Search Results (2,420)

The integration of renewable energy sources (RESs) into modern power systems has introduced significant challenges in maintaining system stability and reliability. Among these challenges, load frequency control (LFC) has become a vital area of research. The variable nature of RESs, such as wind and solar, along with their intermittent availability, necessitates advanced management systems for effective frequency regulation. LFC plays a crucial role in ensuring the stability and performance of electrical power systems by managing frequency through the balance of supply and demand, accounting for variations in load, generation, and other disturbances within the system. In traditional power systems, LFC is achieved through a combination of primary, secondary, and tertiary control mechanisms. However, the advent of smart grids has considerably complicated and enhanced the potential for LFC. In these smart grids, which leverage digital communication, sensors, and automation technologies, LFC becomes more intricate and adaptable. These systems not only utilize traditional centralized control but also incorporate RESs, decentralized resources, energy storage solutions, and real-time data to improve frequency management. This research methodically evaluates current LFC techniques using a hierarchical control and technology-focused framework, classifying approaches as conventional, intelligent, and hybrid control schemes within centralized and decentralized system architectures. An evaluative analysis reveals that while intelligent and hybrid control strategies markedly enhance dynamic frequency response and robustness with substantial renewable energy source (RES) integration, persistent challenges remain regarding controller coordination, scalability, computational requirements, and real-time execution. The analysis highlights adaptive hybrid intelligent control schemes, namely those that combine data-driven learning with physical system models, as the most promising avenue for future research, particularly in low-inertia and highly dispersed smart grid scenarios. Full article

With the proliferation of Internet of Things (IoT) deployments and mobile sensing systems, reversible data hiding in encrypted images (RDHEI) has emerged as a cornerstone technology for secure cloud-based sensor data management. RDHEI ensures data confidentiality while enabling bit-to-bit restoration of original visual assets. However, conventional RDHEI methods often struggle to optimize the trade-off between high embedding capacity (EC) and the fidelity requirements of sensor-acquired content. This paper proposes an advanced RDHEI framework based on Adaptive Predicted Value Computation and Pixel Classification (APVCPC). The core contribution is a context-aware prediction engine that adaptively selects optimal estimation functions based on local texture complexity, significantly enhancing prediction accuracy in heterogeneous image regions. Subsequently, a content-driven pixel classification paradigm categorizes pixels into loadable (Lpxls) and non-loadable (NLpxls) sets using a dynamic threshold, maximizing the utilization of spatial redundancy. The proposed scheme further supports separable data extraction and image decryption, providing flexible access control for diverse user privileges in secure sensing scenarios. Experimental results on standard benchmarks and the BOW-2 database demonstrate that APVCPC achieves a superior average embedding rate exceeding 2.0 bpp and ensures perfect reversibility, significantly outperforming state-of-the-art techniques in terms of both capacity and security. Full article

The increasing penetration of rooftop photovoltaic (PV) systems has introduced significant challenges to voltage regulation and power quality within low voltage (LV) distribution networks. Reverse power flows during periods of high solar generation and low local demand can lead to overvoltage issues, voltage unbalance, and increased neutral-to-ground potential. This paper presents a comprehensive review of voltage regulation challenges and mitigation strategies for PV-rich distribution networks. The review consolidates findings from recent literature, focusing on traditional methods such as on-load tap changers and reactive power compensation, as well as modern techniques including smart inverter functionalities, community energy storage, static compensators, and advanced coordinated control schemes. A detailed examination of the suitability and limitations of these approaches in the Australian regulatory and network context is provided. The literature review demonstrates that previous work has mainly considered generic LV regulation issues without explicit four-wire MEN modelling or detailed LV–MV time series impact analysis. As a response to the lack of detailed practical analysis, a detailed three-phase four-wire LV–MV modelling and case study analysis, which illustrates the technical implications of high PV penetration on a representative Australian LV feeder, has been completed. The network is modelled using a three-phase four-wire unbalanced load flow formulation, explicitly incorporating the neutral conductor and multiple earthed neutral (MEN) system configuration. Results demonstrate pronounced voltage rise and unbalance during midday generation periods, highlighting the need for distributed and adaptive voltage-management solutions. The paper concludes by identifying key research gaps and future directions for voltage regulation in Australian distribution networks, emphasizing the importance of low voltage visibility, coordinated control architectures, and the integration of emerging distributed energy resources. The novelty of this work lies in combining a focused review of state-of-the-art with respect to management of voltage regulation in the presence of high penetration of distributed PV generation with a detailed three-phase four-wire LV–MV modelling framework and time-series case study of a representative Australian residential feeder, which illustrates the practical implications of increasing PV penetration. Full article

