Intelligent Particle Research Articles

Development of “Intelligent particles” for the treatment of dental caries

Dental caries is one of the most prevalent non-communicable diseases worldwide, mediated by a multispecies biofilm that consists of high levels of acidogenic bacteria which ferment sugar to acid and cause teeth demineralization. Current treatment practice remains insufficient in addressing 1) rapid clearance of therapeutic agents from the oral environment 2) destroying bacteria that contribute to the healthy oral microbiome. In addition, increasing concerns over antibiotic resistance calls for innovative alternatives. In this study, we developed a pH responsive nano-carrier for delivery of polycationic silver nanoparticles. Branched-PEI capped silver nanoparticles (BPEI-AgNPs) were encapsulated in a tannic acid − Fe (III) complex-modified poly(D,L-lactic-co-glycolic acid) (PLGA) particle (Fe(III)-TA/PLGA@BPEI-AgNPs) to enhance binding to the plaque biofilm and demonstrate “intelligence” by releasing BPEI-AgNPs under acidic conditions that promote dental caries The constructed Fe(III)-TA/PLGA@BPEI-AgNPs (intelligent particles − IPs) exhibited significant binding to an axenic S. mutans biofilm grown on hydroxyapatite. Ag+ ions were released faster from the IPs at pH 4.0 (cariogenic pH) compared to pH 7.4. The antibiofilm results indicated that IPs can significantly reduce S. mutans biofilm volume and viability under acidic conditions. Cytotoxicity on differentiated Caco-2 cells and human gingival fibroblasts indicated that IPs were not cytotoxic. These findings demonstrate great potential of IPs in the treatment of dental caries.

New intelligent particle swarm optimization algorithm with extreme learning machine for forecasting Pattavia pineapple productivity: case study of Loei and Nong Khai provinces in Thailand

New intelligent particle swarm optimization algorithm with extreme learning machine for forecasting Pattavia pineapple productivity: case study of Loei and Nong Khai provinces in Thailand

Intelligent Particle Swarm Optimization Method for Parameter Selecting in Regularization Method for Integral Equation

We use the Tikhonov method as a regularization technique for solving the integral equation of the first kind with noisy and noise-free data. Following that, we go over how to choose the Tikhonov regularization parameter by implementing the Intelligent Piratical Swarm Optimization (IPOS) technique. The effectiveness of combining these two approaches IPOS and Tikhonov is demonstrated to be highly practicable.

An Optimized Energy and Time Constraints-Based Path Planning for the Navigation of Mobile Robots Using an Intelligent Particle Swarm Optimization Technique

Mobile robots (MRs) typically require running for many hours on one charge of the battery. Electric autonomous mobile robots (AMRs) have become increasingly common in the manufacturing process in the last few years. MRs must often complete difficult assignments while gathering information across an unknown area involving energy constraints and time-sensitive preferences. This paper estimates the information collection assignment for surveillance as a multi-objective optimization dilemma with both energy and time constraints. In this study, three main objectives during acquiring data are taken into consideration, including the greatest quantity of data acquired for surveillance, following a path where obstacles are least likely to be experienced, and traveling the smallest feasible path. To obtain the optimal path for an MR by addressing the presented issue, this approach presents an intelligent particle swarm optimization (PSO) technique that determines fitness value by simplifying the optimization task for achieving the shortest path for MR navigation. It allows particles to execute variable operations while maintaining most of the prior search information. The findings of the simulation show that this technique of the PSO algorithm can realize swift convergence and high accuracy when compared with different benchmark functions derived for PSO. A comparative discussion on various energy-efficient navigation techniques for MRs is also provided. Lastly, this study describes the possible future research directions.

