Search Results (3,946)

Fetal health assessment is a crucial aspect of prenatal care, aimed at the early detection of potential complications to ensure optimal outcomes for both mother and child. Traditional methods, such as the visual analysis of cardiotocography (CTG) data by healthcare professionals, are valuable but often subjective and time-consuming. This work investigates the application of machine learning techniques, with a focus on ensemble learning, to enhance the accuracy and efficiency of fetal health classification based on CTG data. Genetic Algorithm (GA) is employed for optimal feature selection, identifying the most discriminative subset of CTG attributes to improve model performance and reduce computational complexity. We employ a combination of advanced machine learning models, including AdaBoost, Gaussian Naïve Bayes, Decision Tree, k-nearest neighbors (KNN), and Logistic Regression. The top two models were selected based on comprehensive performance metrics using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. These models were then integrated through ensemble learning approaches, such as stacking, Particle Swarm Optimization (PSO) weighted averaging, and soft voting, to improve prediction reliability. Our proposed stacking ensemble model achieves a remarkable accuracy of 97.9%, demonstrating its potential as a robust, data-driven tool for fetal health monitoring and the early identification of at-risk pregnancies. The results indicate that machine learning can effectively complement traditional fetal health assessment methods by providing an objective framework to support clinical decision-making. Full article

Vegetation phenology is a key indicator of ecosystem responses to climate change. This study investigates the spatial-temporal dynamics of vegetation phenology in the Changbai Mountain Nature Reserve from 2001 to 2025 and projects future changes under CMIP6 scenarios using a particle swarm optimization–least squares support vector machine (PSO-LSSVM) model. The results show that SOS exhibits an advancing trend, while EOS is delayed, leading to an overall extension of LOS. Spatially, phenological patterns are strongly controlled by elevation, with higher elevations characterized by later SOS and shorter LOS. Correlation analysis indicates that SOS is primarily driven by spring temperature, whereas EOS is influenced by both temperature and precipitation, showing more complex responses. Notably, a negative relationship between autumn temperature and EOS suggests that factors other than temperature may play an important role. Future projections reveal that phenological changes intensify with increasing emission scenarios. By the end of the 21st century, SOS is projected to advance by 0.8–3.6 days, EOS to be delayed by 0.8–7.4 days, and LOS to extend by 1.6–11.8 days. Vegetation-type-based analysis further demonstrates significant heterogeneity in phenological responses. These findings improve the understanding of vegetation phenology in mountain ecosystems and provide a useful reference for assessing ecosystem responses under future climate change. Full article

In recent years, animation-based systems for human-computer interaction have attracted increasing attention. This work proposes a hybrid framework that combines mathematical modeling and bio-inspired optimization algorithms to generate motion sequences from skeletal data. The framework takes as input a complete skeletal sequence corresponding to a given action and optimizes both the number of key poses and the parameters of a homotopy-based formulation to generate transitions between consecutive poses. A homotopy-based approach is used to compute transitions between selected key poses. The homotopy parameter $\lambda$ serves as an indicator of the completeness of the transition between pairs of key poses. Four nature-inspired optimization algorithms: Genetic Algorithm, Micro Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization were evaluated to determine the number of key poses and homotopy parameters that enable feasible motion generation. Dynamic Time Warping (DTW) is used as an external metric to assess the similarity between generated and reference sequences. It is important to note that Dynamic Time Warping (DTW) should be considered as a sequence similarity measure, as it does not explicitly evaluate perceptual realism or biomechanical plausibility. The framework was evaluated on 18 action sequences, demonstrating its ability to generate feasible motion transitions in 16 of the 18 evaluated actions when using PSO and MicroGA. For each pair of key poses, a fixed number of intermediate frames is generated to provide a uniform temporal discretization of the motion. The results suggest that homotopy-based methods provide a feasible approach for animation-based interaction systems. Full article

This paper presents the Spike Processing Unit (SPU), a digital spiking neuron model based on a discrete-time second-order Infinite Impulse Response (IIR) filter. By constraining filter coefficients to powers of two, the SPU implements all internal operations via shift-and-add arithmetic on 6-bit signed integers, eliminating general-purpose multipliers. Unlike traditional models, computation in the SPU is fundamentally temporal; spike timing emerges from the interaction between input events and internal IIR dynamics rather than signal intensity accumulation. The model’s efficacy is evaluated through a temporal pattern discrimination task. Using Particle Swarm Optimization (PSO) within a hardware-constrained parameter space, a single SPU is optimized to emit pattern-specific spikes while remaining silent under stochastic noise. Results from cycle-accurate Python simulations and synthesizable VHDL implementations indicate that the learned temporal dynamics are preserved under hardware-constrained digital execution, supporting the feasibility of the proposed approach. This work demonstrates that discrete-time IIR-based neurons enable reliable temporal spike processing under strict quantization and arithmetic constraints. Full article

