Search Results (40)

The novel enhanced dynamic game method (EDGM) is proposed to advance game-based design approaches, with a focus on enhancing solution distribution, precision, and the ability to reveal the dynamic influence sensitivity of design variables on objective functions. An integrated mathematical model is developed by combining EDGM with PARSEC and CST parameterization methods, forming a systematic framework for offshore wind turbine airfoil optimization. Targeting airfoils with approximately 30% and 35% thickness, the study aims to improve annual energy production (AEP) and optimize the polar moment of inertia. Redesigned airfoils using the EDGM-integrated model exhibit significant enhancements in aerodynamic performance and anti-flutter capability compared to baseline airfoils DU97W300 and DU99W350. The methodology’s superiority is validated through analyses of pressure distributions, lift-to-drag ratios, and streamline patterns, as well as comparative evaluations using HV and Spacing metrics, demonstrating EDGM’s potential for broader engineering applications in complex multi-objective optimization scenarios. Full article

This paper presents a method for optimizing the inertia constants and damping coefficients of interconnected virtual synchronous generators (VSGs) using a genetic algorithm. The goal of optimization is to find a balance between minimizing the rate of change of frequency (RoCoF) and enhancing controllability. Five controllability-based metrics are tested: the minimum eigenvalue, the sum of the two smallest eigenvalues, the maximum eigenvalue, the trace, and the determinant of the controllability Gramian matrix. The approach includes the oscillatory modes’ damping ratio constraints to ensure the small-signal stability of the entire system. The results of optimization on the IEEE 9-bus system with three VSGs show that the proposed method improves controllability, reduces RoCoF, and maintains the desired oscillation damping. The proposed approach was tested through time-domain simulations. Full article

To address the modeling complexity and multi-objective collaborative optimization challenges in multi-depot and multiple unmanned aerial vehicle (UAV) delivery task planning, this paper proposes a bi-layer planning framework, which comprehensively considers resource constraints, multi-depot coordination, and the coupling characteristics of path execution. The novelty of this work lies in the seamless integration of an enhanced genetic algorithm and tailored swarm optimization within a unified two-tier architecture. The upper layer tackles the task assignment problem by formulating a multi-objective optimization model aimed at minimizing economic costs, delivery delays, and the number of UAVs deployed. The Enhanced Non-Dominated Sorting Genetic Algorithm II (ENSGA-II) is developed, incorporating heuristic initialization, goal-oriented search operators, an adaptive mutation mechanism, and a staged evolution control strategy to improve solution feasibility and distribution quality. The main contributions are threefold: (1) a novel ENSGA-II design for efficient and well-distributed task allocation; (2) an improved PSO-based path planner with chaotic initialization and adaptive parameters; and (3) comprehensive validation demonstrating substantial gains over baseline methods. The lower layer addresses the path planning problem by establishing a multi-objective model that considers path length, flight risk, and altitude variation. An improved particle swarm optimization (PSO) algorithm is proposed by integrating chaotic initialization, linearly adjusted acceleration coefficients and maximum velocity, a stochastic disturbance-based position update mechanism, and an adaptively tuned inertia weight to enhance algorithmic performance and path generation quality. Simulation results under typical task scenarios demonstrate that the proposed model achieves an average reduction of 47.8% in economic costs and 71.4% in UAV deployment quantity while significantly reducing delivery window violations. The framework exhibits excellent capability in multi-objective collaborative optimization. The ENSGA-II algorithm outperforms baseline algorithms significantly across performance metrics, achieving a hypervolume (HV) value of 1.0771 (improving by 72.35% to 109.82%) and an average inverted generational distance (IGD) of 0.0295, markedly better than those of comparison algorithms (ranging from 0.0893 to 0.2714). The algorithm also demonstrates overwhelming superiority in the C-metric, indicating outstanding global optimization capability in terms of distribution, convergence, and the diversity of the solution set. Moreover, the proposed framework and algorithm are both effective and feasible, offering a novel approach to low-altitude urban logistics delivery problems. Full article

In order to expand the bandwidth of the power amplifier (PA), this paper proposes a PA optimization design method based on improved particle swarm optimization (PSO) with output power and efficiency as optimization objectives. Simulated annealing strategy and adaptive inertia weight are introduced to PSO to achieve the global optimum and accelerate the convergence speed. Considering performance metrics of PA, such as the efficiency and output power, a piecewise objective function is formulated for wideband PA optimization design. For validation, a wideband PA operating at 0.5–4.3 GHz (fractional bandwidth of 158.3%) was designed and fabricated. Measured results show a saturated output power ranging from 39.7 to 42.4 dBm and an efficiency between 60.5 and 68.8% within the operating bandwidth. Full article

