Search Results (1,705)

In terms of their high efficiency and low environmental impact, proton exchange membrane fuel cells (PEMFC) are becoming increasingly essential in the development of hydrogen electric vehicles. Despite these advantages, optimizing hydrogen consumption remains difficult because of the highly nonlinear behavior of PEMFC systems and their sensitivity to variations in operating conditions. This article outlines an intelligent control approach based on extremum seeking control (ESC), based on an artificial neural network (ANN) model, to improve hydrogen utilization in hydrogen electric vehicles. Experimental data on current, voltage, and temperature are collected, preprocessed, and used to train the ANN model of the PEMFC. The ESC algorithm uses this predictive ANN model to adjust the fuel cell current in real time, ensuring voltage stability while reducing hydrogen consumption. The simulation results demonstrate that the ANN-based ESC system provides voltage stability under dynamic load variations and achieves approximately 2.7% hydrogen savings without affecting the experimental current profile, validating the efficacy of the suggested strategy for effective hydrogen management in fuel cell electric vehicles. Full article

Intelligent driving cabins operated by artificial intelligence technology are evolving into the third living space. They aim to integrate perception, analysis, decision making, and intervention. By using multimodal biosignal acquisition technologies (flexible sensors and non-contact sensing), it is possible to monitor the physiological indicators of heart rate and blood pressure in real time. Leveraging the benefits of domain controllers in the vehicle and edge computing helps the AI platform reduce data latency and enhance real-time processing capabilities, as well as integrate the cabin’s internal and external data through machine learning. Its aim is to build tailored health baselines and high-precision risk prediction models (e.g., CNN, LSTM). This system can initiate multi-level interventions such as adjustments to the environment, health recommendations, and ADAS-assisted emergency parking with telemedicine help. Current issues consist of sensor precision, AI model interpretation, security of data privacy, and whom to attribute legal liability to. Future development will mainly focus on cognitive digital twin construction, L4/L5 autonomous driving integration, new biomedical sensor applications, and smart city medical ecosystems. Full article

This review examines the impact of data analytics powered by the Internet of Things (IoT), edge computing, and artificial intelligence (AI) on improving energy efficiency in smart environments, with a focus on smart factories, smart cities, and smart territories. Advanced AI, machine learning (ML), and deep learning (DL) techniques enable real-time energy optimization and intelligent decision-making in complex, data-intensive systems. Integrating edge computing reduces latency and improves responsiveness in IoT and Industrial Internet of Things (IIoT) networks, enabling local energy management and reducing grid load. Federated learning further enhances data privacy and efficiency by enabling decentralized model training across distributed smart nodes without exposing sensitive information or personal data. Emerging 5G and 6G technologies provide the necessary bandwidth and speed for seamless data exchange and control across energy-intensive, connected infrastructures. Blockchain increases transparency, security, and trust in energy transactions and decentralized energy trading in smart grids. Together, these technologies support dynamic demand response mechanisms, predictive maintenance, and self-regulating systems, leading to significant improvements in energy sustainability. Case studies of smart cities and industrial ecosystems within Industry 4.0/5.0/6.0 demonstrate measurable reductions in energy consumption and carbon emissions through these synergistic approaches. Despite significant progress, challenges remain in interoperability, scalability, and regulatory frameworks. This review demonstrates that AI-based edge computing, supported by robust connectivity and secure IoT and IIoT architectures, has a transformative potential for creating energy-efficient and sustainable smart environments. Full article

Flywheel energy storage systems play an important role in frequency regulation and power quality control within modern power grids, yet the fault signals generated by defects in their rolling bearings are typically indistinct, making direct diagnosis difficult. Raw noisy signals often yield unsatisfactory diagnostic performance when directly processed by neural networks. Although MOMEDA (Multipoint Optimal Minimum Entropy Deconvolution Adjusted) can effectively extract impulsive fault components, its performance is highly dependent on the selected fault period and filter length. To address these issues, this paper proposes an improved fault diagnosis method that integrates MOMEDA-based periodic extraction with a neural network classifier. The Artificial Fish Swarm Algorithm (AFSA) is employed to adaptively determine the key parameters of MOMEDA using multi-point kurtosis as the optimization objective, and the optimized parameters are used to enhance impulsive fault features. The filtered signals are then converted into image representations and fed into a ResNet-18 network (a compact 18-layer deep convolutional neural network from the residual network family) to achieve intelligent identification and classification of bearing faults. Experimental results demonstrate that the proposed method can effectively extract and diagnose bearing fault signals. Full article

