Search Results (975)

Industrial waste heat and renewable thermal sources below 350 °C can be stored and dispatched through heat pump–organic Rankine cycle (HP-ORC) pumped thermal energy storage (PTES), yet its performance is governed by a series of coupled thermodynamic and operational variables whose relative importance to competing objectives remains poorly quantified. This study, for the first time, applies a weighted neighborhood rough set (WNRS) algorithm to rank the significance of fifteen continuous decision variables of a latent-storage HP-ORC PTES system with respect to electrical-power-to-power ratio, exergy efficiency, levelized cost of storage (LCOS), and life-cycle carbon intensity (CI). The WNRS-reduced feature subsets, with neighborhood radius calibrated by KNN and SVM cross-validation, are then optimized through a two-stage particle swarm optimization (PSO)–technique for order of preference by similarity to ideal solution (TOPSIS) framework. Results show that the four objectives are governed by distinct parameter subsets: electrical-power-to-power ratio depends on a compact set of cycle temperatures, LCOS on heat-source-related operating variables, and exergy efficiency on a broader set including heat-transfer matching parameters, while CI on the mass flow rates. The HP evaporating temperature emerges as the dominant coupling variable. For the composite TOPSIS objective, the eight-variable reduct selected for the composite objective recovers about 69% of the 11-feature improvement while markedly reducing dimensionality. An independent benchmark confirms that the WNRS reduction is physically interpretable rather than purely statistical. Full article

The interest in maximizing sustainable energy sources has recently increased worldwide as a result of an increase in energy demand. For nations with coastal boundaries, electric energy generation from sea or ocean waves represents a possible source of sustainable energy. It turns out that sea wave frequencies as well as heights may differ from one geographical location to another. In other words, maximization of sea wave energy harvesting necessitates tailoring the harvester to the expected coastal conditions. This paper presents a design optimization of a coreless permanent magnet linear generator for southern Mediterranean Sea wave energy conversion. In this work, sea wave data of the aforementioned Mediterranean Sea zone are taken into consideration in the design of the coreless permanent magnet generator. Design and simulation of the generator are carried out using a precise two- and three-dimensional analytical formulation, and optimization is carried out using the particle swarm (PSO) and genetic algorithm (GA) optimization techniques. These simulation results offer qualitative and quantitative insights on numerous aspects, including induced voltage and output power. A coreless sea wave generator design, achieved using three-dimensional analytical computations and PSO, capable of generating 116.54 V and 197.4 W ais presented in the paper. This design offers a maximum induced voltage per unit volume and unit velocity of about 67 K. Details of the design, simulations, and comparisons with samples of similar previously published generators are given in the paper. Full article

The three-dimensional fast Fourier transform (3D FFT) spectral analysis approach is the mainstream technique for retrieving significant wave height (SWH) from X-band marine radar image sequences by using the extracted wave signal-to-noise ratio (SNR). Nevertheless, an ideal analysis sub-region cannot be stably acquired in nearshore field observations due to terrain occlusion and variations in wave direction, which leads to azimuth distortion of the extracted SNR. The backscatter intensity and shadow probability of sea waves in radar images present strong directional correlations with wave direction. To solve these problems, wave direction is introduced as a correction factor to compensate for distorted SNR and shadow probability values, and a composite feature vector integrating multi-window SNR, shadow probability, and wave direction is constructed as the input of a particle swarm optimization support vector regression (PSO-SVR) model for SWH inversion. Conventional SVR relies on grid search for hyperparameter optimization, which limits inversion accuracy. PSO is adopted to seek suitable hyperparameters of the SVR model. Field radar datasets collected at Pingtan Island, together with synchronous buoy measurements serving as ground truth, are adopted to quantitatively evaluate the proposed method. Experimental results show that the proposed method, using multi-feature combination and the PSO-SVR model, improves inversion precision, which provides an accurate and robust SWH inversion scheme for practical shore-based radar monitoring without ideal analysis regions. Full article