One of the most challenging problems in data analysis is visualizing patterns and extracting insights from multi-dimensional datasets that vary over time. The complexity of data and variations in the correlations between different features adds further difficulty to the analysis. In this paper, we provide a framework to analyze the temporal dynamics of such datasets. We use machine learning clustering techniques and examine the time evolution of data patterns by constructing the corresponding cluster trajectories. These trajectories allow us to visualize the patterns and the changing nature of correlations over time. The similarity and correlations of features are reflected in common cluster membership, whereas the historical dynamics are described by a trajectory in the corresponding (cluster, time) space. This allows an effective visualization of multi-dimensional data over time. We introduce several statistical metrics to measure duration, volatility, and inertia of changes in patterns. Using the Hamming distance of trajectories over multiple time periods, we propose a novel metric, the Hamming diversification index, to measure the spread between trajectories. The novel metric is easy to compute, has a simple machine learning implementation, and provides additional insights into the temporal dynamics of data. This parsimonious diversification index can be used to examine changes in pattern similarities over aggregated time periods. We demonstrate the efficacy of our approach by analyzing a complex multi-year dataset of multiple worldwide economic indicators. Full article

Snowboard Big Air (SBA), recognized as an Olympic discipline since 2018, emphasizes maneuver difficulty as a key scoring criterion, requiring athletes to integrate technical skill with adaptive responses to dynamic environments in order to perform complex aerial rotations. The takeoff phase is critical, determining both flight trajectory and rotational performance through coordinated lower limb extension and upper body movements. Despite advances in motion analysis technology, quantitative assessment of key takeoff parameters remains limited. This study investigates parameters related to performance, joint kinematics, and rotational kinetics during the SBA takeoff phase to identify key factors for success and provide practical guidance to athletes and coaches. Eleven athletes from the Chinese national snowboard team performed multiple backside tricks (720°, 1080°, 1440°, and 1800°) at an outdoor dry slope with airbag landings. Three-dimensional motion capture with synchronized cameras was used to collect data on performance, joint motion, and rotational kinetics during takeoff. The results showed significant increases in most measured metrics with rising trick difficulty from 720° to 1800°. The findings reveal that elite SBA athletes optimize performance in high-difficulty maneuvers by increasing the moment of inertia, maximizing propulsion, and refining joint kinematics to enhance rotational energy and speed. These results suggest that training should emphasize lower limb power, core and shoulder strength, flexibility, and coordination to maximize performance in advanced maneuvers. Full article

This study investigates the potential of thermally treated olive husk (OH)—a heterogeneous agro-industrial by-product comprising olive stones, pulp, and fibrous residues—as a multifunctional component in lightweight bio-concrete. Uniquely, this work harnesses the intrinsic dual nature of OH as both a fibrous reinforcement and a porous aggregate, without further fractionation, to evaluate its influence on the hygrothermal and mechanical behavior of cementitious composites. While prior studies have often focused selectively on thermal conductivity, this work provides a comprehensive assessment of all major thermal parameters; including diffusivity, effusivity, and specific heat capacity; offering deeper insights into the full thermal behavior of bio-based concretes. OH was incorporated at 0%, 10%, and 20% by weight, and the resulting concretes were subjected to a comprehensive characterization of their thermal, hygric, mechanical, and microstructural properties. Thermal performance metrics included conductivity, specific heat capacity, diffusivity, effusivity, time lag, and predicted energy savings. Hygric behavior was assessed through the moisture buffering value (MBV), while density, porosity, and mechanical strengths were also evaluated. At 20% OH content, thermal conductivity decreased to 0.405 W/m·K (a 72% reduction), thermal diffusivity dropped by 87%, and thermal effusivity reached 554 W·s^0.5/m²·K, collectively enhancing thermal inertia and increasing the time lag by 77% (to 2.32 h). MBVs improved to 2.18 g/m²·%RH, rated as “Excellent” for indoor moisture regulation. Despite the higher porosity, the bio-concrete maintained adequate mechanical integrity, with compressive and flexural strengths of 11.68 MPa and 3.58 MPa, respectively, attributed to the crack-bridging action of the fibrous inclusions. Microstructural analysis (SEM/XRD) revealed improved paste continuity and denser C–S–H formation, attributed to enhanced matrix compatibility following oil removal via thermal pre-treatment. These findings demonstrate the viability of OH as a new bio-based, multifunctional additive for fabricating thermally efficient, hygroscopically active, and structurally sound concretes suitable for sustainable construction. Full article

