Analysis of the Temperature Dependence of the Capacitance of NiO/Ga2O3 Heterojunction Diodes Using Analytical and PSO Modelling

Satellite imaging consists of capturing images of the Earth through a series of artificial satellites. These images contain an abundance of information that can be used in several applications such as fishing, agriculture, regional planning, biodiversity conservation and many others. Digital image processing can help overcome the limitations of human vision by extracting key information from these images at a much higher rate through the speed of automation. This paper aims to achieve that by exploiting the potential of the Particle Swarm Optimization (PSO) algorithm in image segmentation. Various satellite images were segmented using PSO algorithm before a trace of the objects that have been isolated in the image was run to evaluate the accuracy of segmentation. Three objective measurements which are Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), were made on the outputs of the segmentation using PSO algorithm and the traditional segmentation technique which is Otsu’s method for comparison. The proposed method which applies the PSO algorithm proved to be superior in producing images of higher quality and accuracy as compared to the traditional segmentation technique.

Nowadays the Sport Utility Vehicle (SUV) become more popular than sedan car around the world even in Malaysia. But, these types of vehicles have weaknesses such as higher center of gravity, heavier, side area and wheel base are larger than sedan car that lead to unstable vehicle handling during critical maneuver. Numerous researchers have proposed their control strategy in order to overcome this problem. However, there are less studies about the Linear Quadratic Integral (LQI) controller especially in the Direct Yaw Control (DYC) system. Therefore, in this paper, the development of the LQI controllers implemented in the DYC is researched and compared with Linear Quadratic Regulator (LQR) and Proportional-Integral-Derivative (PID) controller for the performances evaluation. Each controller optimized using Particle Swarm Optimization (PSO) algorithm and tested on lane change maneuver with interference of external disturbance and different road condition. With the help of PSO algorithm, the LQI controllers not only produce significant improvement in the lane change maneuver but the controller is more precise, faster tuning gains, robust against external disturbance and capable to endurance the maneuver with lower Root Mean Square Error (RMSE) compare with two other controllers.

Background/Objectives: PV array being shaded partially by buildings, trees or passing clouds is common. This makes the P-V curve of the PV system complex with more than one peak. MPPT algorithm capable of consistently detecting the global peak within a short duration of time is essential. Methods/Statistical Analysis: Lately Particle Swarm Optimization (PSO) algorithm has been used for Maximum Power Point (MPP) tracking due to its ability to locate the MPP irrespective of its location in the P-V curve. This paper evaluates and compares the performance of the basic PSO algorithm and the modified PSO algorithms for ten different shading patterns. Findings: The basic PSO algorithm is compared with three modified PSO algorithms - PSO algorithm with random numbers eliminated, PSO algorithm with linearly varying constants and PSO algorithm with fixed maximum iterations. The basic PSO algorithm gives good results but random numbers in the algorithm tends to make the convergence time random for the same shading pattern and makes hardware implementation difficult. The PSO algorithm with random numbers eliminated overcomes this disadvantage and is found to give good results. But the convergence time is a little higher and varies with shading pattern. The PSO algorithm with fixed maximum iterations gives good performance with shorter and fixed convergence time. Application/Improvements: PSO algorithm with fixed maximum iterations thus improves the responsiveness of the algorithm to rapidly changing patterns of shading. Keywords: Maximum Power Point Tracking, Partial Shading, Particle Swarm Optimization, PV Array

Particle Swarm Optimization technique has been improved by fractional order calculus to be used for photovoltaic (PV) modeling. The modified technique which is called Fractional Order Darwinian Particle Swarm Optimization (FODPSO) has been constructed to estimate the optimal electrical parameters of PV modules. Single and double diode models have been used to designate the PV modules. FODPSO and PSO algorithms have been designed and applied on two different PV modules at different irradiances and temperatures. In order to validate the proposed modeling technique, Root Mean Square Error (RMSE) of the current, RMSE of power and Summation of the Individual Absolute Error (SIAE) results obtained using FODPSO and traditional Particle Swarm Optimization (PSO) algorithms have been compared. Minimum RMSE and SIAE have been achieved using the FODPSO technique. To verify the FODPSO results accuracy, accurate measurements of short circuit current, open circuit voltage, and maximum power, voltage at maximum power and current at maximum power have been performed for both PV modules. FODPSO-estimated results show excellent agreement with the experimental ones at different irradiances and temperatures.