The major challenge in photovoltaic (PV) maximum power point tracking (MPPT) systems is finding a balance between the high performance of artificial intelligence techniques and the interpretability and reliability of physics-based approaches. This paper proposes a new MPPT controller based on a combination of Lagrangian and artificial intelligence techniques. In the proposed method, power maximization is modeled as a Lagrangian system, and the duty cycle is determined by physics equations. An artificial neural network is utilized to adaptively adjust the parameters of inertia and damping in real-time based on an eight-dimensional feature vector. Simulation results for step changes, ramp changes, and partial shading conditions confirm the effectiveness of the approach. The controller has 99.7% tracking efficiency in 18.2 ms, which is superior to P&O (55 ms), INC (45 ms), PSO (28.3 ms), and conventional ANN (22.5 ms). Under partial shading conditions, the controller correctly identifies the global maximum power point. The steady-state ripple is very low (±0.1 W), and the transient energy losses are significantly reduced compared to the benchmark algorithms. The results confirm that the integration of Lagrangian dynamics with adaptive neural tuning provides a systematic and efficient approach for designing reliable PV energy systems, effectively bridging the gap between data-driven and physics-based methods. Full article

In order to improve maximum power point tracking (MPPT) and energy extraction in grid-connected photovoltaic (PV) systems under various climatic conditions, this research proposes a Secretary Bird Optimization-tuned Super-Twisting Sliding Mode Controller (SBOA–STSMC). The efficacy of traditional MPPT and adaptive control techniques is frequently compromised by nonlinear dynamics, abrupt changes in irradiance, and partial shading. In order to overcome these restrictions, the suggested method uses the Secretary Bird Optimization Algorithm (SBOA) to optimize STSMC parameters, resulting in improved disturbance rejection and transient response. Five increasingly difficult simulation scenarios are used to evaluate the controller. A single-array grid-connected system under stepwise irradiance fluctuations, combined irradiance–temperature disturbances, severe atmospheric dynamics, and low-irradiance operation are examined in Scenarios 1–4. The performance of adaptive controllers tuned using the Harmony Search Algorithm (HSA) and Invasive Weed Optimization (IWO) is compared with the traditional Incremental Conductance approach. In the fifth scenario, which deals with dynamic partial shading in a dual-array arrangement, the suggested approach is contrasted with HSA-based and IWO-based adaptive controllers and the traditional Perturb and Observe (P&O) technique. Furthermore, a sensitivity analysis is carried out for ±50% parameter modifications, demonstrating a small change in the total injected energy. An additional severe-disturbance test under rapid irradiance variations is performed, together with a ±50% sensitivity analysis of the grid-side choke inductance under the same severe profile. Statistical repeatability is further evaluated through 30 independent runs of the SBOA. A separate statistical comparison based on 30 independent runs of SBOA, TLBO, and PSO is also performed using the Wilcoxon rank-sum test, supporting the superior and consistent performance of SBOA. In addition, real-time validation is conducted using a Speedgoat real-time platform to demonstrate the practical implementation capability of the proposed controller. The results confirm improved dynamic response, reduced oscillations, enhanced robustness, and increased energy injected into the grid across all considered operating conditions. Full article

It is universally acknowledged that water is essential for the survival of humanity. Therefore, the effective utilization and sustainability of water resources are of paramount importance. Driven by this necessity, this study develops predictive models for the Çubuk2 Dam, which supplies drinking water to Ankara, using historical data on temperature, precipitation, humidity, reservoir volume, and water level. The models were constructed using deep learning, optimization, and wavelet decomposition techniques, which have garnered significant attention from researchers in recent years. Specifically, Recurrent Neural Network (RNN), Random Forest (RF), Particle Swarm Optimization (PSO), and Tunable Q-Factor Wavelet Transform (TQW) methods were utilized. RNN was employed both as a standalone model and hybridized as RNNRF and RNNPSO, with TQW applied to all models to enhance predictive performance. Furthermore, ten different input scenario structures were established using Mutual Information (MI). To evaluate model performance, the Correlation Coefficient (R), Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), Performance Index (PI), and Root Mean Square Error (RMSE) metrics were adopted. The results demonstrated that the hybrid models yielded highly effective outcomes and that Mutual Information successfully identified optimal model input structures. Full article

