Search Results (4,819)

In this paper, an event-triggered optimal control strategy is proposed for three-phase grid-connected voltage source inverters (VSIs) based on the voltage-modulated direct power control (VM-DPC) principle. The optimization control problem of VSIs is addressed in the framework of nonzero sum (NZS) games to ensure mutual cooperation between active power and reactive power. To achieve optimal performance, the power components are driven to track their desired references while minimizing the individual performance index function. Accurate tracking of active and reactive powers not only stabilizes the grid but also guarantees compliant renewable integration. An adaptive dynamic programming (ADP) approach is adopted, where the critic neural network (NN) approximates the value function and provides optimal control policies. Moreover, an event-triggered mechanism with a dead-zone operation is incorporated to reduce redundant updates, thereby saving computational and communication resources. The stability of the closed-loop system and a strictly positive minimum inter-event interval are guaranteed. Simulation results verify that the proposed method achieves accurate power tracking, improved dynamic performance, and efficient implementation. Full article

Home energy management systems have emerged as a crucial solution for enhancing energy efficiency, reducing carbon emissions, and facilitating the integration of renewable energy sources into homes. To fully realize their potential, these systems’ performance must be optimized, which involves addressing multiple objectives, such as minimizing costs and environmental impact. The Pareto frontier is a tool widely adopted in multi-objective optimization within home energy management systems’ operation, where a range of optimal solutions are produced. This study uses the Pareto curve to optimize the operational performance of home energy management systems, considering the state health of the battery to determine the best answer among the optimal solutions in the curve. The main reason for considering the state of health is the effects of the battery’s operation on the performance of energy systems, especially for long-term optimization outcomes. In this study, the performance of the battery is measured through a physical model named PyBaMM that is tuned based on swarm intelligence techniques, including the Whale Optimization Algorithm, Grey Wolf Optimization, Particle Swarm Optimization, and the Gravitational Search Algorithm. The proposed framework automatically identifies the optimal solution out of the ones in the Pareto curve by comparing the performance of the battery through the tuned physical model. The effectiveness of the proposed algorithm is demonstrated for a home, including four distinct energy carriers along with a 12 V 128 Ah LFP chemistry Li-ion battery module, where the overall cost and carbon emissions are the metrics for comparisons. Implementation results show that tuning the physical model based on the Whale Optimization Algorithm reaches the highest accuracy compared to the other methods. Moreover, considering the state of health of the battery as the selecting criterion will improve home energy management systems’ performance, particularly in long-term operation models, because it guarantees a longer battery lifespan. Full article

The characterization and biomedical modification of bentonite clays from the Kalzhat deposit (Kzh), which is situated in Kazakhstan’s Zhetysu region, are the main objectives of this work. In order to improve the raw material’s structural qualities, the montmorillonite fraction was enriched, and coarse impurities were eliminated using the Salo method. The presence of meso- and micropores that guarantee high dispersity and specific surface area, as well as the prevalence of montmorillonite and kaolinite, was all confirmed by physicochemical analysis. Particle size measurements indicated finely dispersed structures with a propensity to aggregate, whereas thermal analysis demonstrated resilience under heating. After effective functionalization with silver nanoparticles, a porous hybrid system with improved surface reactivity was produced. These enhancements demonstrate the modified bentonite’s usefulness as a multifunctional carrier for the immobilization and controlled release of pharmaceuticals, with potential uses in drug delivery systems, antimicrobial coatings, and wound-healing materials. The material has potential use in sorption and environmental protection technologies in addition to its biomedical application. Overall, Kzh’s structural and functional performance is greatly improved by the combination of purification and functionalization with silver nanoparticles, highlighting its promise as a useful element in the development of next-generation polymer–composite systems. Full article