Digital Twins (DTs) integrated with Artificial Intelligence (AI) are emerging as transformative tools in smart manufacturing. By bridging the physical and virtual domains, DTs enable real-time monitoring, predictive analytics, and autonomous decision-making. Originally conceived as advanced simulation models, DTs have evolved significantly with the incorporation of AI, which enhances their ability to acquire process knowledge, optimize scheduling, and autonomously control system variables. This evolution transforms DTs from passive representations into prescriptive, self-optimizing systems. AI-driven DTs support a wide range of applications, including predictive maintenance, process optimization, quality control, and dynamic scheduling, using techniques such as deep reinforcement learning and convolutional neural networks. These capabilities have been successfully deployed across industrial domains such as CNC machining, robotics, and industrial printing, yielding substantial improvements in efficiency, reliability, and responsiveness. Despite these advancements, the full realization of intelligent DTs relies heavily on the availability of high-fidelity, real-time data and a seamless alignment between physical systems and their digital counterparts. This literature survey provides a state-of-the-art review of AI-driven DTs in manufacturing, highlighting their key applications, challenges, and emerging research directions that will shape the future of intelligent and adaptive manufacturing systems. To present a structured perspective on the evolution and scalability of AI-driven DTs, the application case studies are organized according to four integration levels—machine, cell, shop floor, and enterprise—highlighting how these technologies scale from individual assets to fully interconnected manufacturing ecosystems. Full article

Microfluidic cell culture systems and organ-on-a-chip platforms provide powerful tools for modeling physiological processes, disease progression, and drug responses under controlled microenvironmental conditions. These technologies rely on diverse cell culture methodologies, including 2D and 3D culture formats, spheroids, scaffold-based systems, hydrogels, and organoid models, to recapitulate tissue-level functions and generate rich, multiparametric datasets through high-resolution imaging, integrated sensors, and biochemical assays. The heterogeneity and volume of these data introduce substantial challenges in pre-processing, feature extraction, multimodal integration, and biological interpretation. Artificial intelligence (AI), particularly machine learning and deep learning, offers solutions to these analytical bottlenecks by enabling automated phenotyping, predictive modeling, and real-time control of microfluidic environments. Recent advances also highlight the importance of technical frameworks such as dimensionality reduction, explainable feature selection, spectral pre-processing, lightweight on-chip inference models, and privacy-preserving approaches that support robust and deployable AI–microfluidic workflows. AI-enabled microfluidic and organ-on-a-chip systems now span a broad application spectrum, including cancer biology, drug screening, toxicity testing, microbial and environmental monitoring, pathogen detection, angiogenesis studies, nerve-on-a-chip models, and exosome-based diagnostics. These platforms also hold increasing potential for precision medicine, where AI can support individualized therapeutic prediction using patient-derived cells and organoids. As the field moves toward more interpretable and autonomous systems, explainable AI will be essential for ensuring transparency, regulatory acceptance, and biological insight. Recent AI-enabled applications in cancer modeling, drug screening, etc., highlight how deep learning can enable precise detection of phenotypic shifts, classify therapeutic responses with high accuracy, and support closed-loop regulation of microfluidic environments. These studies demonstrate that AI can transform microfluidic systems from static culture platforms into adaptive, data-driven experimental tools capable of enhancing assay reproducibility, accelerating drug discovery, and supporting personalized therapeutic decision-making. This narrative review synthesizes current progress, technical challenges, and future opportunities at the intersection of AI, microfluidic cell culture platforms, and advanced organ-on-a-chip systems, highlighting their emerging role in precision health and next-generation biomedical research. Full article

Many complex systems requires the use of controllers to allow an automated, self-adaptive management of components and resources. Controllers are software components that observe a system, analyse its quality, and recommend and enact decisions to maintain or improve quality. While controllers have been for many years, recently Artificial Intelligence (AI) techniques such as Machine Learning (ML) and specifically reinforcement learning (RL) are used to construct these controllers, causing uncertainties about the quality of them due to their construction. We investigate quality metrics for RL-constructed software-based controllers that allow for their continuous quality control, which is particularly motivated by increasing automation and also the usage of artificial intelligence and control theoretic solutions for controller construction and operation. We introduce self-adaptation and control principles and define a quality-oriented controller reference architecture for controllers for self-adaptive systems. This forms the basis for the central contribution, a quality analysis metrics framework for controllers themselves. Full article

