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The phenomenon of artificial cavitation is a significant and practical aspect of reducing drag forces on devices interacting with water. Among various regimes, the supercavity regime, where the entire body or a substantial portion of it is enveloped by a cavity, plays a crucial role. This study investigates the cavitation phenomenon using a combination of numerical experimental methods and machine learning techniques. Specifically, the Support Vector Machine (SVM) classification model is employed to identify the type of artificial cavitation regime and to determine the occurrence of the supercavity regime based on input variables. The findings indicate that the Radial Basis Function (RBF) model outperforms other machine learning models in accurately detecting the cavity regime type, achieving an accuracy of over 95% in identifying the supercavity regime. Additionally, results from the RBF model demonstrate that the supercavity regime is observed at low cavitation numbers, exhibiting the longest cavity length. [ABSTRACT FROM AUTHOR]

Introduction: With the rapid optimization and evolution of various neural networks, the control problem of robotic arms in the area of automation control has gradually received more attention. Methods: To improve the control performance of robotic arms under complex dynamic models, this study proposes an adaptive affective radial basis function network control strategy. Firstly, the kinematic and dynamic mathematical models of the tendon driven robotic arm are constructed. Then, by integrating the affective computing model and the radial basis function network, an adaptive affective radial basis function network control algorithm is constructed. Results and Discussion: The research results indicate that the designed algorithm significantly outperforms the other two compared algorithms in terms of control accuracy and stability. In benchmark performance testing, the designed algorithm has a error accuracy of up to 0.97 and a steady state of up to 0.95. In the simulation results, the maximum torque change of the designed algorithm is only 3.8 Nm, which is much lower than other algorithms. In addition, the control error fluctuation range of this algorithm is between −0.001 and 0.001, almost close to zero error. This study provides a new optimization strategy for precise control of tendon driven robotic arms, and also opens up new avenues for the application of artificial intelligence technology in complex nonlinear system control. [ABSTRACT FROM AUTHOR]

The prediction of rock fragmentation (Fr) is highly beneficial to the optimization of blasting operations in the mining industry. The characteristics of the rock mass, the blast geometry, and the explosive qualities are the primary elements influencing Fr. The methodical explosion of explosives within a rock mass results in the production of smaller rock pieces. This work is a step toward the prediction of the degree of Fr in opencast mining using advanced soft computing (SC) methods like: back propagation neural network (BPNN), k-nearest neighbor (KNN), multilayer perceptron (MLP), radial basis function (RBF), multi-variable regression (MVR), gene expression programming (GEP), Takagi-Sugeno fuzzy model (TSF), least-square-support vector machine (LS-SVM), and support vector machine (SVM). A dataset consisting of 219 blasting events with 10 influencing parameters: hole diameter (HDM), spacing (S), burden (B), maximum charge per delay (MCPD), stemming (ST), compressive strength (CS), powder factor (PF), specific drilling (SPD), number of holes (NH), and bench height (BH), were used in the present study. All models were assessed with the help of following performance parameters: RRSE, RSE, NRMSE, RRMSE, MAD, MAPE, MSE, RMSE, and R2. Based on loss function for Fr, scattered diagram, importance ranking, sensitivity analysis, rank analysis, and violin plot the top models were chosen. From the obtained results, it is seen that SVM produce better result compare to other models when predicting the Fr of rock. Under sensitivity analysis, spider diagrams and Tomado diagrams were plotted to determine the variation of input and output factors. The sensitivity analysis of the developed model shows that HDM has the least impact, whereas the parameters B and PF have the maximum impact on the Fr of rock. [ABSTRACT FROM AUTHOR]