Particle swarm optimization (PSO) is an optimizing method that is based on the theory of swarm intelligence. PSO is an effective algorithm that is used to search in a parallel manner compared to other methods. However, PSO has a tendency towards local optima when tackling complex multimodal optimization problems. It also has the disadvantages of slow convergence process and poor stability in the latter evolutionary period. In view of these demerits, a hybrid PSO method based on chaotic opposition-based initialization and an adaptive learning strategy is presented in this work (abbreviated as ACMPSO). First, the chaos initialization and opposition-based learning (OBL) are employed to produce high-quality initial particles in the feasible region, which is able to improve the quality of the initial solutions. Second, the logistic mapping embedded inertia weight is formulated to better trade off the global and local search process. Third, the global optimal particle is regulated by an exclusive velocity and position updating strategy whereas the rest particles are adjusted by the standard updating mechanism so as to prevent particles from premature convergence. Furthermore, an adaptive position update paradigm is developed to finely regulate the global exploration and local exploitation. Finally, conducted experiments on CEC’13 and CEC’22 reveal that the proposed ACMPSO outperforms several other advanced PSO variants regarding their convergence rate and accuracy. Alternatively, to further illustrate the effect of ACMPSO, we have applied it to two real-world problems, and simulation results ascertain its effectiveness and robustness. Full article

Accurate forecasting of water demand enables optimized peak-load management, alleviating pressure during high-demand periods and improving the operational efficiency of urban secondary water supply systems—a critical component in the energy-efficient and sustainable operation of buildings. However, existing water demand prediction methods in some regions suffer from low accuracy and excessively long prediction cycles, posing challenges for real-time water scheduling in building-scale systems. To address these challenges, this study develops a hybrid predictive framework that integrates a BP neural network with the Gray Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) algorithms for enhanced parameter optimization. Using hourly water consumption data from a representative residential district, the proposed model is compared against standalone machine learning models—Extreme Learning Machines (ELM), Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Model performance is rigorously evaluated using the coefficient of determination, mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NSE). The PSO-GWO-BP hybrid model achieves a predictive accuracy of 97.06%, yielding the lowest MAE, MSE, RMSE, and MAPE, as well as the highest R among all models considered, thereby significantly outperforming the benchmark standalone models. Furthermore, the high-precision short-term prediction outputs enable dynamic regulation of secondary water tank refill thresholds, facilitating refined water allocation and enhanced operational management of building water supply systems. These findings demonstrate the considerable application potential of the proposed hybrid model in enhancing both water resource efficiency and energy utilization performance in the daily operation of green buildings, providing reliable technical support for intelligent and low-carbon building water supply management. Full article

Horizontal wells in bottom-water reservoirs are highly susceptible to water coning during production. Consequently, accurately evaluating the water-control performance of inflow control valves (ICVs) is critical for optimizing completion strategies. Conventional semi-analytical models often struggle to capture the transient dynamics of multiphase flow, while standard numerical reservoir simulators fail to explicitly resolve the complex geometries of completion hardware. To address these limitations, this study proposes a multiscale composite modeling framework tailored for bottom-water reservoirs. At the near-well scale, a semi-analytical model is developed to characterize wellbore hydraulics and the pressure drops induced by ICV completions. At the reservoir scale, a numerical model is employed to simulate multiphase fluid transport, with the two scales coupled via cross-scale pressure field mapping. Validation against NETool software under steady-state conditions confirms the physical consistency of the near-well model in determining zonal flow allocation. Comparisons with conventional equivalent well numerical models demonstrate that the proposed composite model offers superior resolution of ICV-induced flow redistribution, yielding distinct production performance profiles. Furthermore, the integration of a Particle Swarm Optimization (PSO) algorithm enables the dynamic optimization of ICV settings. Results indicate that this composite framework provides a robust theoretical and computational basis for designing and evaluating intelligent water-control completions in bottom-water reservoirs. Full article