With the advancement of intelligent technologies, industrial production systems are being profoundly transformed by artificial intelligence algorithms. To address persistent challenges, such as cargo swing and low operational efficiency during the lifting processes of all-terrain cranes, this research investigates an intelligent control algorithm designed for swing suppression and high-stability payload trajectory control. Firstly, a nonlinear dynamic model of the crane system was derived using the Euler–Lagrange formulation based on a simplified three-dimensional representation. A linear time-varying model predictive control (LTV-MPC) strategy was then designed to incorporate real-time feedback during luffing and slewing motions to monitor the payload’s swing state. On this basis, the controller predicts the desired trajectory and applies negative feedback to adjust the control input, thereby steering the system toward the optimal trajectory and aligning it with the target path. Secondly, a comparative analysis was conducted among four scenarios: the natural swing state of the payload and three control strategies—LTV-MPC, sliding mode control (SMC), and PID control—under both single-input and dual-input conditions. Finally, an experimental platform was established, employing the YOLOv12 algorithm for real-time detection and trajectory tracking of the suspended payload. The experimental results validate the effectiveness of LTV-MPC in suppressing cargo swing. Under single-input control, LTV-MPC achieved the best performance in both stabilization time (3.05 s for luffing condition one and 1.15 s for luffing condition two) and steady-state error (0.003–0.007°). The swing angle, θ₁, was reduced by 91.9%, 54.2%, and 59.3% compared to the natural swing state, SMC, and PID, respectively. In dual-input control, LTV-MPC attained a steady-state error of only 0.0008° under “luffing condition two,” while during slewing operations, it also outperformed SMC and PID in both settling time (26.05 s) and precision (0.008°). Full article

The rapid evolution of modern vehicles into intelligent and interconnected systems presents new complexities in both functional safety and cybersecurity. In this context, ensuring the reliability and integrity of critical sensor data, such as wheel speed inputs for anti-lock brake systems (ABS), is essential. Effective detection of wheel speed sensor faults not only improves functional safety, but also plays a vital role in keeping system resilience against potential cyber–physical threats. Although data-driven approaches have gained popularity for system development due to their ability to extract meaningful patterns from historical data, a major limitation is the lack of diverse and representative faulty datasets. This study proposes a novel dual learning model, based on Temporal Convolutional Networks (TCN), designed to accurately distinguish between normal and faulty wheel speed sensor behavior within a hardware-in-the-loop (HIL) simulation platform implemented on an FPGA. To address dataset limitations, a TruckSim–MATLAB/Simulink co-simulation environment is used to generate realistic datasets under normal operation and eight representative fault scenarios, yielding up to 5000 labeled sequences (balanced between normal and faulty behaviors) at a sampling rate of 60 Hz. Two TCN models are trained independently to learn normal and faulty dynamics, and fault decisions are made by comparing the reconstruction errors (MSE and MAE) of both models, thus avoiding manually tuned thresholds. On a test set of 1000 sequences (500 normal and 500 faulty) from the 5000 sample configuration, the proposed dual TCN framework achieves a detection accuracy of 97.8%, a precision of 96.5%, a recall of 98.2%, and an F1-score of 97.3%, outperforming a single TCN baseline, which achieves 91.4% accuracy and an 88.9% F1-score. The complete dual TCN architecture is implemented on a Xilinx ZCU102 FPGA evaluation kit (AMD, Santa Clara, CA, USA), while supporting real-time inference in the HIL loop. These results demonstrate that the proposed approach provides accurate, low-latency fault detection suitable for safety-critical ABS applications and contributes to improving both functional safety and cyber-resilience of braking systems. Full article