With the rapid development of wearable technology, smart contact lenses (SCL) are gradually gaining attention as a breakthrough innovation. The emergence of these products suggests that smart glasses, which incorporate electronic components and visual aids, are expected to become the mainstream of human-computer interaction in the future. However, realizing this vision requires not only advanced electronics but also highly sophisticated manufacturing processes. Therefore, this paper provides an in-depth discussion on the process of manufacturing smart contact lenses using in-mold electronic decoration technology and focuses on the multi-objective problem of optimizing injection parameters such as melt temperature and holding pressure to achieve on micro-molecular displacements as well as residual stresses. First, the background and technical requirements of smart contact lenses are described in detail, emphasizing the prospect of SCL for a wide range of applications in augmented reality, healthcare, and smart assistance. Subsequently, the key role of IME technology in SCL manufacturing is discussed. Focusing on the optimization of melting temperature, holding pressure and holding time, the effects of these three key parameters on eyewear were systematically analyzed with the goal of improving the overall performance and biocompatibility of SCL. The multi-objective optimization of melting temperature and holding pressure was achieved by NSGA-III. Radial basis function interpolation was used as an auxiliary method to provide finer optimization results for NSGA-III. During the multi-objective optimization process, efforts were made to achieve uniform flow of melt temperature and optimal adjustment of holding pressure to maximize the transparency, stability and comfort of SCL. The final results obtained achieved an optimization rate of 95.60% and 93.47% for nodal displacement and residual stress, respectively, compared with the initially recommended process parameters. [ABSTRACT FROM AUTHOR]

Soil fertility is a critical factor in agricultural production, directly impacting crop growth, yield, and quality. To achieve precise agricultural management, accurate spatial interpolation of soil parameters is essential. This study developed a new interpolation prediction framework that combines Radial Basis Function (RBF) neural networks with Inverse Distance Weighting (IDW), termed the IDW-RBFNN. This framework initially uses the IDW method to apply preliminary weights based on distance to the data points, which are then used as input for the RBF neural network to form a training dataset. Subsequently, the RBF neural network further trains on these data to refine the interpolation results, achieving more precise spatial data interpolation. We compared the interpolation prediction accuracy of the IDW-RBFNN framework with ordinary Kriging (OK) and RBF methods under three different parameter settings. Ultimately, the IDW-RBFNN demonstrated lower error rates in terms of RMSE and MRE compared to direct RBF interpolation methods when adjusting settings based on different power values, even with a fixed number of data samples. As the sample size decreases, the interpolation accuracy of OK and RBF methods is significantly affected, while the error of IDW-RBFNN remains relatively low. Considering both interpolation accuracy and resource limitations, we recommend using the IDW-RBFNN method (p = 2) with at least 60 samples as the minimum sampling density to ensure high interpolation accuracy under resource constraints. Our method overcomes limitations of existing approaches that use fixed steady-state distance decay parameters, providing an effective tool for soil fertility monitoring in delta regions. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurately estimating the State of Health (SOH) of batteries is of great significance for the stable operation and safety of lithium batteries. This article proposes a method based on the combination of Capacity Incremental Curve Analysis (ICA) and Whale Optimization Algorithm-Radial Basis Function (WOA-RBF) neural network algorithm to address the issues of low accuracy and slow convergence speed in estimating State of Health of batteries. Firstly, preprocess the battery data to obtain the real battery SOH curve and Capacity-Voltage (Q-V) curve, convert the Q-V curve into an IC curve and denoise it, analyze the parameters in the IC curve that may serve as health features; Then, extract the constant current charging time of the battery and the horizontal and vertical coordinates of the two IC peaks as health features, and perform correlation analysis using Pearson correlation coefficient method; Finally, the WOA-RBF algorithm was used to estimate the battery SOH, and the training results of LSTM, RBF, and PSO-RBF algorithms were compared. The conclusion was drawn that the WOA-RBF algorithm has high accuracy, fast convergence speed, and the best linearity in estimating SOH. The absolute error of its SOH estimation can be controlled within 1%, and the relative error can be controlled within 2%. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In increase of pathogenic bacteria Echrichia coli in the surface water in the river, ground and lakes water is a concern as it is the main precursor to health hazard disinfection in conventional drinking water treatment systems. One possibility of growing interest in water utilities is the technology of riverbank filtration (RBF). RBF is a new method that could introduce non-chemical techniques and natural treatments in Malaysia. Although RBF systems are efficient in reducing or removing the concentrations of contaminants, they are mostly ineffective in the removal of pathogenic bacteria especially during flood and wet seasons. This literature focused on reports published at the last years including the pathogenic bacteria i.e. the total coliform, bacteria E. coli, Giardia lamblia, Leptospira interrogans, Cryptosporidium spp., Enterococci, Cyanobacteria as well as other baceria i.e. Clostridium perfringens. Using this method, the analysis provided an overview of the removal rates of pathogens as the main indicators of BF efficiency. In order to understand and develop further knowledge on RBF, at different locations in Malaysia. Three pilot projects of RBF facilities were constructed in the states of Selangor, Perak, and Kedah. The results from the proposed analytical model are well matched with the data collected from a RBF site in France. After this validation, the model was then applied to the first pilot project of a RBF system conducted in Malaysia. Sensitivity analysis results highlighted the importance of the degradation rates of contaminants on surface water (rivers, lakes and groundwater) quality after removal of pathogens, for which higher utilization rates led to the faster consumption of pollutants. The development perspective of RBF in Malaysia is promising. With the establishment of a management system, improvement of the monitoring system, reinforcement of legal protection, and promotion of civic awareness, Malaysia RBF will play an important role in development of the water resource industry. [ABSTRACT FROM AUTHOR]

