Search Results (2,690)

This paper addresses the problem of observer-based control (OBC) for nonlinear systems with time delay (TD). A novel hybrid modeling framework for nonlinear TD systems is first introduced by synergistically combining TD Takagi–Sugeno (TDTS) fuzzy and Lipschitz approaches. The proposed methodology broadens the range of representable systems by enabling Lipschitz nonlinearities to fulfill dual functions: they may describe essential dynamic behaviors of the system or represent aggregated uncertainties, depending on the specific application. The proposed TDTS–Lipschitz (TDTSL) model class features measurable premise variables while accommodating Lipschitz nonlinearities that may depend on unmeasurable system states. Then, through the construction of an appropriate Lyapunov–Krasovskii (L-K) functional, we derive sufficient conditions to ensure exponential stability of the augmented closed-loop model. Subsequently, through a decoupling methodology, these stability conditions are reformulated as a set of linear matrix inequalities (LMIs). Finally, the proposed OBC design is validated through application to a continuous stirred tank reactor (CSTR) with lumped uncertainties. Full article

Deploying Large Language Models (LLMs) in edge environments faces two major challenges: (i) the conflict between limited device resources and high computational demands, and (ii) the dynamic impact of user mobility on model partitioning and task offloading decisions. To address these challenges, this paper proposes the Dynamic Adaptive Partitioning and Offloading (DAPO) framework, an intelligent solution for multi-user, multi-edge Mobile Edge Intelligence (MEI) systems. DAPO employs a Deep Deterministic Policy Gradient (DDPG) algorithm to jointly optimize the model partition point and the task offloading destination. By mapping continuous policy outputs onto valid discrete actions, DAPO efficiently addresses the high-dimensional hybrid action space and dynamically adapts to user mobility. Through extensive simulations, we demonstrate that DAPO outperforms baseline strategies and mainstream RL methods, achieving up to 27% lower latency and 18% lower energy consumption compared to PPO and A2C, while maintaining fast convergence and scalability in dynamic mobile environments. Full article

Lithium-ion batteries are widely used in energy storage and power systems due to their high energy density, long cycle life, and stability. Accurate prediction of the state of health (SOH) of batteries is critical to ensuring their safe and reliable operation. However, the prediction task remains challenging due to various complex factors. This paper proposes a hybrid TCN–Transformer–BiLSTM prediction model for battery SOH estimation. The model is first validated using the NASA public dataset, followed by further verification with dynamic operating condition simulation experimental data. Health features correlated with SOH are identified through Pearson analysis, and comparisons are conducted with existing LSTM, GRU, and BiLSTM methods. Experimental results demonstrate that the proposed model achieves outstanding performance across multiple datasets, with root mean square error (RMSE) values consistently below 2% and even below 1% in specific cases. Furthermore, the model maintains high prediction accuracy even when trained with only 50% of the data. Full article

Many applications of quantum computers require the classical and quantum implementation of dynamic systems (DSs). These applications comprise interacting quantum and classical tasks. While quantum tasks evolve in the quantum domain, classical tasks behave in the classical domain. Besides tackling these kinds of tasks, the computational gap between these domains is covered by the current study. The quantum computing feature All at Once (A@O) executions is appropriate for static systems but less for DSs. The novelty of the proposed approach consists of using Distributed Quantum-Centric Petri Net (DQCPN) models composed of quantum and high-level Petri Nets for specification, design, verification, and implementation of classical–quantum applications. Quantum Processing Units (QPUs) are linked to classical components implementing the control and optimization operations in the proposed application. Many practical applications combine quantum and classical computing to address optimization problems. Quantum computers can be built with a combination of qubits and bosonic qumodes, leading to a new paradigm toward quantum computing. The optimizations are performed by some Evolutionary Algorithms (EAs), including Particle Swarm Optimization (PSO) methods and Genetic Algorithms (GAs). For experiments, an Urban Vehicle Traffic System (UVTS) is used as an open distributed system. The vehicle flows are implemented by discrete qubits, discrete vectors of qubits, or qumodes. Full article