In this paper, a new particle swarm optimization particle filter (NPSO-PF) algorithm is proposed, which is called particle cluster optimization particle filter algorithm with mutation operator, and is used for real-time filtering and noise reduction of nonlinear vibration signals. Because of its introduction of mutation operator, this algorithm overcomes the problem where by particle swarm optimization (PSO) algorithm easily falls into local optimal value, with a low calculation accuracy. At the same time, the distribution and diversity of particles in the sampling process are improved through the mutation operation. The defect of particle filter (PF) algorithm where the particles are poor and the utilization rate is not high is also solved. The mutation control function makes the particle set optimization process happen in the early and late stages, and improves the convergence speed of the particle set, which greatly reduces the running time of the whole algorithm. Simulation experiments show that compared with PF and PSO-PF algorithms, the proposed NPSO-PF algorithm has lower root mean square error, shorter running time, higher signal-to-noise ratio and more stable filtering performance. It is proved that the algorithm is suitable for real-time filtering and noise reduction processing of nonlinear signals.

The present research introduces the best architectural relevance vector machine (RVM) model for predicting the compaction parameters of soil. The two types of RVM models, i.e., single kernel function-based (SRVM) and dual kernels (parallel) function-based (DRVM), have been constructed in this study. However, the RVM is a kernel function-based approach. Therefore, linear, gaussian, laplacian, and polynomial kernel functions have been implemented in these models. Each model has been optimized by each Genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. For this purpose, 59 soil samples have been collected from the literature. The root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) statistical tools have been used to measure the performance and accuracy of models. From the overall analysis, models MC10 and MD12 have predicted OMC (RMSE = 0.8194%, R = 0.9956, MAE = 0.7920%) and MDD (RMSE = 0.1310g/cc, R = 0.9941, MAE =0.0008g/cc) better than other RVM models. It has also been observed that the DRVM model predicts the compaction parameters better than the SRVM models. The GA algorithm is robust in predicting OMC prediction, and the PSO algorithm is robust in MDD prediction. The score analysis also confirms the robustness of the dual kernel function based DRVM models for predicting OMC and MDD of soil. The sensitivity analysis demonstrates that compaction parameter prediction is strongly influenced by the specific gravity, liquid limit, and plasticity index of soil.

In recent years, the modeling and simulation of lithium-ion batteries have garnered attention due to the rising demand for reliable energy storage. Accurate charge cycle predictions are fundamental for optimizing battery performance and lifespan. This study compares particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms in modeling a commercial lithium-ion battery, emphasizing the voltage behavior and the current delivered to the battery. Bio-inspired optimization tunes parameters to reduce the root mean square error (RMSE) between simulated and experimental outputs. The model, implemented in MATLAB/Simulink, integrates electrochemical parameters and estimates battery behavior under varied conditions. The assessment of terminal voltage revealed notable enhancements in the model through both the PSO and GWO algorithms compared to the non-optimized model. The GWO-optimized model demonstrated superior performance, with a reduced RMSE of 0.1700 (25 °C; 3.6 C, 455 s) and 0.1705 (25 °C; 3.6 C, 10,654 s) compared to the PSO-optimized model, achieving a 42% average RMSE reduction. Battery current was identified as a key factor influencing the model analysis, with optimized models, particularly the GWO model, exhibiting enhanced predictive capabilities and slightly lower RMSE values than the PSO model. This offers practical implications for battery integration into energy systems. Analyzing the execution time with different population values for PSO and GWO provides insights into computational complexity. PSO exhibited greater-than-linear dynamics, suggesting a polynomial complexity of O(nk), while GWO implied a potential polynomial complexity within the range of O(nk) or O(2n) based on execution times from populations of 10 to 1000.