Nowadays, the world is struggling with the problems of an aging society. With the increasing share of older people in the population, degenerative joint diseases are a growing problem. The result of progressive degenerative changes in joints is primarily the deterioration of the patients' quality of life and their gradual exclusion from activity and social life. The ability to effectively, non-invasively and quickly detect cases of chondromalacia of the knee joints is a challenge for modern medicine. The possibility of early detection of progressive degenerative changes allows for the appropriate selection of treatment protocols and significantly increases the chances of inhibiting the development of degenerative diseases of the musculoskeletal system. The article presents a non-invasive method for detecting degenerative changes in the knee joints based on recurrence analysis and classification using neural networks. The result of the analyses was a classification accuracy of 91.07% in the case of multilayer perceptron (MPL) neural networks and 80.36% for radial basis function (RBF) networks. [ABSTRACT FROM AUTHOR]

The combinations of chemical admixtures play a significant role in producing concrete. Understanding their mechanical properties is crucial for ensuring safety and durability. Among these properties, compressive strength (CS) stands out as the most critical attribute of concrete. This research introduced a distinct concrete mix utilizing a blend of superplasticizers, retarders, and air-entraining agents, designed to meet specific construction requirements for enhanced durability and workability. The study examines the long-term effects of combining various chemical admixtures on the compressive strength of concrete, utilizing advanced experimental data and machine learning models with a level of precision and detail that has been relatively underexplored.This investigation includes a substantial increase in samples (7845) compared to previous research. Samples were tested at different ages, ranging from 3 days to 3 years. To enhance the accuracy of machine learning (ML) models, a novel approach to data distribution simulation based on K-means clustering was employed for generating synthetic data. Various ML models, including Nonlinear Autoregressive with Exogenous Inputs (NARX), Support Vector Regression (SVR), Radial Basis Function (RBF), Multilayer Perceptron (MLP), Decision Tree (DT), and Random Forest (RF), were evaluated for predicting the compressive strength of concrete (CS). Results show the NARX model outperforms the other models, validated by experimental data and k-fold cross-validation. This model showed a coefficient of determination (R2 = 0.9932), normalized mean square error (NMSE = 18.97), normalized mean absolute error (NMAE = 2.49), and normalized root-mean-square error (NRMSE = 6.18). The findings revealed that in Mix - 1, the compressive strength increased from 450 (kg/cm2) at 28 days to 480 (kg/cm2)at 90 days, but then decreased to 420 (kg/cm2) after three years. This reduction in strength may lead to decreased load-bearing capacity and higher repair costs, highlighting the need to revise concrete design standards. This study emphasizes revising some current concrete structure design standards to accommodate the observed long-term reductions in compressive strength.

IntroductionWith the rapid optimization and evolution of various neural networks, the control problem of robotic arms in the area of automation control has gradually received more attention.MethodsTo improve the control performance of robotic arms under complex dynamic models, this study proposes an adaptive affective radial basis function network control strategy. Firstly, the kinematic and dynamic mathematical models of the tendon driven robotic arm are constructed. Then, by integrating the affective computing model and the radial basis function network, an adaptive affective radial basis function network control algorithm is constructed.Results and DiscussionThe research results indicate that the designed algorithm significantly outperforms the other two compared algorithms in terms of control accuracy and stability. In benchmark performance testing, the designed algorithm has a error accuracy of up to 0.97 and a steady state of up to 0.95. In the simulation results, the maximum torque change of the designed algorithm is only 3.8 Nm, which is much lower than other algorithms. In addition, the control error fluctuation range of this algorithm is between −0.001 and 0.001, almost close to zero error. This study provides a new optimization strategy for precise control of tendon driven robotic arms, and also opens up new avenues for the application of artificial intelligence technology in complex nonlinear system control.