As the core equipment integrating primary electrical devices with secondary intelligent control units, the control cabinet plays a vital role in smart substations, and its operating reliability is crucial to the security and stability of the entire power grid. In order to solve the problems of low accuracy and insufficient generalization ability of traditional control cabinet fault diagnosis schemes, this paper proposes a fault diagnosis method based on QPSO-KELM. Firstly, the structure of the control cabinet and the current characteristics of the switching coil are introduced. Then, by combining the global optimization capability of the QPSO with the nonlinear feature extraction advantages of the KELM, a fault diagnosis method for the control cabinet is proposed. Finally, the coil current signals collected by the operating mechanism of the integrated primary and secondary switches in the control cabinet are extracted for experimental analysis to validate the proposed model. Compared with the existing techniques, the proposed approach not only raises the diagnostic accuracy but also shortens the convergence time considerably in the fault-diagnosis task of the primary–secondary integrated switch housed in the control cabinet. It achieves a diagnostic accuracy of 97.5% and saves 13.55 ms in a single training time compared with PSO-KELM, providing a new approach for the intelligent operation and upkeep of substation assets. Full article

Cardiovascular Disease (CVD) is a prevailing issue across the world. It requires an accurate method for diagnosing in order to treat the disease effectively. The use of machine learning (ML) techniques has gained popularity for diagnosing CVDs. Some existing ML models have demonstrated good results using the process of manual hyperparameter tuning. But manual hyperparameter tuning is a very tedious task that may cause fluctuations in diagnosis accuracy. To construct a PSO-tuned classifier that provides stable diagnosis for CVD, this paper proposes a robust hybrid framework named PSO-PNN using a particle swarm optimization algorithm for fine-tuning the value of the Smoothing Parameter ($\sigma$) of a probabilistic neural network (PNN). PSO-PNN could be employed as an effective classifier for cardiovascular disease. To validate the effectiveness of PSO-PNN, a dataset obtained from UCI Cleveland database was utilized containing 303 patients’ data. The results indicated that the proposed PSO-PNN framework improved the baseline PNN performance; obtained an accuracy of 91.3%, an ROC-AUC value of 94.8%, and a recall rate of 95.24%; and showed stable predictive performance across 30 independent PSO executions. Additionally, SHAP was applied as a post hoc explainability method to interpret the trained PSO-PNN predictions and identify the contribution of input features. The proposed framework may support future decision-support applications for CVD prediction; however, further external validation is required before practical clinical use can be considered. Full article

Adequate soil quality is essential for ensuring the productivity and sustainability of agriculture, with soil pH being a key variable due to its influence on nutrient availability, microbial activity, and plant development. This study proposes a Mamdani fuzzy inference system (FIS) optimized through metaheuristic algorithms for soil pH prediction in papaya (Carica papaya L.) cultivation, a crop highly sensitive to pH fluctuations within the rhizosphere. Soil temperature and soil moisture were used as independent variables, while the estimated soil pH constituted the dependent variable of the system. Three optimization techniques—genetic algorithms (GAs), Differential Evolution (DE), and Particle Swarm Optimization (PSO)—were evaluated to optimize the membership functions and fuzzy rule base of the Mamdani FIS. Model performance was assessed through 30 independent runs using 1500 records collected from a commercial papaya plantation. Across the 30 independent runs, the GA-optimized model achieved the best overall predictive performance, with a Mean Absolute Error (MAE) of 0.3636 ± 0.0035, Mean Relative Error (MRE) of 0.0554 ± 0.0004, and mean coefficient of determination (r²) of 0.8699 ± 0.0048. The best observed GA values were an MAE of 0.3305, MRE of 0.0513, and r² of 0.8957. In addition, a web-based decision support platform and an automated Telegram alert system were developed for event-driven pH alert notification. The results confirm that GA-optimized fuzzy systems constitute an effective, interpretable, and practical tool for pH-based soil condition monitoring and agronomic decision support in precision agriculture. Full article