The integration of variable distributed energy sources (DERs) can reduce overall system inertia, potentially impacting the transient response of both conventional and renewable generators within electrical grids. Although transient stability indicators—for instance, the Critical Clearing Time (CCT), fault-induced short-circuit current ratios, and machine parameters, including subtransient–transient reactances and associated time constants—are influenced by total system inertia, their detailed evaluation remains insufficiently explored. These parameters provide standardized benchmarks for systematically assessing the transient stability performance of conventional and photovoltaic (PV) generators as the penetration level of distributed PV systems (PVD1) increases. This study explores the relationship between conventional stability parameters and system inertia across different levels of PV penetration. CCT, a key metric for transient stability assessment, incorporates multiple influencing factors and typically increases with higher system inertia, making it a reliable comparative indicator for evaluating the effects of PV integration on system stability. To investigate this, the IEEE New England 39-bus system is adapted by replacing selected synchronous machines with PVD1 PV units and adjusting the PV penetration levels. The resulting system behavior is then compared to that of the original configuration to evaluate changes in transient stability. The findings confirm that transient and subtransient reactances, along with their respective time constants under fault conditions, are shaped not only by the characteristics of the generator on the faulted line but also by the surrounding network structure and overall system inertia. The newly introduced sensitivity parameters offer insights by capturing trends specific to conventional versus PV-based generators under different inertia scenarios. Notably, transient parameters show similar responsiveness to inertia variations to subtransient ones. This paper demonstrates that in certain scenarios, the integration of low-inertia PV generators may generate insufficient energy, which is not above critical energy during major disturbances, resulting surviving fault and subsequently an infinite CCT. While the integration of PV generators can be beneficial for their own operational performance, it may adversely impact the dynamic behavior and fault response of conventional synchronous generators within the system. This highlights the need for effective planning and control of DER integration to ensure reliable power system operation through accurate selection and application of both conventional and proposed transient stability parameters. Full article

This paper proposes a robust impedance controller to address the performance limitations of mechanical impedance rendering in robotic joints, enabling stable interaction with passive environments. Considering structured uncertainties, such as dynamic parameter perturbations, sensor noise, disturbances, and unmodeled dynamics in actuator models, the $\mathsf{\mu }$-synthesis method is employed to optimize closed-loop robustness performance. This approach minimizes impedance-matching errors in the frequency domain, thereby enhancing the regulation of the systems’s inherent impedance characteristics. Key performance metrics are analyzed, and the impedance-rendering accuracy is evaluated. Furthermore, the limiting factors affecting impedance-matching bandwidth are investigated to inform the selection of impedance parameters and ensure safe physical interaction. The proposed controller is validated through simulations and hardware experiments on a one-DoF modular robotic joint. Frequency domain impedance matching comparisons show that relative to ${\mathcal{H}}_{\infty }$ control, the $\mathsf{\mu }$-synthesis approach reduces impedance matching errors by up to 94.6% and 97.5% under 5% and 30% inertia uncertainties, respectively. Furthermore, experimental results demonstrate that compared to classical impedance control, the proposed method reduces impedance rendering errors by an average of 85.71% across all tested configurations while maintaining superior passivity and interaction stability under diverse impedance conditions. These results validate the effectiveness of $\mathsf{\mu }$-synthesis in achieving safe and high-fidelity physical interaction behavior. Full article

Lattice materials demonstrate exceptional advantages in lightweight design applications due to their low mass density, high specific strength, and customizable topology. Inspired by the hollow vascular bundle structure of bamboo, this study develops four bio-inspired lattice configurations through two key modifications to conventional body-centered cubic (BCC) structures: Z-axis (loading direction) strut reinforcement and strut hollowing. The specimens were fabricated using AlSi10Mg powder via selective laser melting (SLM) technology, followed by the systematic evaluation of the compressive properties and the energy absorption characteristics. The experimental results reveal that the synergistic combination of Z-strut reinforcement and hollow design significantly enhances both the compressive resistance and the energy absorption capacity. The optimized BCC-5ZH configuration (5 Z-struts with full hollowing) achieves remarkable performance metrics at 0.5 g/cm³ density: yield strength (16.78 MPa), compressive strength (27.91 MPa), and volumetric energy absorption (10.4 MJ/m³). These values represent 236.9%, 283.4%, and 239.3% enhancements, respectively, compared to the reference BCC lattices with an equivalent density. Z-strut integration induces homogeneous stiffness distribution throughout the lattice architecture, while strut hollowing increases the effective moment of inertia. This structural evolution induces a failure mode transition from single shear band deformation to dual X-shaped shear band propagation, resulting in enhanced deformation sequence regulation within the lattice system. Full article