The development of digital techniques in control engineering leads to the creation of innovative algorithms for measuring specific parameters. In the field of electric power engineering these parameters may be amplitude, phase and frequency of voltage or current occurring in the analyzed electric grid. Thus, the algorithms mentioned, applied in relation to the quoted parameters, may provide precise and reliable measurement results in the electric grid as well as ensure better grid monitoring and security. Signal analysis regarding its identification due to the type of interference is very difficult because the multitude of information obtained is very large. In order to indicate the best method for determining errors in measuring synchronous parameters of the measured current or voltage waveforms, the authors propose in this paper a new form of one error for all testing functions, which is called an equivalent error. This error is determined for each error's value defined in the applicable standards for each of selected 15 methods. The use of the equivalent error algorithm is very helpful in identifying a group of methods whose operation is satisfactory in terms of measurement accuracy for various types of disturbances (both in the steady state and in the dynamic state) that may occur in the power grid. The results are analyzed for phasor measurement unit (PMU) devices of class P (protection) and M (measurement). [ABSTRACT FROM AUTHOR]

Copyright of Water Conservancy Science & Techonlogy & Economy is the property of Water Conservancy Science & Technology & Economy Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cryptocurrencies are digital assets that have attracted a lot of investment and attention. It is challenging and essential for investors and traders to predict their stock price movements. Making accurate predictions about cryptocurrency prices is crucial for avoiding losses and gaining profits. Our research proposes a novel method for predicting the stock closed prices of three popular cryptocurrencies: Bitcoin, Ethereum and Polkadot. The SVR (Support vector regression) machine learning method can provide robust and accurate predictions for nonlinear and nonstationary data. This paper compares SVR radial basis functions (RBFs) and hybrid kernels based on cryptocurrency data characteristics. SVR parameters such as regularization, gamma, and epsilon can also be tuned using grid search. Our approach is tested on real-world cryptocurrency stock prices collected from Yahoo Finance. Prediction performance is measured using regression metrics like MAPE (Mean absolute percentage error) and R² score. In our work, a MAPE value of 0.07772 and an R² score of 0.9999 have been obtained. The results of our experiments indicate that our approach is significantly more accurate and reliable than existing methods. [ABSTRACT FROM AUTHOR]

Copyright of Water Conservancy Science & Techonlogy & Economy is the property of Water Conservancy Science & Technology & Economy Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Active suspension control technologies have become increasingly significant in improving suspension performance for driving stability and comfort. An RBF-based fractional-order SMC fault-tolerant controller is developed in this research to guarantee ride comfort and handling stability when faced with the partial loss of actuator effectiveness due to failure. To obtain better control performance, fractional-order theory and the RBF algorithm are discussed to solve the jitter vibration problem in SMC, and the RBF is exploited to obtain a more appropriate switching gain. First, a half-nonlinear active suspension model and a fault car model are presented. Then, the design process of the RBF-based fractional-order SMC fault-tolerant controller is described. Next, a simulation is presented to demonstrate the effectiveness of the proposed strategy. According to the simulation, the proposed method can improve performance in the case of a healthy suspension, and the fault-tolerant controller can guarantee the capabilities when actuators go wrong. [ABSTRACT FROM AUTHOR]