Pixel value ordering (PVO) is a widely used framework for reversible data hiding (RDH). As the demand for higher embedding capacity continues to grow, achieving a proper balance between capacity and image quality has become increasingly important. In this paper, we propose a novel PVO-based multi-pixel embedding RDH scheme for grayscale images, which improves capacity by embedding multiple bits of data within multiple pixels in each block. A PVO recovery strategy is designed to guarantee reversibility while minimizing image distortion when multiple bits are embedded per block. Moreover, an improved flexible spatial location strategy is introduced, which defines pixel positions within a block using twelve modes. By selecting the optimal mode for each block, the number of expandable prediction errors is increased, further enhancing embedding capacity. In addition, the artificial lemming algorithm (ALA) is employed to optimize embedding parameters, enabling a better balance between capacity and visual quality for a given payload. Experimental results demonstrate that the proposed method achieves significantly improved embedding capacity while maintaining high image quality, offering a well-balanced performance compared to similar PVO-based schemes. Full article

Smart contracts rely on blockchain oracles to access off-chain data, yet existing oracle designs often face challenges such as untrustworthy data sources, weak temporal guarantees, and limited verifiability. This work presents Ivy Oracle, a robust and time-trustworthy data feed framework that enhances the reliability and auditability of off-chain information for smart contracts. Ivy Oracle integrates trusted execution environments (TEEs) for secure data acquisition, an external time server for authenticated timestamps, and a PageRank-based trust model to evaluate source credibility. We implement and evaluate Ivy Oracle on the Ethereum Sepolia testnet, demonstrating that it achieves up to 63.6% lower on-chain gas consumption than Chainlink for signature verification while maintaining only a slight increase in communication overhead due to its dual-attestation mechanism. These results confirm that Ivy Oracle provides strong time trustworthiness and data reliability with minimal performance cost, making it suitable for latency-sensitive blockchain applications. Full article

Distributed Acoustic Sensing (DAS) transforms conventional optical fibres into large-scale acoustic sensor arrays. While existing telecommunication cables are increasingly considered for DAS-based monitoring, their performance depends strongly on cable construction and strain transfer efficiency. In this study, the relative DAS signal amplitudes of three commercial telecommunication optical cables were experimentally compared using a benchtop Rayleigh backscattering-based interrogator under controlled laboratory conditions. By maintaining a constant temperature and ensuring no additional strain changes from the outside environment, we guaranteed that only strain-induced variations from acoustic excitations were measured. The results show clear differences in signal amplitude and signal-to-noise ratio (SNR) among the tested cables. The Microcable consistently produced the highest spatial peak amplitude (up to 0.029 a.u.) and SNR (up to 79), while the Duct cable reached 0.00268 a.u. with mean SNR ≈ 32. The Anti-Rodent cable showed low signal amplitude (0.0018 a.u.) but exhibited a high mean SNR (≈111) driven by an exceptional low noise floor in one of the runs. These findings reflect the variations in mechanical coupling between the fibre core and external perturbations and provide practical insights into the suitability of different telecom cable types for DAS applications, supporting informed choices for future deployments. Full article

This paper presents a novel multi-loop control strategy for Vienna rectifiers that eliminates coordinate transformations while achieving superior performance under adverse grid conditions. Unlike conventional $dq$-frame controllers that suffer from computational complexity and degraded performance during unbalanced conditions, the proposed $abc$-frame scheme achieves a power factor of $98\%$ with total harmonic distortion (THD) below $5\%$ across all operating conditions. The system exhibits a settling time under 120 μs for $90\%$ load transients and ensures robust operation during Type A voltage sags while maintaining a $94\%$ power factor. Furthermore, it guarantees zero steady-state neutral point deviation. The controller employs a dual-loop architecture with high-gain current tracking and PI-based voltage regulation, validated through extensive PSIM/C++ co-simulations at 120 kw. Comparative analysis demonstrates a $35\%$ reduction in computational burden relative to $dq$-frame alternatives, while fully complying with IEEE-519:2022 standards. These results highlight the proposed method as a practical and robust solution for industrial rectification applications requiring grid-fault tolerance. Full article