Joint inverse estimation of groundwater pollution source characteristics and model parameters based on an intelligent particle filter

Accurate groundwater pollution source estimation (GPSE) is the prerequisite for efficiently designing groundwater pollution remediation strategies and pollution risk assessment. However, deterministic point estimation of pollution source characteristics may not completely solve the problem for practical situations. Therefore, an interval estimation, capable of providing more comprehensive and abundant information for decision-makers, should also be considered. In GPSE, the particle filter (PF) method is a good means for the interval estimation. However, some inherent problems in the general PF method may compromise the estimation accuracy. Thus, this study made two improvements to the general PF method as a solution. First, to solve the problem of its weak local search ability when tackling complex problems, this study embedded the optimization method with strong local search ability into the importance sampling process, to strengthen the local search ability, better optimize the particle position, and guide the overall distribution of particles to move towards the true distribution. Secondly, in the resampling process, all particles need to be resampled in each iteration, which may lead to plunging into the local optima. Thus, this study introduced an intelligent resampling process that intelligently adjusted the number of resampling particles in each iteration to avoid plunging into the local optima. Through two improvements on the PF method, an intelligent PF method was innovatively constructed for GPSE, improving the estimation accuracy. Additionally, to alleviate the computational cost caused by the frequent simulation model invocation, we adopted a deep learning method (deep belief neural network method) to build the surrogate model of the simulation model, ensuring high approximation accuracy to the simulation model. Also, we applied the theories and methods mentioned above to two respective scenarios (inorganic and organic groundwater pollution). We simultaneously estimated the pollution source characteristics and simulation model parameters to verify the effectiveness. Finally, we obtained the point estimation, posterior probability distribution, and interval estimation results, capable of providing a more reliable and comprehensive reference for decision-makers.

Impact of Various Load Models for Combined Assignment of DG Source and D-STATCOM Device in the Radial Distribution System

This research work is concentrated on swarm-based intelligence Particle Swarm Optimization algorithm for combined assignment of D-STATCOM device and Distributed Generation source in a radial distribution structure. This work intends to diminish total real power loss, total cost and voltage magnitude profile enhancement for different circumstances. Generally Constant Power load design analysis is carried out for a distribution scheme. However, it is observed that load models remarkably impact the optimum sizing and positioning of DG source and D-STATCOM device. In this paper, work has been carried out for constant power load, polynomial load, and load growth model under various load factor conditions from light load factor (0.6) to heavy load factor (1.6) for power system planning. The sizing and positioning of D-STATOM device and DG source are considered based on loss sensitivity factor computation and PSO algorithmic rule. The planned scheme is investigated on IEEE 69 node and IEEE 33 node radial distribution structures. Further, the simulated results obtained by this algorithm is compared with other available techniques.

Application Analysis of Intelligent Particle Swarm Algorithm in the Development of Modern Tourism Intelligence

Information technology has been increasingly vital in the rapid expansion of the tourism industry economy. The new information technology has become a significant driving force and factor in the tourism industry’s economic development. The application of new information technology to the advancement of modern tourist intelligence has emerged as an essential study area. Smart tourism, aided by informatization, can frequently provide more attractive economic benefits to tourism businesses. Tourism is being positioned as a new economic growth point and core business in an increasing number of cities. Various measures have been taken among the main body of the city to improve the tourism competitiveness of the city, so as to be in an advantageous position in the fierce market competition. Under this background, the research on urban tourism competitiveness has become a research hotspot in the development of modern tourism intelligence. This study used a combined PSO and neural network to assess the competitiveness of urban tourism. First, this paper offers a method that makes use of an improved IPSO. It changes the inertia weight dynamically and nonlinearly based on the particle fitness value. Simultaneously, the particle swarm technique is enhanced by combining the particle iterative cycle to boost position disturbance. Second, in order to build an IPSO-BP network, this work optimizes the initial weights and thresholds of the BP network based on IPSO. This work uses this network to evaluate the competitiveness of urban tourism, which can overcome the defects of traditional BP network. Third, this work conducts systematic experiments on the IPSO-BP method, and the experimental results confirm the superiority of this method in the evaluation of urban tourism competitiveness.