Temperature-responsive hydrogels are sophisticated stimuli-responsive biomaterials that undergo rapid, reversible sol–gel phase transitions in response to subtle thermal stimuli, most notably around physiological temperature. This inherent thermosensitivity enables non-invasive, precise spatiotemporal control of material properties and bioactive payload release, rendering them highly promising for advanced biomedical applications. This review critically surveys recent advances in the design, synthesis, and translational potential of thermo-responsive hydrogels, emphasizing nanoscale and hybrid architectures optimized for superior tunability and biological performance. Foundational systems remain dominated by poly(N-isopropylacrylamide) (PNIPAAm), which exhibits a sharp lower critical solution temperature near 32 °C, alongside Pluronic/Poloxamer triblock copolymers and thermosensitive cellulose derivatives. Contemporary developments increasingly exploit biohybrid and nanocomposite strategies that incorporate natural polymers such as chitosan, gelatin, or hyaluronic acid with synthetic thermo-responsive segments, yielding materials with markedly enhanced mechanical robustness, biocompatibility, and physiologically relevant transition behavior. Cross-linking methodologies—encompassing covalent chemical approaches, dynamic physical interactions, and radiation-induced polymerization are rigorously assessed for their effects on network topology, swelling/deswelling kinetics, pore structure, and degradation characteristics. Prominent applications include on-demand drug and gene delivery, injectable in situ gelling systems, three-dimensional matrices for cell encapsulation and organoid culture, tissue engineering scaffolds, self-healing wound dressings, and responsive biosensing platforms. The integration of multi-stimuli orthogonality, nanotechnology, and artificial intelligence-guided materials discovery is anticipated to deliver fully programmable, patient-specific hydrogels, establishing them as pivotal enabling technologies in precision and regenerative medicine. Full article

This Editorial presents an integrative overview of the Special Issue “Advanced Autonomous Systems and Artificial Intelligence Stage”, which assembles fifteen peer-reviewed articles dedicated to the recent evolution of AI-enabled and autonomous systems. The contributions span a broad spectrum of domains, including renewable energy and power systems, intelligent transportation, agricultural robotics, clinical and assistive technologies, mobile robotic platforms, and space robotics. Across these diverse applications, the collection highlights core research themes such as robust perception and navigation, semantic and multi modal sensing, resource-efficient embedded inference, human–machine interaction, sustainable infrastructures, and validation frameworks for safety-critical systems. Several articles demonstrate how physical modeling, hybrid control architectures, deep learning, and data-driven methods can be combined to enhance operational robustness, reliability, and autonomy in real-world environments. Other works address challenges related to fall detection, predictive maintenance, teleoperation safety, and the deployment of intelligent systems in large-scale or mission-critical contexts. Overall, this Special Issue offers a consolidated and rigorous academic synthesis of current advances in Autonomous Systems and Artificial Intelligence, providing researchers and practitioners with a valuable reference for understanding emerging trends, practical implementations, and future research directions. Full article

This review provides a comprehensive analysis of advanced control strategies and operational optimization of energy systems, focusing on heat pumps, with an emphasis on their role in enhancing energy efficiency and operational flexibility. The study concentrates on methods supported by artificial intelligence algorithms, highlighting Model Predictive Control (MPC), Reinforcement Learning (RL), and hybrid approaches that combine the advantages of both. These methods aim to optimize both the operation of heat pumps and their interaction with thermal energy storage (TES) systems, renewable energy sources, and power grids, thereby enhancing the flexibility and adaptability of the systems under real operating conditions. Through a systematic analysis of the existing literature, 95 studies published after 2019 were examined to identify research trends, key challenges such as computational requirements and algorithm interpretability, and future opportunities. Furthermore, significant benefits of applying advanced control compared to conventional practices were highlighted, such as reduced operational costs and lower CO₂ emissions, emphasizing the importance of heat pumps in the energy transition. Thus, the analysis highlights the need for digital solutions, robust and adaptive control frameworks, and holistic techno-economic evaluation methods in order to fully exploit the potential of heat pumps and accelerate the transition to sustainable and flexible energy systems. Full article

Based on an enhanced artificial potential field approach, this paper presents a control method for obstacle avoidance in vehicle platoons within Intelligent Vehicle-Infrastructure Cooperative Systems (I-VICS). To enhance safety during maneuvers, an inter-vehicle obstacle avoidance potential field model is established. By integrating virtual forces and a consistency control strategy into the control law, the proposed method effectively handles obstacle avoidance for vehicles operating at large inter-vehicle distances (80–110 m). Experimental validation using real-world trajectory data shows a 34% improvement in trajectory smoothness, as quantified by a proposed Vehicle Trajectory Stability (VTS) metric, leading to significantly safer avoidance maneuvers. A coordinated multi-vehicle obstacle avoidance strategy is further devised using a rotating potential field method, enabling collaborative and safe overall motion planning. Moreover, a path tracking strategy based on virtual force design is introduced to enhance platoon stability and reliability. Future work will focus on collision avoidance for vehicle platoons with varying inter-vehicle distances and will extend the consistency control and cooperative avoidance strategies to longer vehicle platoon to further improve overall traffic safety. Full article