Bidirectional Interleaved Converters (BICs) are widely used in Battery Energy Storage Systems (BESSs) due to their modular structure, high efficiency, and reduced current ripple. However, under partial-load operation, conventional control strategies with fixed or purely current-based phase shedding repeatedly activate the same converter branches, resulting in increased switching losses, thermal imbalance, and uneven aging of power semiconductors. This paper proposes a temperature-adaptive control strategy for BICs aimed at improving light-load efficiency while actively balancing thermal stress between converter branches. The approach combines a current-adaptive phase-shedding algorithm with a temperature-based branch rotation mechanism, where real-time transistor junction temperature is used as the primary decision variable for branch activation and deactivation. An electro-thermal real-time simulation model of a two-branch BIC is developed using the Controller Hardware-in-the-Loop (CHIL) methodology in the Typhoon HIL environment. The proposed control strategy is validated through real-time CHIL experiments in both boost and buck operating modes under representative battery load profiles. The results demonstrate a reduction in average and peak transistor junction temperatures, improved thermal distribution between converter branches, and more uniform branch utilization, while preserving stable current regulation and power flow. The presented method represents a practical extension of conventional phase-shedding techniques and provides an implementation solution for improving efficiency and reliability of BICs in BESS applications. Full article

Accurate real-time pneumatic flow estimation offers a cost-effective alternative to expensive, bulky flow meters, yet persistent challenges stem from complex valve environments, high nonlinearity, and stringent precision requirements. This paper introduces a novel control framework integrating fuzzy PID dynamic tuning with adaptive wavelet threshold denoising, synergistically optimizing fuzzy PID and improved wavelet transform (IWT) to simultaneously enhance control accuracy and signal quality. Experimental validation demonstrates a 35% reduction in spool displacement overshoot versus conventional PID control. IWT integration improves flow estimation signal-to-noise ratio (SNR) by 65% relative to hard/soft thresholding methods while reducing root mean square error (RMSE) by 49%. The approach significantly outperforms mainstream techniques in dynamic response and noise immunity, enabling precise proportional valve flow measurement. This algorithm-driven strategy replaces high-cost sensors, reducing industrial maintenance requirements. Especially applicable to electromagnetic pneumatic systems in harsh environments, it establishes a reliable framework for proportional valve flow control. Full article

In the era of Industry 4.0, artificial intelligence technology is experiencing rapid development, and the integration of artificial intelligence (AI) with flexible sensors has emerged as a transformative approach for human motion and posture sensing. This paper explores the advancements in AI-enhanced flexible sensors, focusing on the application of flexible sensors on various parts of the human body. Flexible sensors, due to their conformability and sensitivity, are ideal for capturing the dynamic and subtle movements of the human body. AI algorithms, particularly machine learning and deep learning techniques are employed to process the complex data streams from these sensors, enabling the accurate recognition and prediction of various human postures and motions. The combination of these technologies overcomes the limitations of traditional sensing systems, offering higher precision, adaptability, and real-time feedback. It can be applied to healthcare for rehabilitation monitoring, sports for performance enhancement, and human–computer interaction for intuitive control. This review also discusses the challenges such as sensor reliability, data privacy, and power management. The future outlook emphasizes more sophisticated AI models and deeper technology integration, promising a seamless integration into everyday life for enhanced human–machine interaction and health monitoring. Full article