In smart manufacturing, the injection molding industry faces a “data scarce environment” due to prohibitive physical trial costs. Processing recycled polypropylene (rPP) exacerbates this challenge, as traditional response surface methodology (RSM) fails to capture complex non-linear rheological behaviors induced by material variability. This study proposes a “domain-knowledge guided data augmentation framework,” integrating Taguchi experimental data (L₂₅) with Moldex3D digital twin simulations to construct a 300-sample hybrid dataset. A back-propagation neural network (BPNN) with L₂ regularization was employed for small-sample learning, providing a continuous differentiable physical mapping. To rigorously prevent neighborhood data leakage, the model was evaluated via a strict nested group-based 5-fold cross-validation. Particle swarm optimization (PSO) was coupled to overcome the local minima of gradient descent. Comparative analysis demonstrates that BPNN significantly outperforms both traditional RSM and a newly introduced Random Forest (RF) baseline, achieving a testing mean squared error (MSE) of 0.001 (±0.0002) and a testing R² of 0.95. PSO minimized the shrinkage rate to 3.079%, validated via Moldex3D digital twin simulation with a 0.19% relative error. Synergizing virtual–physical integration with robust neural computing enables superior process control precision in small-data regimes, offering small and medium-sized enterprises (SMEs) a cost-effective pathway for smart optimization. Full article

This study investigates the problem of profit maximization in a retrial queueing-inventory system. Customers who arrive at the system when there is no stock enter a retrial orbit and are treated as retrial demands. We consider two strategies for inventory replenishment: the base stock policy and the (s, S) policy. For each strategy, we first formulate the fundamental equations needed to determine the rate matrix and the steady-state probabilities. Then, we compute the system’s performance metrics and profit function. Moreover, by leveraging particle swarm optimization (PSO) and genetic algorithm (GA), we introduce an improved hybrid optimization algorithm, Improved Hybrid Particle Swarm optimization (IHPSO), to solve the profit maximization problem. This algorithm initially uses PSO, followed by GA crossover and mutation to improve performance. In comparison to the canonical PSO algorithm (CPSO), our algorithm exhibits superior global search capabilities. Finally, we conduct a numerical analysis on the optimal decision variables and the corresponding profits utilizing the IHPSO algorithm and present several interesting findings. Full article

Millimeter-wave (mmWave) unmanned aerial vehicle (UAV) networks are a promising solution for rapidly deployable backhaul systems in urban disaster scenarios, where terrestrial infrastructure may become unavailable. Although mmWave bands provide wide bandwidth for high-capacity transmission, their strong susceptibility to blockage and beam misalignment poses significant challenges in dense urban environments, particularly under UAV positional fluctuations caused by wind. This study investigates the optimization of multi-hop mmWave UAV backhaul networks with the objective of maximizing the bottleneck link capacity. A three-dimensional urban model of the Shinjuku area in Tokyo is employed, and radio propagation is evaluated using a ray-tracing-based approach considering line-of-sight (LoS) constraints and inter-link interference. Particle Swarm Optimization (PSO) is used to determine optimal UAV placements for two- to four-hop configurations. Numerical results demonstrate that multi-hop relaying combined with directional 2 × 2 patch antennas significantly improves the minimum link capacity, enabling the target backhaul capacity of approximately 9 Gbps per link under static conditions. However, capacity degradation is observed when UAV jitter is introduced due to LoS blockage and beam misalignment. To address this issue, a jitter-aware optimization method incorporating an expanded Fresnel-zone constraint is proposed. The proposed method substantially mitigates capacity degradation under realistic positional fluctuations, resulting in more robust backhaul performance. These findings demonstrate that jitter-aware placement design is essential for realizing reliable high-capacity mmWave UAV backhaul networks in dense urban disaster environments. Full article

Conventional fixed phase-shift strategies for parallel PV converters fail to minimize output ripple under heterogeneous input conditions, while communication-based synchronous methods incur high costs and reliability risks. Furthermore, standard global optimization algorithms like conventional Particle Swarm Optimization (PSO) suffer from slow convergence, hindering real-time application. To address these limitations, this paper proposes a communication-free distributed ripple suppression method based on an enhanced PSO with randomized phase shifting. Unlike traditional approaches, our method enables autonomous convergence without inter-unit communication. Crucially, a randomized pre-scanning mechanism narrows the search space, accelerating convergence significantly. Simulation results demonstrate that the proposed method reaches a steady state in merely 5 ms, which is 50% faster than conventional PSO (~10 ms) and eliminates communication latency. Under severe heterogeneous conditions, the technique reduces output voltage ripple to 0.66 V (a 53% reduction) compared to the unoptimized 1.21 V, vastly outperforming fixed interleaving strategies that show negligible improvement. The approach also ensures robust stability during load steps and plug-and-play operations, offering a superior low-cost and high-speed solution for distributed PV systems. Full article