This study investigates the effect of incorporating an electromagnetic harvester inside the bluff body of a 2-DoF hybrid harvester in comparison to a standalone piezoelectric harvester for various external loads. The harvester is excited through a vortex-induced vibration owing to the resultant wake vortices created behind the bluff body. The coupled dynamics of the two harvester components are modeled, and numerical simulations are conducted to evaluate the system’s performance under varying electrical loads. Numerical results show that at high, optimum electrical load, the standalone piezoelectric harvester outperforms the hybrid harvester. Nevertheless, for small electrical loads, the results show that the hybrid harvester outperforms the standalone PZT harvester by up to 18% in peak power output, while reducing the bandwidth by approximately 10% compared to the standalone piezoelectric harvester. Optimal spring stiffness values were identified, with the hybrid harvester achieving its maximum output power at a spring stiffness of 83.56 N/m. These findings underscore the need for careful design considerations, as the hybrid harvester may not achieve enhanced power output and bandwidth under higher electrical loads. Full article

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

Grid-based distribution networks represent an advanced form of smart grids that enable modular, region-specific optimization of power resource allocation. This paper presents a novel planning framework aimed at the coordinated deployment of distributed generation, electrical loads, and energy storage systems, including both dispatchable and non-dispatchable electric vehicles. A three-dimensional objective system is constructed, incorporating investment cost, reliability metrics, and network loss indicators, forming a comprehensive multi-objective optimization model. To solve this complex planning problem, an improved version of the NSGA-II is employed, integrating hybrid encoding, feasibility constraints, and fuzzy decision-making for enhanced solution quality. The proposed method is applied to the IEEE 33-bus distribution system to validate its practicality. Simulation results demonstrate that the framework effectively addresses key challenges in modern distribution networks, including renewable intermittency, dynamic load variation, resource coordination, and computational tractability. It significantly enhances system operational efficiency and electric vehicles charging flexibility under varying conditions. In the IEEE 33-bus test, the coordinated optimization (Scheme 4) reduced the expected load loss from 100 × 10⁻⁴ yuan to 51 × 10⁻⁴ yuan. Network losses also dropped from 2.7 × 10⁻⁴ yuan to 2.5 × 10⁻⁴ yuan. The findings highlight the model’s capability to balance economic investment and reliability, offering a robust solution for future intelligent distribution network planning and integrated energy resource management. Full article

This research is based on the acoustic package design of new energy vehicles, investigating the application of the hybrid Finite Element–Statistical Energy Analysis (FE-SEA) model in predicting the high-frequency dynamic response of automotive structures, with a focus on the modeling and correction methods for hybrid point connections. New energy vehicles face unique acoustic challenges due to the special nature of their power systems and operating conditions, such as high-frequency noise from electric motors and electronic devices, wind noise, and road noise at low speeds, which directly affect the vehicle’s ride comfort. Therefore, optimizing the acoustic package design of new energy vehicles to reduce in-cabin noise and improve acoustic quality is an important issue in automotive engineering. In this context, this study proposes an improved point connection correction factor by optimizing the division range of the decision circle. The factor corrects the dynamic stiffness of point connections based on wave characteristics, aiming to improve the analysis accuracy of the hybrid FE-SEA model and enhance its ability to model boundary effects. Simulation results show that the proposed method can effectively improve the model’s analysis accuracy, reduce the degrees of freedom in analysis, and increase efficiency, providing important theoretical support and reference for the acoustic package design and NVH performance optimization of new energy vehicles. Full article

The early developmental transition from endogenous to exogenous feeding is a critical period in carnivorous fish larvae, often associated with high mortality rates in aquaculture. Although trypsin, a key protease in protein digestion, is hypothesized to play a pivotal role in initiating exogenous feeding, the expression dynamics and functional contributions of trypsin and isoforms during early development remain poorly characterized in carnivorous species. This study explores the critical role of trypsin in the early feeding process of carnivorous fish, using mandarin fish (Siniperca chuatsi) as a model, which is a commercially valuable species that faces significant challenges during this phase due to its strict dependence on live prey and underdeveloped digestive system. Phylogenetic analysis indicates that, compared to herbivorous and omnivorous fish, carnivorous fish have evolved a greater number of trypsins, with a distinct branch specifically dedicated to try. RNA-seq data revealed the expression profiles of 13 trypsins during the early developmental stages of the mandarin fish. Most trypsins began to be expressed in large quantities with the appearance of the pancreas, reaching a peak prior to feeding. In situ hybridization revealed the spatiotemporal expression pattern of trypsins, starting from the pancreas in early development and later extending to the intestines. Furthermore, inhibition of trypsins activity successfully suppressed early oral feeding in mandarin fish, which was achieved by increasing the expression of cholecystokinin 2 (CCK2) and proopiomelanocortin (POMC) to suppress appetite. These findings enhance our understanding of the adaptive relationship between the ontogeny of the digestive enzyme system and feeding behavior in carnivorous fish. This research may help alleviate bottleneck issues in aquaculture production by improving the survival rate and growth performance of carnivorous fish during critical early life stages. Full article