When solar irradiation is uniform along with the array, the P–V curve represents a unique maximum power point (MPP). If the cells undergo shade conditions in the presence of bypass diodes, the solar array's power is decreased, and the P–V curve of the array represents multiple local MPPs (LMPP) and a global MPP (GMPP). LMPPs might mislead the maximum power point tracking (MPPT) algorithms because their characteristics are identical to the MPP. Various studies have been conducted on partial shading conditions. This study uses parallel distributed maximum power point tracking (DMPPT) due to the advantages of this structure. A high-gain converter is presented to resolve the high conversion gain required by the DC/DC converter in this structure. This study also presents MLP and RBF networks for MPP tracking and compares their efficiencies under the same irradiation and partial shading conditions. Since determining optimal weight coefficients in MLP neural networks and variances, means, and weights in RBF networks play an essential role in their performance, this study uses four optimization algorithms of particle swarm optimization (PSO), gray wolf optimization (GWO), grasshopper optimization algorithm (GOA), and Harris Hawks optimization (HHO). Finally, an adaptive fuzzy-PID controller controls the three-phase grid-connected inverter. A comparison of the results shows that the efficiency of MLP and RBFII is almost the same, about 98–99%. Moreover, the accuracy of MLP networks is higher than RBF, and RBF networks' only advantage is shorter training time. In addition, RBF networks require much more activation functions for proper performance. The simulation outcomes confirm the superior efficacy of the HHO algorithm in training neural networks when compared to alternative algorithms. [ABSTRACT FROM AUTHOR]

This paper studies the influence of scaling on the behavior of radial basis function interpolation. It focuses on certain central aspects, but does not try to be exhaustive. The most important questions are: How does the error of a kernel-based interpolant vary with the scale of the kernel chosen? How does the standard error bound vary? And since fixed functions may be in spaces that allow scalings, like global Sobolev spaces, is there a scale of the space that matches the function best? The last question is answered in the affirmative for Sobolev spaces, but the required scale may be hard to estimate. Scalability of functions turns out to be restricted for spaces generated by analytic kernels, unless the functions are band-limited. In contrast to other papers, polynomials and polyharmonics are included as flat limits when checking scales experimentally, with an independent computation. The numerical results show that the hunt for near-flat scales is questionable, if users include the flat limit cases right from the start. When there are not enough data to evaluate errors directly, the scale of the standard error bound can be varied, up to replacing the norm of the unknown function by the norm of the interpolant. This follows the behavior of the actual error qualitatively well, but is only of limited value for estimating error-optimal scales. For kernels and functions with unlimited smoothness, the given interpolation data are proven to be insufficient for determining useful scales. [ABSTRACT FROM AUTHOR]

Agriculture serves as the predominant driver of a country's economy, constituting the largest share of the nation's manpower. Most farmers are facing a problem in choosing the most appropriate crop that can yield better based on the environmental conditions and make profits for them. As a consequence of this, there will be a notable decline in their overall productivity. Precision agriculture has effectively resolved the issues encountered by farmers. Today's farmers may benefit from what's known as precision agriculture. This method takes into account local climate, soil type, and past crop yields to determine which varieties will provide the best results. The explainable artificial intelligence (XAI) technique is used with radial basis functions neural network and spider monkey optimization to classify suitable crops based on the underlying soil and environmental conditions. The XAI technology would provide assets in better transparency of the prediction model on deciding the most suitable crops for their farms, taking into account a variety of geographical and operational criteria. The proposed model is assessed using standard metrics like precision, recall, accuracy, and F1‐score. In contrast to other cutting‐edge approaches discussed in this study, the model has shown fair performance with approximately 12% better accuracy than the other models considered in the current study. Similarly, precision has improvised by 10%, recall by 11%, and F1‐score by 10%. [ABSTRACT FROM AUTHOR]

Copyright of Food & Machinery is the property of Food & Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study is devoted to analyse of free and forced vibrations and semi-active control vibrations of sandwich microbeam with Functionally Graded Materials (FGM) and viscoelastic/electrorheological (ER) core. The intended model is for top and bottom layers of functionally graded materials with power law and a core model for Viscoelastic materials with complex shear modulus. Hamilton principle is used to determine the governing Equations of motion on the sandwich microbeam based on the modified couple stress theory. Mesh less method of Radial Basis Functions (RBF) is used to calculate natural frequency and the loss factor. All the effects of length scale parameter, shear modulus and changes due to variation of the electric field on the natural frequency and loss factor have been drawn. Combination of RBF method and forward difference led to evaluation of forced vibration and deflection of microbeam for length scale parameters and different electric fields under the dynamic load have been calculated and drawn. The feedback effects are analyzed for vibration amplitudes of sandwich microbeam by using Linear Quadratic Gaussian (LQG) and optimal control method. At the end, the results are compared with papers for different viscoelastic models such as Kelvin model, Bingham plastic model and complex modulus.