Adaptive Power Sharing and Switching Frequency Control for Power Loss Optimization in WBG/Si Hybrid Half-Bridge Converters

The optimization of switching frequencies and the power-sharing ratio between the silicon (Si) phase and wide-bandgap (WBG) phase are critically important for the efficiency improvement and safe operation of the WBG/Si hybrid half-bridge (HHB)-based power converters. In this article, a novel adaptive power sharing and switching frequency control for power loss optimization in WBG/Si HHB is proposed. It is based on intelligence particle swarm optimization (PSO), especially suitable for applications with time-varying operation current. The PSO approach only evaluates the fitness values to seek the best optimal parameters without requiring an accurate power loss model and additional hardware components. Therefore, the proposed method can be easily implemented and adapted to various working conditions. A 3-kW prototype of the Si/SiC HHB-based single-phase inverter is built to validate the proposed approach. In comparison with the fixed frequency and power-sharing ratio, the proposed method achieves an 18% maximum total power loss reduction while maintaining the same power quality performance.

Goal-Oriented Tuning of Particle Filters for the Fault Diagnostics of Process Systems

This study introduces particle filtering (PF) for the tracking and fault diagnostics of complex process systems. In process systems, model equations are often nonlinear and environmental noise is non-Gaussian. We propose a method for state estimation and fault detection in a wastewater treatment system. The contributions of the paper are the following: (1) A method is suggested for sensor placement based on the state estimation performance; (2) based on the sensitivity analysis of the particle filter parameters, a tuning method is proposed; (3) a case study is presented to compare the performances of the classical PF and intelligent particle filtering (IPF) algorithms; (4) for fault diagnostics purposes, bias and impact sensor faults were examined; moreover, the efficiency of fault detection was evaluated. The results verify that particle filtering is applicable and highly efficient for tracking and fault diagnostics tasks in process systems.

Balancing and Tracking Control of Ballbot Mobile Robots Using a Novel Synchronization Controller Along With Online System Identification

Ballbots are omnidirectional self-balancing platforms that can be exploited in many applications to detect, track, or interact with objects or humans, such as a service robot. Ballbot will enable mobile robots to stand tall and move elegantly through busy environments. However, maintaining equilibrium through synchronization of motion between the ball and the body of a Ballbot is still an open research problem. This article presents a synchronization control (SC) design, with synchronization and coupling errors for Ballbots to stabilize the body and control ball transfer simultaneously. The proposed SC method is applied to the two 2-D planar models of a Ballbot robot. The dynamic model of the Ballbot is derived, and parameters are identified online using the intelligent particle swarm optimization method. The proposed controller is proven to guarantee asymptotic convergence to zero errors in tracking and synchronization. The stabilizing and transferring problems are investigated through several simulations and experiments by using an actual Ballbot platform. Moreover, the controller performance is compared with an augmented proportional derivative controller and a partial feedback linearization controller. The results and comparisons demonstrate a superior stabilization accuracy of the proposed SC method.

Regulation of Evapotranspiration in Different Precipitation Zones and Its Application in High-Temperature and Drought Monitoring

When drought occurs in different regions, evapotranspiration (ET) changes differently with the process of drought. To achieve an accurate monitoring of large-scale drought using remote sensing, it is particularly necessary to clarify the temporal and spatial characteristics of ET changes with soil water content (SWC). Firstly, based on the measured data, combined with the artificial intelligence particle swarm optimization (PSO) algorithm, an empirical model of ET retrieval by FY–4A satellite data was established and the spatial–temporal characteristics of ET changes with SWC were further analyzed. Lastly, different ET regulation regions were distinguished to achieve the remote sensing monitoring of large-scale drought based on SWC. The main results are as follows: (1) The correlation coefficient between the ET estimated by the empirical model and the measured value was 0.48 and the root mean square error was 24 W·m−2. (2) In the areas with extreme water shortage, water limits the conversion rate of net radiation (Rn) to ET (ECR) and surpasses Rn to become the determinative factor of ET. (3) In extreme arid areas, ET has a significant positive correlation with WVP and SWC. In other precipitation areas, ET has a significant linear correlation with WVP, but the slope of the linear fitting line is different for precipitation. The relationship between ET and SWC is more complex. In areas with precipitation exceeding 800 mm, the correlation between SWC and ET is not significant. In areas with precipitation between 200 mm and 800 mm or in alpine regions, SWC and ET have a quadratic relationship. (4) ECR has quadratic correlations with WVP and SWC, and ECR reaches the maximum when WVP = 0.182 kPa and SWC = 0.217 m3∙m−3. ET may be inhibited for water shortage or water supersaturation. (5) In areas where SWC determines ET, the ET stress index (ESI) is inversely proportional to SWC, and in areas where heat affects ET, the ESI is directly proportional to SWC. Therefore, for the accurate monitoring of large-scale drought, various drought monitoring criteria should be determined in different areas and periods, considering information on precipitation, the underlying surface type, and digital elevation.