In gas‐fired power plants, emissions may reduce turbine blade rotation, thus decreasing power output. This study proposes a hybrid model integrating the Feed forward Neural Network (FFNN) model and Particle Swarm Optimization (PSO) algorithm to predict gas emissions from natural gas power plants. The FFNN predicts gas turbine nitrogen oxides (NOx) and carbon monoxide (CO) emissions, while the PSO optimizes FFNN weights, improving prediction accuracy. The PSO adopts a unique random number selection strategy, incorporating the K‐Nearest Neighbor (KNN) algorithm to reduce prediction errors. Neighbor Component Analysis (NCA) selects parameters most correlated with CO and NOx emissions. The hybrid model is constructed, trained, and testedusing publicly available datasets, evaluating performance with statistical metrics like Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Results show significant improvement in FFNN training with the PSO algorithm, boosting CO and NOx prediction accuracy by 99.18% and 82.11%, respectively. The model achieves the lowest MSE, MAE, and RMSE values for CO and NOx emissions. Overall, the hybrid model achieves high prediction accuracy, particularly with optimized PSO parameter selection using seed random generators.

This paper presents an intelligent computational method using the PSO (particle swarm optimisation) algorithm to determine the optimum tilt angle of solar panels in PV systems. The objective of the paper is to assess the performance of this method against conventional methods of determining the optimum tilt angle. The method presented here can be used to determine the optimum tilt angle at any location around the world. In this paper, it was applied to Brunei Darussalam, and succeeded in computing monthly optimum tilt angles, ranging from 34.7ᵒ in December to -26.7ᵒ in September. Results showed that changing the tilt angle every month, as determined by the PSO algorithm, increased annual yield by: (i) 5.94%, compared to keeping it fixed at 0ᵒ, (ii) 8.65%, compared to Lunde’s method and (iii) 17.31%, compared to Duffie and Beckman’s method. Benchmark test functions were used to compare and evaluate the performance of the PSO algorithm with the artificial bee colony (ABC) algorithm, another metaheuristic algorithm. The tests revealed that the PSO algorithm outperformed the ABC algorithm, exhibiting lower root mean square error and standard deviation, better convergence to the global minimum, more accurate location of the global minimum, and faster execution times.

Accuracy prediction of the yield strength and displacement of reinforced concrete (RC) columns for evaluating the seismic performance of structure plays an important role in engineering the structural design of RC columns. A new hybrid machine learning technique based on the least squares support vector machine (LSSVM) and the particle swarm optimization (PSO) algorithm is proposed to predict the yield strength and displacement of RC columns. In this PSO-LSSVM model, the LSSVM is applied to discover the mapping between the influencing factors and the yield strength and displacement, and the PSO algorithm is utilized to select the optimal parameters of LSSVM to facilitate the prediction performance of the proposed model. A dataset covering the PEER database and the available experimental data in relevant literature is established for model training and testing. The PSO algorithm is then evaluated and compared with other metaheuristic algorithms based on the experiment’s database. The results indicate the effectiveness of the PSO employed for improving the prediction performance of the LSSVM model according to the evaluation criteria such as the root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). Overall, the simulation demonstrates that the developed PSO-LSSVM model has ideal prediction accuracy in the yield properties of RC columns.

This study presents the development of predictive models for concrete performance, specifically targeting the compressive strength and slump value, utilizing the quantities of individual raw materials in the concrete mix design as input variables. Three distinct machine learning approaches—Backpropagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF)—were employed to establish the prediction models independently. In the model construction process, the Particle Swarm Optimization (PSO) algorithm was integrated with cross-validation to fine-tune the hyperparameters of each model, ensuring optimal performance. Following the completion of training and modeling, a comprehensive comparison of the predictive accuracy among the three models was conducted, with the aim of selecting the most suitable model for incorporation into an optimized objective function. The findings reveal that among the chosen machine learning techniques, BPNN exhibited superior predictive capabilities for the compressive strength of concrete. Specifically, in the validation set, BPNN achieved a high correlation coefficient (R) of 0.9531 between the predicted and actual outputs, accompanied by a low Root Mean Square Error (RMSE) of 4.2568 and a Mean Absolute Error (MAE) of 2.6627, indicating a precise and reliable prediction. Conversely, for the prediction of the concrete slump value, RF outperformed the other two models, demonstrating a correlation coefficient (R) of 0.8986, an RMSE of 9.4906, and an MAE of 5.5034 in the validation set. This underscores the effectiveness of RF in capturing the complexity and variability inherent in slump behavior. Overall, this research highlights the potential of integrating advanced machine learning algorithms with optimization techniques for enhancing the accuracy and efficiency of concrete performance predictions. The identified optimal models, BPNN for compressive strength and RF for slump, can serve as valuable tools for engineers and researchers in the field of construction materials, facilitating the design of concrete mixes tailored to specific performance requirements.