Objective: Taking Aksu apples as an example, a joint image optimal feature extraction and improved RBF neural network learning apple weight estimation method is designed to overcome the high cost and large error of manual grading and weighing. Methods: Firstly, an apple image acquisition system was established to obtain apple foreground image information. Secondly, the optimal subset extraction strategy for apple image feature sets was designed, by transforming the process of extracting the optimal subset into an objective function optimization problem, and an improved discrete locust optimization algorithm was designed to obtain the optimal apple image feature subset. Finally, a weight estimation model for apples based on RBF neural network learning was constructed, with the optimal feature subset as network input. The locust optimization algorithm was used to optimize the configuration of RBF neural network hyperparameters, to achieve effective estimation of apple weight. Results: The proposed apple weight estimation method had higher accuracy, with an average relative error rate of 1.23% for weight estimation. Conclusion: This method can effectively achieve apple weight estimation and can also be applied to other fruits with similar axisymmetric shapes for weight estimation.

Inertial navigation system (INS) is often integrated with satellite navigation systems to achieve the required precision at high-speed applications. In global navigation system (GPS)/INS integration systems, GPS outages are unavoidable and a severe challenge. Moreover, because of the usage of low-cost microelectromechanical sensors (MEMS) with noisy outputs, the INS will get diverged during GPS outages, and that is why navigation precision severely decreases in commercial applications. In this paper, we improve GPS/INS integration system during GPS outages using extended Kalman filter (EKF) and artificial intelligence (AI) together. In this integration algorithm, the AI receives the angular rates and specific forces from the inertial measurement unit (IMU) and velocity from the INS at t and t − 1. Therefore, the AI has positioning and timing data of the INS. While the GPS signals are available, the output of the AI is compared with the GPS increment; so that the AI is trained. During GPS outages, the AI will practically play the GPS role. Thus, it can prevent the divergence of the GPS/INS integration system in GPS-denied environments. Furthermore, we utilize neural networks (NNs) as an AI module in five different types: multi-layer perceptron (MLP) NN, radial basis function (RBF) NN, wavelet NN, support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS). To evaluate the proposed approach, we utilize a real dataset that has been gathered by a mini-airplane. The results demonstrate that the proposed approach outperforms the INS and GPS/INS integration systems with the EKF during GPS outages. Meanwhile, the ANFIS also reached more than 47.77% precision compared to the traditional method. [ABSTRACT FROM AUTHOR]

Electromagnetic radiation (EM) pollution has a certain impact on human life and health, and the reconstruction of the EM space field in this paper is of great practical significance for EM analysis and research. The radial basis function (RBF) sufficiently considers the influence of each sampling point and is more suitable for reconstructing the EM space field than other spatial interpolation methods. Currently, when RBF is used to reconstruct the EM space field, the optimal determination of the basis function and shape parameter (SP) is rarely considered. This ultimately leads to low reconstruction accuracy of the EM space field. Therefore, in this paper, the particle swarm optimization (PSO) is used to calculate the optimal SP of the RBF. On this basis, reliable EM space field reconstruction is performed, which helps people understand the EM distribution characteristics in actual situations from a visual perspective. The EM sampling data of a region on the Yunnan Normal University campus are used as the data source, and the RBF under the optimal parameters is used for EM reconstruction. The accuracy of its interpolation results is evaluated and compared and analyzed with inverse distance weighting (IDW) after distance index optimization. The results show that the RBF under optimal parameters reconstructs the EM space field with high accuracy and good effect, which can truly reflect the actual distribution of EM. Electromagnetic radiation (EM) pollution has a great impact on the surrounding environment. Therefore, EM space field reconstruction can help us analyze the characteristics of the electromagnetic environment in a visual way. Radial Basis Function (RBF) is a method more suitable for EM space field reconstruction than other methods because it fully considers the influence of each sampling point. However, when currently using RBF to reconstruct the EM space field, few researchers consider how to choose the most appropriate basis function and shape parameter (SP). This results in low reconstruction accuracy. Therefore, this study uses particle swarm optimization (PSO) to find the optimal SP parameters for reliable EM space field reconstruction. The study used the EM sampling data of an area within the campus of Yunnan Normal University as the study material, and a parameter-optimized RBF method was adopted for the reconstruction of the EM space field. The reconstruction results were then evaluated for accuracy and compared and analyzed with the IDW method optimized with a distance index. Research results show that using RBF with optimal parameters to reconstruct the EM space field has high accuracy and can effectively reflect the actual EM distribution, thereby helping people better understand the characteristics of the electromagnetic environment. [ABSTRACT FROM AUTHOR]