An Intelligent Particle Filter for Infrared Dim Small Target Detection and Tracking

With consideration of low tracking accuracy and even losing target when the track-before-detect method based on particle filter (PF-TBD) tracks infrared dim small target in the complex background. In this article, a track-before-detect method based on the spring model firefly algorithm optimization particle filter (SFA-PF-TBD) was proposed to address this problem. First, the attractiveness and movement behavior of fireflies were introduced into the particle filter for optimizing the particles, and the optimization strength was controlled by evaluating the real-time distribution of particles. After optimization, detecting the density of the particles around the optimal particle, the elastic mechanism of spring was used to control the density of particles around the optimal particle when the particles gathered excessively, which made the distribution of particles more reasonable. Then, the TBD method was realized by the improved particle filter for tracking dim small target under the low signal-noise ratio conditions. Finally, we compared the SFA-PF-TBD algorithm with other algorithms by tracking experiments in simulation scenes and actual scenes. The results showed that the SFA-PF-TBD algorithm has more advantages than the PF-TBD algorithm.

An intelligent fast controller for autonomous wheeled robot path navigation in challenging environments

PurposeThis paper aims to incorporate one intelligent particle swarm optimization (IPSO) controller to realize an optimum path in unknown environments. In this paper, the fitness function of IPSO is designed with intelligent design parameters, solving the path navigation problem of an autonomous wheeled robot towards the target point by avoiding obstacles in any unknown environment.Design/methodology/approachThis controller depends on randomly oriented positions with all other position information and a fitness function. Evaluating the position’s best values, this study gets the local best values, and finally, the global best value is updated as the current value after comparing the local best values.FindingsThe path navigation of the proposed controller has been compared with particle swarm optimization algorithm, BAT algorithm, flower pollination algorithm, invasive weed algorithm and genetic algorithm in multiple challenging environments. The proposed controller shows the percent deviation in path length near 14.54% and the percent deviation in travel time near 4% after the simulation. IPSO is applied to optimize said parameters for path navigation of the wheeled robot in different simulation environments.Originality/valueA hardware model with a 32-bit ARM board interfaced with a global positioning system (GPS) module, an ultrasonic module and ZigBee wireless communication module is designed to implement IPSO. In real-time, the IPSO controller shows the percent deviation in path length near 9%.

Study on Minimum Miscibility Pressure of CO2–Oil System Based on Gaussian Process Regression and Particle Swarm Optimization Model

Abstract Injecting CO2 into the reservoir can not only improve crude oil recovery but also achieve the goal of CO2 geological storage. It can not only reduce the greenhouse effect but also obtain additional economic benefits. From the perspective of minimum miscible pressure, carbon dioxide flooding can be divided into miscible flooding and immiscible flooding, and miscible flooding is widely used in the oil field. The most important condition for miscible flooding is to achieve the minimum miscible pressure (MMP). In the study, combining the particle swarm optimization (PSO) with Gaussian process regression (GPR), a novel intelligent GPR and particle swarm optimization (GPR-PSO) method was proposed to establish the model of predicting the MMP of the CO2 and oil system. The model uses the database with more data than in the previous literature, with 365 data points, and the value range of the data is also wider. Moreover, the accuracy of GPR-PSO model was evaluated by statistical error and graph error and compared with the prediction results of existing models. The results show that compared with other models, the GPR-PSO model has higher accuracy and wider application range, the mean absolute relative error is only 1.66%. Meanwhile, the reliability of the model is verified by the sensitivity analysis of parameters. The results show that the most influential parameter on the prediction results is the reservoir temperature, and the least influential parameter is the critical temperature of injected gas. The GPR-PSO model can be used not only to predict the MMP of CO2 and oil system but also to predict the MMP of other gases and crude oil system.