This paper describes a compact low-cost telemetry system featuring ready-made sensors and an acquisition unit based on the ESP32, which makes use of the LoRa/Wi-Fi wireless standard for communication, and autonomous fallback logging to guarantee data recovery during communication loss. Ensuring safe atmospheric re-entry requires reliable onboard monitoring of capsule conditions during descent. The system is intended for sub-orbital, low-cost educational capsules and experimental atmospheric descent missions rather than full orbital re-entry at hypersonic speeds, where the environmental loads and communication constraints differ significantly. The novelty of this work is the development of a fully self-contained telemetry system that ensures continuous monitoring and fallback logging without external infrastructure, bridging the gap in compact solutions for CubeSat-scale capsules. In contrast to existing approaches built around UAVs or radar, the proposed design is entirely self-contained, lightweight, and tailored to CubeSat-class and academic missions, where costs and infrastructure are limited. Ground test validation consisted of vertical drop tests, wind tunnel runs, and hardware-in-the-loop simulations. In addition, high-temperature thermal cycling tests were performed to assess system reliability under rapid temperature transitions between −20 °C and +110 °C, confirming stable operation and data integrity under thermal stress. Results showed over 95% real-time packet success with full data recovery in blackout events, while acceleration profiling confirmed resilience to peak decelerations of ~9 g. To complement telemetry, the TeleCapsNet dataset was introduced, facilitating a CNN recognition of descent states via 87% mean Average Precision, and an F1-score of 0.82, which attests to feasibility under constrained computational power. The novelty of this work is twofold: having reliable dual-path telemetry in real-time with full post-mission recovery and producing a scalable platform that explicitly addresses the lack of compact, infrastructure-independent proposals found in the existing literature. Results show an independent and cost-effective system for small re-entry capsule experimenters with reliable data integrity (without external infrastructure). Future work will explore AI systems deployment as a means to prolong the onboard autonomy, as well as to broaden the applicability of the presented approach into academic and low-resource re- entry investigations. Full article

In Underwater Wireless Sensor Networks (UWSNs) with mobile nodes, the mobility of the nodes leads to dynamic changes in the network topology. Thus, pre-established routing paths may become invalid and next-hop nodes may be unavailable due to link disruptions. This implies that routing decisions for mobile UWSNs that do not account for changes in the connectivity state of communication links cannot guarantee reliable packet delivery. In this study, a Q-learning-based link-aware routing (QLAR) protocol designed for mobile UWSNs is proposed. The proposed QLAR protocol introduces the Link Expiration Time (LET) into the reward function of the Q-learning algorithm as a critical decision metric, thereby guiding the agent to prioritize more stable communication links with longer expected lifetime. In addition, multiple decision metrics are dynamically predicted and updated by actively perceiving and acquiring information from neighbor nodes through periodic control packet interactions. To achieve a balance among these metrics, the Entropy Weight Method (EWM) is employed to adaptively adjust their weights in response to real-time network conditions. Comprehensive simulation results demonstrate that QLAR outperforms existing routing protocols in terms of various performance metrics under different scenarios. Full article

Construction of new seawater reverse osmosis desalination (SWRO) plants in the state of California (USA) requires environmental permits containing rather strict conditions. The California Ocean Plan requires the use of subsurface intake systems (SSIs) unless they are deemed to be not feasible. The Governor of California requested that the State Water Resources Control Board (State Board) study the issue of accelerating the desalination plant permitting process and making it more efficient. The State Board formed an independent scientific Panel to study the issue of SSI feasibility and to submit a report. The Panel recommendations included the following: the feasibility assessment (FA) for SSIs should be streamlined for completion within a maximum of three years, and this requirement should be added to the Ocean Plan; applicants need to perform a financial feasibility study before pursuing SSI capacities exceeding 38,000 m³/d (10 MGD) for wells or 100,000 m³/d (25 MGD) for galleries because project financing may be denied for such larger capacity systems; the mitigation options for each site–SSI combination in the screening process should be addressed by both the project proponent and regulatory agencies as early as practicable in the overall permitting process; and the impacts of SSIs on local aquifers and associated wetland systems must be assessed during the analyses conducted during the FA and during post-construction monitoring. The Panel further concluded that the design and evaluation of SSI–site combinations are highly site-specific, involving technically complex issues, which require both the applicant and the reviewing state agencies to have the expertise to design and review the applications. Economic feasibility must consider cost to the consumer and the engineering risk that can preclude project financing. Projected capacities exceeding the above noted limits may not by financed due to risks of failure or could require government guarantees to lenders. The current permitting system in California is likely to preclude construction of large seawater desalination facilities that can provide another source of potable water for coastal communities in California during severe droughts. Without seawater desalination, the potable water supply in California would suffer a greater sustainability and resilience risk during future periods of extended drought. Full article