This work presents an innovative, energy-efficient IoT routing protocol that combines advanced data fusion grouping and routing strategies to effectively tackle the challenges of data management in smart cities. Our protocol employs hierarchical Data Fusion Head (DFH), relay DFHs, and marine predators algorithm, the latter of which is a reliable metaheuristic algorithm which incorporates a fitness function that optimizes parameters such as how closely the Sensor Nodes (SNs) of a data fusion group (DFG) are gathered together, the distance to the sink node, proximity to SNs within the data fusion group, the remaining energy (RE), the Average Scale of Building Occlusions (ASBO), and Primary DFH (PDFH) rotation frequency. A key innovation in our approach is the introduction of data fusion techniques to minimize redundant data transmissions and enhance data quality within DFG. By consolidating data from multiple SNs using fusion algorithms, our protocol reduces the volume of transmitted information, leading to significant energy savings. Our protocol supports both direct routing, where fused data flow straight to the sink node, and multi-hop routing, where a PDF relay is chosen based on an influential relay cost function that considers parameters such as RE, distance to the sink node, and ASBO. Given that the proposed protocol incorporates efficient failure recovery strategies, data redundancy management, and data fusion techniques, it enhances overall system resilience, thereby ensuring high protocol performance even in unforeseen circumstances. Thorough simulations and comparative analysis reveal the protocol’s superior performance across key performance metrics, namely, network lifespan, energy consumption, throughput, and average delay. When compared to the most recent and relevant protocols, including the Particle Swarm Optimization-based energy-efficient clustering protocol (PSO-EEC), linearly decreasing inertia weight PSO (LDIWPSO), Optimized Fuzzy Clustering Algorithm (OFCA), and Novel PSO-based Protocol (NPSOP), our approach achieves very promising results. Specifically, our protocol extends network lifespan by 299% over PSO-EEC, 264% over LDIWPSO, 306% over OFCA, and 249% over NPSOP. It also reduces energy consumption by 254% relative to PSO-EEC, 264% compared to LDIWPSO, 247% against OFCA, and 253% over NPSOP. The throughput improvements reach 67% over PSO-EEC, 59% over LDIWPSO, 53% over OFCA, and 50% over NPSOP. By fusing data and optimizing routing strategies, our protocol sets a new benchmark for energy-efficient IoT DFG, offering a robust solution for diverse IoT deployments. Full article

To address the issues of slow convergence and large errors in existing metaheuristic algorithms when optimizing neural network-based subway passenger flow prediction, we propose the following improvements. First, we replace the random initialization method of the population in the SSA with Circle mapping to enhance its diversity and quality. Second, we introduce a hybrid mechanism combining dimensional small-hole imaging backward learning and Cauchy mutation, which improves the diversity of the individual sparrow selection of optimal positions and helps overcome the algorithm’s tendency to become trapped in local optima and premature convergence. Finally, we enhance the individual sparrow position update process by integrating a cosine strategy with an inertia weight adjustment, which improves the algorithm’s global search ability, effectively balancing global search and local exploitation, and reducing the risk of local optima and insufficient convergence precision. Based on the analysis of the correlation between different types of subway station passenger flows and weather factors, the ISSA is used to optimize the hyperparameters of the CNN-LSTM model to construct a subway passenger flow prediction model based on ISSA-CNN-LSTM. Simulation experiments were conducted using card swipe data from Harbin Metro Line 1. The results show that the ISSA provides a more accurate optimization with the average values and standard deviations of the 12 benchmark test function simulations being closer to the optimal values. The ISSA-CNN-LSTM model outperforms the SSA-CNN-LSTM, PSO-ELMAN, GA-BP, CNN-LSTM, and LSTM models in terms of error evaluation metrics such as MAE, RMSE, and MAPE, with improvements ranging from 189.8% to 374.6%, 190.9% to 389.5%, and 3.3% to 6.7%, respectively. Moreover, the ISSA-CNN-LSTM model exhibits the smallest variation in prediction errors across different types of subway stations. The ISSA demonstrates superior parameter optimization accuracy and convergence speed compared to the SSA. The ISSA-CNN-LSTM model is suitable for the precise prediction of passenger flow at different types of subway stations, providing theoretical and data support for subway station passenger density and trend forecasting, passenger organization and management, risk emergency response, and the improvement of service quality and operational safety. Full article