A Multi-Objective Optimization for Supply Chain Management using Artificial Intelligence (AI)

Supply chain management seeks to solve the complex problems of transporting goods from the suppliers to the end customers. Improving the differentiation between different paths to reduce costs and time may require smart systems. This paper proposes two new algorithms for determining, with Multi-Objective Optimization, the least cost and the most appropriate path between two nodes. First: Ant colony optimization (ACO) algorithm, working alongside with Multi Objective Optimization (MOO), is adopted to determine the shortest path and time between two nodes to reach with the least cost. Multi-Objective intelligent Ant Colony (MOIAC) algorithm improves supply chain management to achieve the optimal and the most appropriate solutions. Second: Particle Swarm Optimization (PSO) algorithm, also working alongside MOO, is adopted to determine the least cost, time, and shortest path. Multi Optimization Intelligent Particle Swarm (MOIPS) algorithm improves supply chain management by determining the shortest path with the least cost. These two proposed algorithms seek the optimal solution by MOO using a JAVA Program. The experimental results show the excellence of the first algorithm in determining the optimal and the most appropriate path while getting throw risks inherent in transporting goods. It also demonstrates excellence in transporting goods in the shortest possible time and with the least cost. The second algorithm also shows excellence in transporting goods with the least possible cost via the shortest path and in the shortest time.

WiFi Positioning in 3GPP Indoor Office with Modified Particle Swarm Optimization

With the start of the Fourth Industrial Revolution, Internet of Things (IoT), artificial intelligence (AI), and big data technologies are attracting global attention. AI can achieve fast computational speed, and big data makes it possible to store and use vast amounts of data. In addition, smartphones, which are IoT devices, are owned by most people. Based on these advantages, the above three technologies can be combined and effectively applied to navigation technology. In the case of an outdoor environment, global positioning system (GPS) technology has been developed to enable relatively accurate positioning of the user. However, due to the problem of radio wave loss because of many obstacles and walls, there are obvious limitations in applying GPS to indoor environments. Hence, we propose a method to increase the accuracy of user positioning in indoor environments using wireless-fidelity (Wi-Fi). The core technology of the proposed method is to limit the initial search region of the particle swarm optimization (PSO), an intelligent particle algorithm; doing so increases the probability that particles converge to the global optimum and shortens the convergence time of the algorithm. For this reason, the proposed method can achieve fast processing time and high accuracy. To limit the initial search region of the PSO, we first build an received signal strength indicator (RSSI) database for each sample point (SP) using a fingerprinting scheme. Then, a limited region is established through a fuzzy matching algorithm. Finally, the particles are randomly distributed within a limited region, and then the user’s location is positioned through a PSO. Simulation results confirm that the method proposed in this paper achieves the highest positioning accuracy, with an error of about 1 m when the SP interval is 3 m in an indoor environment.