Sustainable manufacturing increasingly demands environmentally friendly machining strategies, and dry machining has become recognized as a sustainable alternative to conventional coolant-assisted processes. This study presents a framework built on a machine learning technique for optimizing the dry machining performance of Super Duplex Stainless Steel (SDSS 2507) using textured cutting inserts. Gaussian process regression (GPR) models were developed to predict maximum roughness depth (R_max) and maximum flank wear (VB_max). Gaussian data augmentation was employed to enhance model generalization. The predictive performance was strong, with R² values recorded above 0.95 on testing datasets. To identify optimal machining parameters, GPR was integrated with particle swarm optimization (PSO), enabling independent optimization of R_max and VB_max. The framework achieved reductions of 13.97% in R_max and 30.70% in VB_max compared to experimental benchmarks. The results confirm the effectiveness of data-driven optimization in enhancing surface quality, tool performance, and intelligent machining control. Full article

Classification is a fundamental technique in data mining that predicts categorical labels by analyzing input features. However, training Artificial Neural Networks (ANNs) using traditional methods often encounters challenges, such as getting stuck in local minima and slow convergence. To address these issues, this study proposes a novel hybrid model, IPA-ANN, which integrates the Immune Plasma Algorithm (IPA) to optimize the ANN’s connection weights and biases. The IPA, inspired by the immune plasma treatment process, utilizes a unique donor-receiver mechanism to balance exploration and exploitation in the search space. The proposed model was evaluated on nine benchmark datasets from the UCI repository and compared with 18 state-of-the-art metaheuristic algorithms, including Grey Wolf Optimization (GWO), Differential Evolution (DE), and Particle Swarm Optimization (PSO). Experimental results were analyzed using accuracy, F1-score, confusion matrices, and convergence graphs. The findings indicate that IPA-ANN achieves competitive and stable classification performance across different datasets while demonstrating favorable convergence characteristics in several cases. Furthermore, the study investigates the influence of donor–receiver parameters on the optimization process, highlighting the adaptability of the proposed framework. The reliability of these findings was further examined through repeated stratified 5-fold cross-validation and paired Wilcoxon signed-rank tests with Holm–Bonferroni correction on representative datasets, confirming that a subset of the observed performance differences are statistically significant, and through a computational cost analysis showing that IPA-ANN incurs no additional overhead relative to the majority of the compared algorithms. This study contributes to the literature by presenting the first documented application of IPA in ANN training and by providing a modular infrastructure for future metaheuristic-based ANN optimization studies. Full article

Taiwan has one of the highest dialysis prevalences worldwide, making safe and reliable hemodialysis monitoring a critical sensor-based healthcare challenge. Modern hemodialysis machines integrate heterogeneous multimodal sensors (pressure, flow, conductivity, temperature, and cardiovascular signals), but differences in machine brands, data formats, and privacy constraints hinder centralized learning and robust complication prediction. This work proposes a Medical IoT-oriented federated learning framework, PSOFed-HD, that performs dual-layer Particle Swarm Optimization (PSO) to enhance heterogeneous sensor fusion for predicting dialysis-related hypotension and discomfort events. The events are defined as abnormal blood-pressure states, defined as systolic blood pressure <90 mmHg. Each hemodialysis machine is paired with an edge gateway acting as an FL client, where local PSO optimizes CNN feature weights over non-IID sensor subsets, while the central server applies PSO-driven aggregation to adaptively weight client models according to validation performance. Experiments on real-world hemodialysis datasets with 17 most commonly seen HD physiological features demonstrate that standard FedAvg yields an accuracy of 65.24% and F1-score of 0.5318, server-side PSO improves accuracy to 75.11%, and client-side PSO further raises accuracy to 81.97%. The proposed dual-layer PSO framework achieves the best performance, with 90.56% accuracy and an F1-score of 0.8533, along with superior ROC characteristics (AUC = 0.908) and stable cross-validation across 11 folds. State-of-the-art federated learning techniques for non-IID data such as SCAFFOLD and FedProx are also examined using the same heterogeneous HD dataset. The performance is close to our client-only PSO techniques, proving the merits of our dual-layer PSO architecture. These results confirm that jointly optimizing local feature representations and global aggregation weights enables effective fusion of heterogeneous hemodialysis sensor data under privacy-preserving Medical IoT constraints, providing a practical decision-support approach for real-time complication prediction in dialysis units. Future work will incorporate temporal models such as LSTM or Transformer architectures to achieve early event prediction. Full article