To properly predict the security posture of these networks, we provide a method based on machine learning algorithms to detect the security condition of power information networks. A perception model outlines the consequences of the abstracted perception problem. Sample data is initially pre-processed using linear discriminant analysis methods to optimise the data, get integrated features, and ascertain the best projection. To assess system safety posture and find mapping relationships with network posture values, the cleaned data is subsequently input into an RBF neural network as training data. The reliability of the suggested technique for network security posture analysis is finally shown by simulations using the KDD Cup99 dataset and attack data from power information networks, with detection rates frequently surpassing 90%. [ABSTRACT FROM AUTHOR]

Due to increasing concerns about global warming regarding CO2 release to the atmosphere, various methods are used to capture CO2, among which chemical absorption via amine mixture solutions is very well developed. A set of 179 data related to CO2 absorption in a mixture, including a physical absorbent (sulfolane) and a chemical absorption (AEEA) in a wide range of temperature, pressure and solvent concentration is used to develop two Artificial Neural Networks (ANN). In Multi-Layer Perceptron (MLP), the Levenberg-Marquardt method is used to train the network. Most important factors such as regression analysis value (R2) of 0.99963, Mean Squared Error (MSE) value of 1.22E-05 and Average Absolute Relative Deviation value (%AARD) of 0.2671 factors reveal that the MLP network has a high capability to predict CO2 loading (aCO2). Also, a Radial Basis Function (RBF) network was developed. RBF network with a spread value of 2.2 and 138 neurons had an outstanding performance and achieved an MSE value of 2.53E-05 along with an R2 value of 0.99993, 11 seconds, and a %AARD value of 0.1460. According to experimental and predicted data, the neural networks are well trained and are able to predict CO2 loading precisely in an economic and optimized way. [ABSTRACT FROM AUTHOR]

The temperature system of the Continuous Stirred Tank Reactor (CSTR) has the characteristics of strong nonlinearity and uncertain parameters. The linear PID controller makes it difficult to meet CSTR's control requirements. Nonlinear PID (NPID) can improve the control effect of nonlinear controlled objects, but due to the influence of nonlinear function selection and manual parameter setting, when parameters are uncertain or subject to external interference, the control performance of the system will decrease. To improve the adaptive capability of the NPID controller, the RBF-NPID control algorithm is proposed. The learning ability of RBF neural network is used to adjust NPID parameters online to improve the control performance of the system. In order to verify the effectiveness of the proposed algorithm, a CSTR model was established in MATLAB and algorithm comparison research was carried out. Simulation results show the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The explosive growth of video data poses a significant challenge in retrieving relevant content swiftly. Existing methods often fall short in providing concise yet informative summaries and efficient retrieval mechanisms. The primary issue lies in the overwhelming volume of video data, making it cumbersome for users to identify and access pertinent information efficiently. Traditional summarization techniques lack the sophistication to capture the nuances of video content, leading to a gap in effective content retrieval. Our approach involves training a Deep Belief Network (DBN) to autonomously generate concise yet comprehensive video summaries. Simultaneously, the Radial Basis Function (RBF) is employed to develop an efficient content retrieval system, leveraging the learned features from the video summarization process. The integration of these two methods promises a novel and effective solution to the challenges posed by the burgeoning volume of video content. Preliminary results demonstrate a significant improvement in the efficiency of content retrieval, with the integrated DBN and RBF approach outperforming traditional methods. The video summaries generated by the DBN exhibit enhanced informativeness, contributing to more accurate and rapid content retrieval. [ABSTRACT FROM AUTHOR]