Hyperspectral imaging (HSI) systems often suffer from complex noise degradation during the imaging process, significantly impacting downstream applications. Deep learning-based methods, though effective, rely on impractical paired training data, while traditional model-based methods require manually tuned hyperparameters and lack generalization. To address these issues, we propose SS³L (Self-Supervised Spectral-Spatial Subspace Learning), a novel HSI denoising framework that requires neither paired data nor manual tuning. Specifically, we introduce a self-supervised spectral–spatial paradigm that learns noisy features from noisy data, rather than paired training data, based on spatial geometric symmetry and spectral local consistency constraints. To avoid manual hyperparameter tuning, we propose an adaptive rank subspace representation and a loss function designed based on the collaborative integration of spectral and spatial losses via noise-aware spectral-spatial weighting, guided by the estimated noise intensity. These components jointly enable a dynamic trade-off between detail preservation and noise reduction under varying noise levels. The proposed SS³L embeds noise-adaptive subspace representations into the dynamic spectral–spatial hybrid loss-constrained network, enabling cross-sensor denoising through prior-informed self-supervision. Experimental results demonstrate that SS³L effectively removes noise while preserving both structural fidelity and spectral accuracy under diverse noise conditions. Full article

Demand response (DR) is increasingly recognized as a critical flexibility resource for modernizing power systems, enabling the large-scale integration of renewable energy and enhancing grid stability. While the field of general electricity load forecasting is supported by numerous systematic reviews, the specific subfield of DR potential forecasting has received comparatively less synthesized attention. This gap leaves a fragmented understanding of modeling techniques, practical implementation challenges, and future research problems for a function that is essential for market participation. To address this, this paper presents a PRISMA-2020-compliant systematic review of 172 studies to comprehensively analyze the state-of-the-art in DR potential estimation. We categorize and evaluate the evolution of forecasting methodologies, from foundational statistical models to advanced AI architectures. Furthermore, the study identifies key technological enablers and systematically maps the persistent technical, regulatory, and behavioral barriers that impede widespread DR deployment. Our analysis demonstrates a clear trend towards hybrid and ensemble models, which outperform standalone approaches by integrating the strengths of diverse techniques to capture complex, nonlinear consumer dynamics. The findings underscore that while technologies like Advanced Metering Infrastructure (AMI) and the Internet of Things (IoT) are critical enablers, the gap between theoretical potential and realized flexibility is primarily dictated by non-technical factors, including inaccurate baseline methodologies, restrictive market designs, and low consumer engagement. This synthesis brings much-needed structure to a fragmented research area, evaluating the current state of forecasting methods and identifying the critical research directions required to improve the operational effectiveness of DR programs. Full article

This study introduces a novel Fuzzy Piecewise Fractional Derivative (FPFD) framework to enhance epidemiological modeling, specifically for the multi-phasic co-infection dynamics of Omicron and malaria. We address the limitations of traditional models by incorporating two key realities. First, we use fuzzy set theory to manage the inherent uncertainty in biological parameters. Second, we employ piecewise fractional operators to capture the dynamic, phase-dependent nature of epidemics. The framework utilizes a fuzzy classical derivative for initial memoryless spread and transitions to a fuzzy Atangana–Baleanu–Caputo (ABC) fractional derivative to capture post-intervention memory effects. We establish the mathematical rigor of the FPFD model through proofs of positivity, boundedness, and stability of equilibrium points, including the basic reproductive number (R0). A hybrid numerical scheme, combining Fuzzy Runge–Kutta and Fuzzy Fractional Adams–Bashforth–Moulton algorithms, is developed for solving the system. Simulations show that the framework successfully models dynamic shifts while propagating uncertainty. This provides forecasts that are more robust and practical, directly informing public health interventions. Full article