Grid-forming inverters (GFIs) are emerging as a key enabling technology for maintaining stability in renewable-dominated power systems, where conventional synchronous generation is progressively displaced by inverter-based resources. This paper presents a comprehensive technical review of GFI control strategies applied to photovoltaic (PV) systems, with focused attention on small-signal stability, transient dynamic performance, and overcurrent-limiting capabilities. In contrast to grid-following inverters (GFLIs), which rely on phase-locked-loop synchronization, GFIs operate as voltage sources capable of forming and regulating grid voltage and frequency. The reviewed control approaches, including droop control, virtual synchronous generator (VSG), synchronverter, matching control, virtual oscillator control (VOC), model predictive control (MPC), and intelligent techniques such as fuzzy logic control (FLC), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFISs), are systematically compared based on dynamic response characteristics, robustness under weak-grid conditions, control complexity, and practical implementation challenges. The paper synthesizes recent findings on stability margins, inertia emulation, transient current response, and protection requirements, highlighting remaining research gaps related to large-disturbance ride-through capability, coordination of multiple GFIs, and protection integration. These insights aim to support future deployments of reliable grid-forming photovoltaic systems in resilient inverter-dominated power networks. Full article

Scanning electron microscopes (SEMs) are crucial for material characterization. They are highly susceptible to vibration from environmental sources, internal components, and other external factors, which can impair measurement accuracy. Traditional solutions are limited in addressing multi-source vibrations: passive isolation struggles with internal vibrations, while image post-processing cannot fundamentally correct large-amplitude deviations in the electron beam. Therefore, this study proposes a hybrid framework that combines real-time active hardware suppression with post-processing to mitigate vibration-induced distortion in SEM images. Using a self-developed external controller and software, the framework extracts periodic vibration features via FFT, quantifies scan line horizontal offset, and implements real-time inverse offset during imaging to suppress dominant-frequency vibration at the source. An adaptive median filtering algorithm is integrated with a Laplacian edge enhancement algorithm to address residual edge burrs, thereby balancing distortion suppression and detail preservation. Experiments at 100 kx magnifications demonstrate notable correction effects: the peak-to-peak value, edge transition width (ETW), and no-reference image quality (NIQE) score are reduced by 39.4%, 91.7%, and 58.9%, respectively. Consistent correction trends are observed at 50 kx, with periodic vibration distortion essentially eliminated across both magnifications. Furthermore, distortion can be regulated through the phase interaction between dwell time and vibration period, making the strategy universally applicable and easy to implement. Without the need for vibration source localization, the framework is compatible with various types of vibration interference. It provides a solution for mitigating vibration impacts in high-magnification, precise characterization of SEMs and offers a reference for anti-vibration optimization of other microscopic techniques, such as transmission electron microscopy (TEM) and atomic force microscopy (AFM). Full article

The recent development of 3D generative AI encompassing generation and editing technologies has been increasingly investigated to advance immersive applications. To enrich visual aesthetics, 3D stylization techniques focus on transferring artistic effects from reference style images to 3D scenes. However, existing 3D stylization techniques primarily focus on global style transfer, which can result in unwanted modifications to background regions and a lack of localized control. To address these limitations, we propose LocalGaussStyle, a novel approach for localized style transfer on scenes represented by 3D Gaussian splatting. The proposed pipeline consists of two phases: object localization and localized stylization. First, 2D instance segmentation masks are projected into a 3D scene to precisely localize target objects. Next, a boundary-aware optimization is designed to perform style transfer and mitigate style leakage caused by the spatial overlap of Gaussians. In addition, geometry-decoupled adaptive densification (GDAD) is employed to enhance the geometric resolution of Gaussians within the target object, thereby improving the representation capacity. The LocalGaussStyle facilitates high-fidelity style transfer that preserves the geometry and appearance of the non-target regions. In terms of style fidelity and background preservation, the effectiveness and efficiency of the proposed method are demonstrated through extensive experiments conducted on various scenes and reference style images. Full article