The optimal performance of direct current (DC) motors is intrinsically linked to their mathematical models’ precision and their controllers’ effectiveness. However, the limited availability of motor characteristic information poses significant challenges to achieving accurate modeling and robust control. This study introduces an approach employing artificial neural networks (ANNs) to estimate critical DC motor parameters by defining practical constraints that simplify the estimation process. A mathematical model was introduced for optimal parameter estimation, and two advanced learning algorithms were proposed to efficiently train the ANN. The performance of the algorithms was thoroughly analyzed using metrics such as the mean squared error, epoch count, and execution time to ensure the reliability of dynamic priority arbitration and data integrity. Dynamic priority arbitration involves automatically assigning tasks in real-time depending on their relevance for smooth operations, whereas data integrity ensures that information remains accurate, consistent, and reliable throughout the entire process. The ANN-based estimator successfully predicts electromechanical and electrical characteristics, such as back-EMF, moment of inertia, viscous friction coefficient, armature inductance, and armature resistance. Compared to conventional methods, which are often resource-intensive and time-consuming, the proposed solution offers superior accuracy, significantly reduced estimation time, and lower computational costs. The simulation results validated the effectiveness of the proposed ANN under diverse real-world operating conditions, making it a powerful tool for enhancing DC motor performance with practical applications in industrial automation and control systems. Full article

In the domain of bionic walking control for biped robots, optimizing the parameters of the central pattern generator (CPG) presents a formidable challenge due to its high-dimensional and nonlinear characteristics. The traditional particle swarm optimization (PSO) algorithm often converges to local optima, particularly when addressing CPG parameter optimization issues. To address these challenges, one improved particle swarm optimization algorithm aimed at enhancing the stability of the walking control of biped robots was proposed in this paper. The improved PSO algorithm incorporates a spiral function to generate better particles, alongside optimized inertia weight factors and learning factors. Evaluation results between the proposed algorithm and comparative PSO algorithms were provided, focusing on fitness, computational dimensions, convergence rates, and other metrics. The biped robot walking validation simulations, based on CPG control, were implemented through the integration of the V-REP (V4.1.0) and MATLAB (R2022b) platforms. Results demonstrate that compared with the traditional PSO algorithm and chaotic PSO algorithms, the performance of the proposed algorithm is improved by about 45% (two-dimensional model) and 54% (four-dimensional model), particularly excelling in high-dimensional computations. The novel algorithm exhibits a reduced complexity and improved optimization efficiency, thereby offering an effective strategy to enhance the walking stability of biped robots. Full article

Seasonal variations in glycaemic patterns in children and young adults affected by type 1 diabetes are currently poorly studied. However, the spread of Flash Glucose Monitoring (FGM) and continuous glucose monitoring (CGM) systems and of dedicated platforms for the synchronization and conservation of CGM reports allows an efficient approach to the comprehension of these phenomena. Moreover, the impact that environmental parameters may have on glycaemic control takes on clinical relevance, implying a need to properly educate patients and their families. In this context, it can be investigated how blood glucose patterns in diabetic patients may have a link to outdoor temperatures. Therefore, in this study, the relationship between outdoor temperatures and glucose levels in diabetic patients, aged between 4 and 21 years old, has been analysed. For a one-year period (Autumn 2022–Summer 2023), seasonal variations in their CGM metrics (i.e., time in range (TIR), Time Above Range (TAR), Time Below Range (TBR), and coefficient of variation (CV)) were analysed with respect to atmospheric temperature. The results highlight a negative correlation between glucose in diabetic patients and temperature patterns (R value computed considering data for the entire year; Ry = −0.49), behaviour which is strongly confirmed by the analysis focused on the July 2023 heatwave (R = −0.67), which shows that during heatwave events, the anticorrelation is accentuated. The diurnal analysis shows how glucose levels fluctuate throughout the day, potentially correlating with atmospheric diurnal temperature changes in addition to the standard trend. Data captured during the July 2023 heatwave (17–21 July 2023) highlight pronounced deviations from the long-term average, signalling the rapid effects of extreme temperatures on glucose regulation. Our findings underscore the need to integrate meteorological parameters into diabetes management and clinical trial designs. These results suggest that structured diabetes self-management education of patients and their families should include adequate warnings about the effects of atmospheric temperature variations on the risk of hypoglycaemia and about the negative effects of excessive therapeutic inertia in the adjustment of insulin doses. Full article