This paper proposes a safety-constrained interaction control scheme for robotic manipulators by integrating model predictive control (MPC) and active disturbance rejection control (ADRC). The proposed method is specifically designed for manipulators with complex nonlinear dynamics. To ensure that the control system satisfies safety constraints during human–robot interaction, MPC is incorporated into the impedance control framework to construct a model predictive impedance controller (MPIC). By exploiting the prediction and constraint-handling capabilities of MPC, the controller provides guaranteed safety throughout the interaction process. Meanwhile, ADRC is employed to track the target joint control signals generated by the MPIC, where an extended state observer is utilized to compensate for dynamic modeling errors and nonlinear disturbances within the system, thereby achieving accurate trajectory tracking. The proposed method is validated through both simulation and real-world experiments, achieving high-performance interaction control with safety constraints at a 2 ms control cycle. The controller exhibits active compliant interaction behavior when the interaction stays within the constraint boundaries, while maintaining strict adherence to the safety constraints when the interaction tends to violate them. Full article

Generalized censoring, combined with a power-based distribution, improves inferential efficiency by capturing more detailed failure-time information in complex testing scenarios. Conventional censoring schemes may discard substantial failure-time information, leading to inefficiencies in parameter estimation and reliability prediction. To address this limitation, we develop a comprehensive inferential framework for the alpha-power Weibull (APW) distribution under a generalized progressive hybrid Type-II censoring scheme, a flexible design that unifies classical, hybrid, and progressive censoring while guaranteeing test completion within preassigned limits. Both maximum likelihood and Bayesian estimation procedures are derived for the model parameters, reliability function, and hazard rate. Associated uncertainty quantification is provided through asymptotic confidence intervals (normal and log-normal approximations) and Bayesian credible intervals obtained via Markov chain Monte Carlo (MCMC) methods with independent gamma priors. In addition, we propose optimal censoring designs based on trace, determinant, and quantile-variance criteria to maximize inferential efficiency at the design stage. Extensive Monte Carlo simulations, assessed using four precision measures, demonstrate that the Bayesian MCMC estimators consistently outperform their frequentist counterparts in terms of bias, mean squared error, robustness, and interval coverage across a wide range of censoring levels and prior settings. Finally, the proposed methodology is validated using real-life datasets from engineering (electronic devices), clinical (organ transplant), and physical (rare metals) studies, demonstrating the APW model’s superior goodness-of-fit, reliability prediction, and inferential stability. Overall, this study demonstrates that combining generalized censoring with the APW distribution substantially enhances inferential efficiency and predictive performance, offering a robust and versatile tool for complex life-testing experiments across multiple scientific domains. Full article