This study evaluates the effectiveness of natural zeolite (Shankhanai deposit, Kazakhstan) as a functional hydroponic substrate compared to a commercial foamed-glass control (GrowPlant). Using the Nutrient Film Technique (NFT), we assessed the growth and metabolic responses of Medicago sativa L. and three cultivars of Lactuca sativa L. Brunauer–Emmett–Teller (BET) analysis confirmed that zeolite (particle size 3.70 ± 1.20 mm) possesses a high specific surface area (21.80 m²/g), significantly exceeding the control (0.49 m²/g). This structure ensured superior moisture retention and cation exchange, even after a moderate decrease in surface area to 16.66 m²/g post-cultivation due to organic pore-filling. In M. sativa experiments, zeolite increased seedling viability and promoted a more branched root system compared to the artificial substrate. Gas chromatography–mass spectrometry (GC–MS) metabolic profiling of L. sativa revealed a significant substrate-driven reprogramming: zeolite increased the relative proportion of fatty acids and their derivatives (up to +51.27% in May King variety roots), suggesting membrane-protective adaptation. Genotype-specific responses were observed, with the Yeralash cultivar showing increased polyol synthesis (+2.93%) for osmoregulation. The results demonstrate that natural zeolite is an efficient, stable substrate for intensive hydroponics, optimizing root development and physiological stability through enhanced nutrient and water management. Full article

The increasing adoption of data-driven techniques in cybersecurity has introduced new opportunities to enhance detection, response, and automation capabilities within the cybersecurity ecosystem; however, cybersecurity auditing remains constrained by traditional compliance-oriented approaches that rely profoundly on binary, checklist-based evaluations. Such approaches often reinforce a policing or “sheriff-style” perception of auditing, emphasizing enforcement rather than enablement, risk insight, and organizational improvement. Of primary concern is that the “sheriff-style” cybersecurity audit approach often fails to accurately portray the true state of an organization’s cybersecurity posture, often providing a misleading sense of assurance based solely on formal compliance and controls existence. This study proposes an Anti-Sheriff Cybersecurity Audit Model, that moves beyond cybersecurity control checklists, by integrating intelligence-informed risk assessments with structured human judgment to support a more robust, adaptive, and risk-oriented auditing process. Grounded in design science research (DSR), the proposed approach combines conventional binary compliance verification with intelligence-derived risk indicators and governance-based maturity assessments to evaluate cybersecurity controls across technical, operational, and organizational dimensions. The approach aligns with established standards and frameworks, including International Organization for Standardization and the International Electrotechnical Commission (ISO/IEC) 27001, the National Institute of Standards and Technology (NIST), and the Center for Internet Security (CIS) benchmarks, while extending their application beyond static compliance validation. A fictional case study is used to demonstrate the model’s applicability and to illustrate how hybrid scoring can reveal residual risk not captured by conventional cybersecurity audits. The findings indicate that combining intelligence-informed analytics with structured human judgment enhances audit depth, interpretability, and business relevance. The proposed approach, therefore, provides a foundation for evolving cybersecurity auditing from just periodic compliance assessments, toward a continuous, risk-informed, and governance-aligned assurance system. Full article

The Taylor approximation theorem is a fundamental tool in numerical analysis, providing a local polynomial representation of smooth functions. In practical computations, a function f is approximated by a finite Taylor polynomial $P_n$, and controlling the resulting truncation error is of central importance. In this paper, we introduce two novel a posteriori error estimation techniques for Taylor polynomial approximations. The proposed estimators are fully computable and do not require prior bounds on the $(n+1)$st derivatives of f. We prove that the estimators converge to the exact error both pointwise and in the $L^2$-norm as $n\to \infty$, and we establish their asymptotic sharpness through effectivity analysis. Based on these results, we develop two adaptive algorithms that automatically determine the minimal degree n required to achieve a prescribed tolerance, either at a specific point or over a domain. We further extend the analysis to multivariate functions and show that analogous estimators and effectivity properties hold in higher dimensions. Numerical experiments are presented to validate the theoretical results and demonstrate the practical performance of the proposed methods. Full article