In the parallel operation of islanded microgrids, line impedance mismatches and random load fluctuations, along with the dynamic response lag and difficulty in multidimensional parameter tuning of traditional control strategies, lead to power sharing imbalances and instability in frequency and voltage. To address these issues, this paper proposes a hierarchical cooperative control strategy based on consistency that integrates load-observation-based dynamic reference feedforward (LODRF) and adaptive particle swarm optimization (APSO). First, an improved adaptive virtual impedance (IAVI) strategy based on consistency is introduced into the virtual synchronous generator control framework. Second, an LODRF mechanism is applied at the secondary control layer to actively reconstruct the power baseline by observing the load status at the point of common coupling (PCC) in real time. Furthermore, an APSO algorithm utilizing the integral of time-weighted absolute error (ITAE) as a global performance index is constructed to optimize key proportional–integral controller parameters cooperatively. Simulation results from a four-unit heterogeneous parallel system in MATLAB/Simulink demonstrate that the IAVI strategy enables stable convergence of frequency and voltage and proportional power sharing. Compared with the system without LODRF, the proposed strategy reduces maximum frequency and voltage dynamic deviations under load disturbances by 78.5% and 53.3%, respectively, and shortens effective recovery times by 0.01 s and 0.09 s, respectively. Moreover, compared with the standard PSO algorithm, the APSO-optimized system reduces maximum frequency and voltage deviations by 3.1% and 36.4%, respectively. Additionally, average active and reactive power sharing errors in the steady state are kept below 0.9%, verifying the significant advantages of the strategy in improving dynamic disturbance rejection and steady-state precision. Full article

Quadrotor UAVs present a significant control challenge due to their underactuated nature; strong coupling effects; nonlinear dynamics; and high sensitivity to unknown effect parameters, external disturbances, and uncertainties. To address this issue, this study proposes a Lyapunov-based backstepping (LYP) controller that ensures robust stability and precise trajectory tracking. The controller employs an inner- and outer-loop architecture for coupled position and attitude control. Its performance is compared with Proportional–Integral–Derivative (PID) and Fractional-Order PID (FOPID) controllers under three scenarios: nominal conditions, external disturbances, and model parameter uncertainties. All controller gains are optimized using Particle Swarm Optimization (PSO). Simulation results, which are evaluated using time-domain metrics and root mean square error (RMSE), demonstrate that the proposed LYP controller achieves superior robustness, faster disturbance rejection, and improved tracking accuracy compared to both PID and FOPID controllers. Full article

The aim of this work is to explore a load frequency control (LFC) strategy in micro hydro power plants (MHPPs). Using MATLAB/Simulink, we examined several variants of genetic algorithms (GAs), including Roulette, Tournament, and Uniform, which are utilized to optimize tuning proportional integral derivative (PID) parameters by addressing the problem of instability caused by load variations. The performances are compared with conventional PID methods and other advanced techniques like particle swarm optimization (PSO), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANN) algorithms for both single and dual-area MHPP systems. The results show that the GA-optimized PID controller with the roulette wheel achieves the fastest settling time of 0.3 s and the smallest undershoot of 0.015 pu in the single area. Also, optimizing GA demonstrates superior performance in the dual area, with the fastest settling times of 2.5 s for both Roulette and Uniform. In contrast, PSO is slower than GA, and conventional PID requires a much longer settling time of 19.8 s, a similar result occurring in the dual area. These findings confirm the effectiveness of the GA-optimized PID controller, especially the Roulette variant, as a reliable and fast solution for maintaining frequency stability in MHPPs. Full article

The widespread integration of wind energy conversion systems has fundamentally reshaped modern power grid architecture. However, the limited dynamic response of wind turbine (WT) converters during grid faults—particularly their inability to provide sufficient reactive current and maintain voltage stability under severe dips—necessitates a redefinition of the conventional low-voltage ride-through (LVRT) curve. This study addresses this challenge by proposing a Mutation-Adaptive Mean Variance Mapping Optimization (A-MVMO) algorithm for the control of grid-side converters (GSCs) in wind farms (WFs). To systematically assess post-fault voltage recovery, a Time-Segmented Analysis for Voltage Recovery (T-SAVR) approach is developed with a multi-objective function. The performance of the proposed A-MVMO is benchmarked against standard MVMO and conventional particle swarm optimization (PSO) under both moderate (0.7 pu) and severe (0.15 pu) voltage dips using the IEEE 39-bus system implemented in DIgSILENT/PowerFactory. The results demonstrate that A-MVMO achieves fast, oscillation-free voltage recovery with negligible overshoot (<1%) and lower current injection than PSO and MVMO, while satisfying all engineering constraints. Moreover, the co-optimization of Park-level and turbine-level controllers ensures seamless coordination, as evidenced by the close tracking between the farm-wide reactive power reference and the aggregated turbine response. The T-SAVR method proves essential for focusing optimization on controllable recovery dynamics, yielding a superior LVRT curve. Full article