payload variations compared to standard MPC, while achieving sub-5 ms edge inference latency with 99.2% reliability. The hybrid estimator achieves 65% faster parameter convergence than classical RLS, with 18% energy efficiency improvement. Statistical significance is confirmed through ANOVA (F = 24.7, p < 0.001) with large effect sizes (Cohen’s d > 1.2). This performance surpasses recent adaptive control methods while maintaining proven stability guarantees. Hardware validation under realistic industrial conditions remains necessary to confirm practical applicability. Full article
(This article belongs to the Special Issue Computation, Modeling and Algorithms for Control Systems)
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