The operational efficiency and precision of boom sprayers, as critical equipment for protecting field crops, are vital to global food security and agricultural sustainability. In precision agriculture systems, achieving uniform pesticide application fundamentally depends on maintaining stable boom posture during operation. However, severe boom vibration not only directly causes issues like missed spraying, double spraying, and pesticide drift but also represents a critical bottleneck constraining its functional realization in cutting-edge applications. Despite its importance, achieving absolute boom stability is a complex task. Its suspension system design faces a fundamental technical contradiction: effectively isolating high-frequency vehicle vibrations caused by ground surfaces while precisely following large-scale, low-frequency slope variations in the field. This paper systematically traces the evolutionary path of self-balancing boom technology in addressing this core contradiction. First, the paper conducts a dynamic analysis of the root causes of boom instability and the mechanism of its detrimental physical effects on spray quality. This serves as a foundation for the subsequent discussion on technical approaches for boom support and balancing systems. The paper also delves into the evolution of sensing technology, from “single-point height measurement” to “point cloud morphology perception,” and provides a detailed analysis of control strategies from classical PID to modern robust control and artificial intelligence methods. Furthermore, this paper explores the deep integration of this technology with precision agriculture applications, such as variable rate application and autonomous navigation. In conclusion, the paper summarizes the main challenges facing current technology and outlines future development trends, aiming to provide a comprehensive reference for research and development in this field. Full article

Cognitive deterioration and the transition to neurodegenerative disease does not develop through simple, linear regression; it develops as rapid and global transitions from one state to another within the neural network. Developing understanding and control over these events is among the largest tasks facing contemporary neuroscience. This paper will discuss a conceptual reframing of cognitive decline as a transitional phase of the functional state of complex neural networks resulting from the intertwining of molecular degradation, vascular dysfunction and systemic disarray. The paper will integrate the latest findings that have demonstrated how the disruptive changes in glymphatic clearance mechanisms, aquaporin-4 polarity, venous output, and neuroimmune signaling increasingly correlate with the neurophysiologic homeostasis landscape, ultimately leading to the destabilization of the network attraction sites of memory, consciousness, and cognitive resilience. Furthermore, the destabilizing processes are exacerbated by epigenetic silencing; neurovascular decoupling; remodeling of the extracellular matrix; and metabolic collapse that result in accelerating the trajectory of neural circuits towards the pathological tipping point of various neurodegenerative diseases including Alzheimer’s disease; Parkinson’s disease; traumatic brain injury; and intracranial hypertension. New paradigms in systems neuroscience (connectomics; network neuroscience; and critical transition theory) provide an intellectual toolkit to describe and predict these state changes at the systems level. With artificial intelligence and machine learning combined with single cell multi-omics; radiogenomic profiling; and digital twin modeling, the predictive biomarkers and early warnings of impending collapse of the system are beginning to emerge. In terms of therapeutic intervention, the possibility of reprogramming the circuitry of the brain into stable attractor states using precision neurointervention (CRISPR-based neural circuit reprogramming; RNA guided modulation of transcription; lineage switching of glia to neurons; and adaptive neuromodulation) represents an opportunity to prevent further progression of neurodegenerative disease. The paper will address the ethical and regulatory implications of this revolutionary technology, e.g., algorithmic transparency; genomic and other structural safety; and equity of access to advanced neurointervention. We do not intend to present a list of the many vertices through which the mechanisms listed above instigate, exacerbate, or maintain the neurodegenerative disease state. Instead, we aim to present a unified model where the phenomena of molecular pathology; circuit behavior; and computational intelligence converge in describing cognitive decline as a translatable change of state, rather than an irreversible succumbing to degeneration. Thus, we provide a framework for precision neurointervention, regenerative brain medicine, and adaptive intervention, to modulate the trajectory of neurodegeneration. Full article