Wireless sensor networks (WSNs) are sophisticated monitoring systems that gather environmental data via wireless sensors. Numerous wireless sensors that have been placed to monitor and gather data on a particular environment make up these networks. Typically, a base station or central node wirelessly collects the data prior to analysis. As the battery in wireless sensors is both non-replaceable and non-rechargeable, it represents a key element. As a result, optimizing energy consumption in WSN has become a growing concern. One of the key challenges is consequently the creation of effective protocols for communication in WSNs. In this article, we provide a novel MSA (Mosquito Swarm Algorithm) technique for cluster formation and data routing. Simulations indicate that our proposed algorithm conserves the energy of the nodes and keeps them running for a greater number of survival rounds compared to LEACH (Low-Energy Adaptive Clustering Hierarchy) by a difference of 70.14%, PSO-R (Particle Swarm Optimization with routing) by a difference of 4.76%, and BA-R (Bat Algorithm with routing) by a difference of 2.52%. Our algorithm provides the highest throughput, surpassing LEACH by approximately 6.38%, PSO-R by almost 1%, and BA-R by 12.67%. Simulations indicate that our algorithm is highly effective at extending network longevity and increasing throughput, making it the preferred option to lower energy consumption in WSNs. Full article

Nonlinear systems of equations arise across engineering, physics, and biological modeling; however, classical Newton-type methods may fail when the initial approximation lies outside the convergence region of the NJN local solver. This work proposes a two-stage hybrid framework that couples Particle Swarm Optimization (PSO) for global exploration with the fifth-order Newton–Jarratt (NJN) iterative method for local refinement. The fifth-order convergence of the NJN phase, established through a complete Fréchet-derivative Taylor expansion with explicitly computed error constants, guarantees rapid local convergence once PSO delivers a sufficiently close starting point. The framework is validated on four test problems with increasing numbers of dimensions ($n=2,5,20,40$): a two-dimensional benchmark algebraic system, a five-dimensional metabolic network model for ethanol production in Saccharomyces cerevisiae, and two large-scale Hammerstein integral equation systems. Over 30 independent runs per method and under the tested conditions, PSO-NJN achieves 100% convergence with mean final residuals of order ${10}^{-14}$–${10}^{-16}$, while pure PSO fails completely on the high-dimensional Hammerstein cases ($n=20,40$) and achieves only 10% success on the metabolic model. These results confirm that combining global metaheuristic search with high-order local refinement yields a robust, scalable solver for complex biological and engineering nonlinear systems, though performance on problems with dense high-dimensional Jacobians may require further adaptation. Full article

The Internet of Medical Things (IoMT) is growing quickly, which has greatly increased the cybersecurity attacks on the healthcare systems. Security techniques used for preventing attacks can improve patient safety, which is most important. As most of the datasets generated by the IoMT have high dimensions, feature selection is needed for accurate identification of data, along with deployment in real-time with limited resources. Moreover, the most influential features need to be identified for intrusion detection. Thus, this paper proposes a novel explainable hybrid framework, DeepMedShield-XAI, using the particle swarm optimization (PSO) method for feature selection and classifying the selected features using deep learning algorithms. The highest performing model among the three deep learning models is the deep neural network (DNN) with 99.68% and 99.87% test accuracy on the CICIoMT2024 and IoMT_TrafficData datasets. The findings of explainable artificial intelligence (XAI) methods reveal that the CICIoMT2024 dataset relies on connection-level features like length, protocol type, and TCP flags, while the IoMT_TrafficData dataset uses flow-based attributes like flow length, byte counts, and packet speeds without a single feature dominating across attack types. The proposed DeepMedShield-XAI framework: the results indicate that cyberattacks and unauthorized access attempts can be detected early by DeepMedShield-XAI, which can substantially improve the security of IoMT devices. This drives research on lightweight, explainable, and real-time security frameworks to protect patient data and ensure healthcare system reliability. Full article