The rapid evolution of Artificial Intelligence (AI) has significantly transformed the capabilities, performance, and autonomy of modern mechatronic systems. As industries transition toward intelligent and interconnected manufacturing environments, AI has emerged as a powerful enabler of real-time decision-making, adaptive control, predictive maintenance, and autonomous operation. This review provides a comprehensive analysis of AI integration within mechatronic systems, examining its influence on system performance, autonomy, and manufacturing efficiency. Key AI techniques including machine learning, deep learning, reinforcement learning, evolutionary optimization, and computer vision are evaluated in terms of their applications in control, sensing, diagnostics, and robotics. The paper also highlights advancements in AI-driven motion control, autonomous navigation, sensor fusion, and smart factory operations. Critical challenges such as data requirements, computational constraints, system interoperability, and safety concerns are discussed to identify research gaps. Finally, emerging trends and future directions, such as edge AI, digital twins, explainable AI, and fully autonomous mechatronic cells, are explored. This review consolidates current knowledge and provides insights to guide researchers and practitioners in developing next-generation intelligent mechatronic systems capable of supporting the demands of Industry 4.0 and beyond. Full article

The paper presents a novel method for automated image processing that combines pyramidal generalized contour preprocessing with parallel–hierarchical segmentation, integrating adaptive multilevel thresholding to enhance segmentation accuracy and robustness. The proposed approach is designed to overcome the limitations of traditional methods—whose performance declines under variations in brightness, surface texture, and noise—by enhancing image contrast and structural defect detection, thereby reducing diagnostic errors and misclassification risks. To achieve these objectives, the implementation utilizes multilevel adaptive thresholding, enabling step-by-step segmentation refinement and the extraction of informative regions using three-level coding (positive, negative, and neutral elements). In conjunction with parallel–hierarchical (PH) transformations and high-frequency filtering, the method enhances image contrast, enables more accurate detection of structural defects, and reduces the number of false positives. Experimental results demonstrate a 10–15% improvement in segmentation accuracy compared to classical methods such as region-growing techniques. Furthermore, correlation analysis between automatic and manual segmentation results demonstrated a high degree of consistency, with a correlation coefficient of 0.95–0.99, indicating the reliability and reproducibility of the developed approach. The proposed method is distinguished by its high processing speed, computational simplicity, and versatility of application, ranging from medical thermography for pathological diagnostics to real-time monitoring of railway infrastructure. The practical significance of these results lies in advancing automation, reducing decision-making errors, and ensuring greater reliability of technical and medical control systems. Full article

The large-scale integration of photovoltaic systems into modern distribution networks requires advanced forecasting and optimisation tools to address variability, uncertainty, and increasingly complex operational conditions. This review examines 160 peer-reviewed studies published primarily between 2018 and 2026 and provides a unified, system-level perspective that links photovoltaic power forecasting, photovoltaic optimisation, and energy storage system management within the broader context of Smart Grid operation. The analysis covers forecasting techniques across all temporal horizons, compares deterministic, stochastic, metaheuristic, and hybrid optimisation approaches, and reviews siting, sizing, and operational strategies for both PV units and Energy Storage Systems, including their effects on hosting capacity, reactive power control, and network flexibility. A key contribution of this work is the consolidation of planning- and operation-oriented methods into a coherent framework that clarifies how forecasting accuracy influences Distributed Energy Resources optimisation and system-level performance. The review also highlights emerging trends, such as reinforcement learning for real-time Energy Storage Systems control, surrogate-assisted multi-objective optimisation, data-driven hosting capacity evaluation, and explainable AI for grid transparency, as essential enablers for flexible, resilient, and sustainable distribution networks. Open challenges include uncertainty modelling, real-world validation of optimisation tools, interoperability with flexibility markets, and the development of scalable and adaptive optimisation frameworks for next-generation smart grids. Full article