This paper presents a traffic-driven scaling framework for a digital twin proxy pool (DTPP) in vehicular edge computing (VEC), designed to eliminate the latency and synchronization issues inherent in conventional digital twin (DT) migration approaches. The core innovation lies in replacing the migration of vehicle DTs between edge servers (ESs) with instantaneous switching within a pre-allocated pool of DT proxies, thereby achieving zero migration latency and continuous synchronization. The proposed architecture differentiates between short-term DTs (SDTs) hosted in edge-side in-memory databases for real-time, low-latency services, and long-term DTs (LDTs) in the cloud for historical data aggregation. A queuing-theoretic model formulates the DTPP as an M/M/c system, deriving a closed-form lower bound for the minimum number of proxies required to satisfy a predefined queuing-delay constraint, thus transforming quality-of-service targets into analytically computable resource allocations. The scaling mechanism operates on a cloud–edge collaborative principle: a cloud-based predictor, employing a TCN-Transformer fusion model, forecasts hourly traffic arrival rates to set a baseline proxy count, while edge-side managers perform monotonic, 5 min scale-ups based on real-time monitoring to absorb sudden traffic bursts without causing service jitter. Extensive evaluations were conducted using the PeMS dataset. The TCN-Transformer predictor significantly outperforms single-model baselines, achieving a mean absolute percentage error (MAPE) of 17.83%. More importantly, dynamic scaling at the ES reduces delay violation rates substantially—for instance, from 13.57% under static provisioning to just 1.35% when the minimum proxy count is 2—confirming the system’s ability to maintain service quality under highly dynamic conditions. These findings shows that the DTPP framework provides a robust solution for resource-efficient and latency-guaranteed DT services in VEC. Full article

Federated learning (FL) enables collaborative model training without centralizing raw data, but its application to large-scale vision models remains constrained by high communication cost, data heterogeneity, and privacy risks. Furthermore, in real-world applications such as autonomous driving and healthcare, model updates can inadvertently expose sensitive information even without direct data sharing. This highlights the need for frameworks that balance privacy, efficiency, and accuracy. The current approach to addressing information exposure involves encrypting data by incorporating additional encoding. However, such approaches to encrypting data significantly increase communication costs. In this paper, we propose Federated Share-A Low-Rank Adaptation with Differential Privacy (FedSA-LoRA-DP), a parameter-efficient and privacy-preserving federated learning framework. The framework combines selective aggregation of low-rank parameters with Differential Privacy (DP), ensuring that only lightweight components are shared while formally bounding individual data influence. Since DP simply perturbs the numeric values of existing parameters without altering their dimensionality or structure, it does not increase communication cost. This design allows FedSA-LoRA-DP to provide strong privacy guarantees while maintaining communication efficiency and model accuracy. Experiments on CIFAR-100, MNIST, and SVHN datasets demonstrate that the proposed framework achieves accuracy comparable to non-private counterparts, even under heterogeneous non-independent and identically distributed data and partial client participation. These results demonstrate that integrating differential privacy into low-rank adaptation enables privacy-preserving and communication-efficient federated learning without sacrificing model performance across heterogeneous environments. Full article

The independent metering system (IMS) realizes the independent adjustments of oil inlet and oil return by decoupling the control of the inlet and outlet orifices of the valve, which can significantly improve the energy utilization efficiency while ensuring the tracking accuracy of the system. In this paper, a predetermined performance control (PPC) strategy based on adaptive output feedback is proposed. Firstly, the K-filter observer is introduced into the IMS framework for the purpose of obtaining accurate estimates of the internal state variables that are not directly measurable. Secondly, the fuzzy logic system (FLS) is used to effectively compensate for the unmodeled error and external disturbance in system dynamics. Furthermore, by incorporating PPC, the control objective is to guarantee that all state errors converge to and remain within the prescribed performance function boundaries within a given time frame. At the same time, the dynamic surface control (DSC) method is used to alleviate the common ‘computational explosion’ problem in the backstepping design. In addition, the oil return pressure controller is designed to maintain the oil return back pressure at a low level, thereby reducing the pressure loss in the oil return path and improving the overall energy efficiency of the system. The theoretical analysis results show that the proposed controller can effectively improve the energy efficiency of the system while ensuring the tracking accuracy. Finally, the effectiveness and superiority of the control strategy are verified by comparative experiments. Full article