Flexible and wearable sensors have emerged as transformative technologies in the field of real-time health monitoring, offering non-invasive, continuous, and personalized healthcare solutions. These devices are designed to conform intimately to the human body, enabling seamless detection of vital physiological and biochemical signals under dynamic conditions. Recent advancements in material science and device engineering have led to the development of sensors with enhanced sensitivity, biocompatibility, and wearability, addressing the growing demand for preventive healthcare and remote patient monitoring. This review provides a comprehensive overview of the progress in flexible wearable sensors, including novel materials, sensor designs, and system integration strategies. It begins by surveying the latest advances in substrate and functional materials and hybrid structures that enable mechanical flexibility, skin conformability, and high sensitivity. The review then examines various sensor mechanisms and their implementation in monitoring vital signs, physical activity, and chronic diseases. Real-world applications are explored in depth, covering scenarios from cardiovascular and respiratory monitoring to motion tracking and rehabilitation support. Despite the significant strides made, challenges related to material robustness, sensor accuracy, and multi-modal integration remain, and this review discusses these challenges alongside potential future directions for enhancing the functionality and adoption of flexible wearable sensor systems. Full article

Autonomous robotic navigation is essential in modern systems for revolutionising various industries that operate in both static and dynamic environments. To achieve this autonomous navigation, various conventional techniques that handle environment mapping, path planning, and motion control as individual modules often face challenges in addressing the complexities of autonomous navigation. Therefore, this paper presents an integrated technique that combines three essential components, such as environment mapping, path planning, and motion control, to enhance autonomous navigation performance. The first component, i.e., the mapping, utilises both binary and probabilistic occupancy maps to represent the environment. The second component is path planning, which incorporates various graph- and sampling-based algorithms such as PRM, A*, Hybrid A*, RRT, RRT*, and BiRRT, which are evaluated in terms of path length, computational time, and safety margin on various maps. The final component, i.e., motion control, utilises both conventional and advanced controller strategies such as PID, FOPID, SFC, and MPC, for better sinusoidal trajectory tracking. The four case studies for path planning and one case study on trajectory tracking on various occupancy maps demonstrated that the A* algorithm and MPC outperformed all the compared techniques in terms of optimal path length, computational time, safety margin, and trajectory tracking error. Thus, the proposed integrated approach reveals that the interplay between mapping fidelity, planning efficiency, and control robustness is vital for reliable autonomous navigation. Full article

Fault detection is critical to the resilience and operational integrity of electrical power grids, particularly smart grids. In addition to requiring a lot of labeled data, traditional fault detection approaches have limited flexibility in handling unknown fault scenarios. In addition, since traditional machine learning models rely on historical data, they struggle to adapt to new fault patterns in dynamic grid environments. Due to these limitations, fault detection systems have limited resilience and scalability, necessitating more advanced approaches. This paper presents a hybrid technique that integrates supervised and unsupervised machine learning with Generative AI to generate artificial data to aid in fault identification. A number of machine learning algorithms were compared with regard to how they detect symmetrical and asymmetrical faults in varying conditions, with a particular focus on fault conditions that have not happened before. A key feature of this study is the application of the autoencoder, a new machine learning model, to compare different ML models. The autoencoder, an unsupervised model, performed better than other models in the detection of faults outside the learning dataset, pointing to its potential to enhance smart grid resilience and stability. Also, the study compared a generative AI-generated dataset (D2) with a conventionally prepared dataset (D1). When the two datasets were utilized to train various machine learning models, the synthetic dataset (D2) outperformed D1 in accuracy and scalability for fault detection applications. The strength of generative AI in improving the quality of data for machine learning is thus indicated by this discovery.By emphasizing the necessity of using advanced machine learning techniques and high-quality synthetic datasets, this research aims to increase the resilience of smart grid networks through improved fault detection and identification. Full article