To address the problem of predicting the state of health (SOH) of lithium-ion batteries, this study develops three models optimized using the particle swarm optimization (PSO) algorithm, including the long short-term memory (LSTM) network, convolutional neural network (CNN), and support vector regression (SVR), for accurate SOH estimation. Key features were extracted by analyzing the temperature, voltage, and current curves of the battery, and health factors with high correlation to SOH were selected as model inputs using the Pearson correlation coefficient. The PSO algorithm was employed to optimize model parameters, resulting in the construction of three predictive models: PSO-LSTM, PSO-CNN, and PSO-SVR. The models were validated using the NASA PCoE battery aging datasets B0005, B0006, and B0007, with prediction accuracy evaluated based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2). Results indicate that the optimized models achieved significant improvements in prediction accuracy, with RMSE and MAE reduced by over 0.5%, a minimum reduction of 38% in MAPE, and R2 exceeding 0.8, demonstrating strong fitting capabilities and validating the effectiveness of the PSO strategy. Among the three models, PSO-LSTM exhibited the best predictive performance, achieving a minimum MAE of 0.67%, RMSE of 0.94%, MAPE of 45.82%, and R2 as high as 0.9298 across the three datasets. These findings suggest that the PSO-LSTM model provides a robust reference for accurate SOH prediction of lithium-ion batteries and shows promising potential for practical applications.

Since two of the most important disadvantages of the classical nonlinear regression methods, such as Levenberg-Marquardt (LM), are to calculate error derivative function and use an initial point to get the results, PSO algorithm, which lies in the category of population based meta-heuristic algorithms, is used in this study to implement nonlinear regression in well test analysis. Root Mean Square Error (RMSE) over pressure and pressure derivative data are used in the cost function formulation and the multi-objective problem is reduced to single objective one by including the weight for each of the cost functions related to pressure and pressured derivative data. The superiority of the procedure developed in this study is verified through a simulated drawdown test and one field case. Error comparison over estimated reservoir parameters and analysis of 95% confidence interval reveal that implemented PSO algorithm can be used accurately to estimate reservoir properties.

Accurate flood forecasting in advance is crucial for planning and implementing watershed flood prevention measures. This study developed the PSO–TCN–Bootstrap flood forecasting model for the Tailan River Basin in Xinjiang by integrating the particle swarm optimisation (PSO) algorithm, temporal convolutional network (TCN), and Bootstrap probability sampling method. Evaluated on 50 historical flood events from 1960 to 2014 using observed rainfall-runoff data, the model showed, under the same lead time conditions, a higher Nash efficiency coefficient, along with lower root mean square and relative peak errors in flood forecasting. These results highlight the PSO–TCN–Bootstrap model’s superior applicability and robustness for the Tailan River Basin. However, when the lead time exceeds 5 h, the model’s relative peak error remains above 20%. Future work will focus on integrating flood generation mechanisms and enhancing machine learning models’ generalisability in flood forecasting. These findings provide a scientific foundation for flood management strategies in the Tailan River Basin.

The non-linearity of Inverse kinematics (IK) equations are complex. A Social Spider Optimization (SSO) and Particle Swarm Optimization (PSO) algorithms are proposed in this paper to solve the IK of Humanoid Robotic Arms (HRA). These optimization algorithms are applied on both right and left arms to find the required angles and desired positions with minimum error. Mathematical model of HRA is simulated depending on Denavit-Hartenberg (D-H) method for each arm in which each arm has five Degree Of Freedom (DOF). Performance of HRA model is tested by many positions to be reach by both arms to obtain which optimization algorithm is better. Comparisons are listed between optimal solution using PSO and SSO algorithms. These optimization algorithms are assessed by calculating the Root Mean Squared Error (RMSE) for the absolute error vector of the positions. Simulations and calculation results showed that RMSE value using SSO is less than RMSE value using PSO. We got the largest RMSE of 0.0864 using PSO algorithm. while the lowest possible error, which is 0.00004 was acquired by SSO algorithm. The Graphical User Interface (GUI) is designed and built for motional characteristics of the HRA model in the Forward Kinematics (FK) and IK.