Intelligent swarm algorithms for optimizing nonlinear sliding mode controller for robot manipulator

This work introduces an accurate and fast approach for optimizing the parameters of robot manipulator controller. The approach of sliding mode control (SMC) was proposed as it documented an effective tool for designing robust controllers for complex high-order linear and nonlinear dynamic systems operating under uncertain conditions. In this work Intelligent particle swarm optimization (PSO) and social spider optimization (SSO) were used for obtaining the best values for the parameters of sliding mode control (SMC) to achieve consistency, stability and robustness. Additional design of integral sliding mode control (ISMC) was implemented to the dynamic system to achieve the high control theory of sliding mode controller. For designing particle swarm optimizer (PSO) and social spider optimization (SSO) processes, mean square error performances index was considered. The effectiveness of the proposed system was tested with six degrees of freedom robot manipulator by using (PUMA) robot. The iteration of SSO and PSO algorithms with mean square error and objective function were obtained, with best fitness for (SSO) =4.4876 𝑒<sup>-6</sup> and (PSO)=3.4948 𝑒<sup>-4</sup>.

Formulation of New Intelligent Nanoparticle Inhibited H1N1 Influenza Subtype and SARS Coronavirus Type 2 (COVID-19) in vitro

Background: Rapid infection of the coronavirus and frequency of the subtypes are the main problems of drug and vaccine intervention during COVID-19 pandemic. New drug discovery to respond these needs, is the goal of study. Hence, considering structural and biological components of SARS-CoV-2, new intelligent particle designed and formulated and several dockings were done as in silico assay. Methods: Fe3O4 nanoparticles synthesized by the coprecipitation method, coated and functionalized. Chemical bindings (Fourier transform infrared assay), magnetic behavior (vibrating sample magnetometer assay), morphology (field emission scanning electron microscope assay), and cell toxicity and cell viability (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay) were checked and confirmed in vitro by exposure of particles to the H1N1 virus laboratory media exposure (BSL2 category). Results: Results show drug has ability to reduce more than 4 logarithms in the virus titration, in concentration of 70 μg/ml, so it was proved these nanoparticles could have antiviral effect. Although, because of lack of BSL3 standard laboratory, the antiviral effect on COVID-19 could not be performed on large scale. Conclusions: By the way, we concluded that new specific nanoparticles could make a new chance for COVID-19 drug therapy at any subtype exposure.

Optimizing process parameters for joint strength efficiency improvement in friction stir welding of polycarbonate sheets

AbstractThe present work addresses optimization of these friction stir welding process variables to maximize joint strength efficiency of welded polycarbonate sheets by using particle swarm optimization algorithm over response surface method based regression model. Initially, parametric influence on weld quality characteristics namely weld bead profile, bead geometry with associated microstructure along with micro‐hardness deviation through the weld centerline and stress‐strain behavior of the weld have been studied in detail as per full factorial design of experiments by using three different tool pin profiles such as cylindrical, square and triangular. The center point experiment i. e. tool rotational speed of 1800 min−1 and welding speed of 20 mm/min, and square tool pin profile were found to be the optimum combination with a maximum joint strength efficiency of 60.06 %. The regression model of weld ultimate tensile strength was developed by using response surface methodology which was found to be significant. Therefore, this model was further used for parametric optimization by using both response surface methodology and intelligent particle swarm optimization approaches. A slight improvement in joint strength efficiency with better optimization capability was found by using particle swarm optimization technique as compared to response surface methodology.

An intelligent particle filter with resampling of multi-population cooperation

The particle filter (PF) has excellent estimation performance for nonlinear non-Gaussian systems. However, this method misleads the results due to sample impoverishment and the sensitivity on the initial values of state and process noise variance. To overcome this, an intelligent PF method with a resampling of multi-population cooperation is presented in this paper. Firstly, an intelligent resampling mechanism based on a circular collaborative structure is proposed. In this mechanism, the particles are divided into multiple populations, which are generated by importance densities with different initial state values and variances individually. Secondly, a collaborative strategy based on Gaussian mutation is designed to improve particle diversity so as to raise estimation accuracy of PF. This strategy reserves the high-weight particles in each population, and replaces the low-weight ones with the particles generated by Gaussian mutation on high-weight particles in the previous population based on the circular collaborative structure. Finally, three systems are introduced to test the performance of the proposed method. The results illustrate that the proposed method can effectively improve the estimation accuracy and robustness of PF when the initial values of state and variance of process noise are unknown compared with three existing methods.