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Natural fibre-reinforced hybrid polymer composites have gained significant attention worldwide in mechanical, aerospace, and automotive applications. Advanced machining techniques, such as abrasive water jet machining, have emerged as a solution to various challenges in this field, offering benefits such as the ability to shape complex geometries, achieve superior performance, improve surface characteristics, and attain high levels of accuracy. The research proposes a new approach for producing biodegradable hybrid composites composed of polylactic acid, bamboo particles, and montmorillonite clay using an innovative solvent-free stir-casting technique optimised for maximum efficiency. To systematically analyse the surface roughness, kerf angle, and material removal rate, a Box–Behnken design of experiments was employed, with the traverse rate, abrasive feed rate, and stand-off distance considered design variables. Analysis of variance was used to determine the significance of the differences between means of variables, while response surface methodology was utilised to establish the explicit relationship between the design variables and the response of the composite machining. The particle swarm optimisation algorithm was also employed to determine the optimal values of the design parameters for machining composites. The results showed that the traverse rate was the most influential factor, followed by the abrasive feed rate. In contrast, the stand-off distance had a relatively lower level of influence. The optimal process parameters were identified, resulting in a minimum surface roughness of 5.56 μm, a kerf taper of 0.0044 radians, and a material removal rate of 1175 g/min. [ABSTRACT FROM AUTHOR]

The aim of this research is to evaluate the performance of the distribution network by connecting wind distributed generation (DG) and determining the optimal location and size using the particle swarm optimization (PSO) technique, once the wind DG is connected at the optimal location, the output of wind turbines is not constant but varies with changes in wind speed. Wind turbines are designed to generate the energy from the wind. As the output of the wind turbines changes, it influences the power flow and voltage levels in the distribution network. The injection of power from the wind turbines can cause variations in voltages within the distribution network. Additionally, the changing power flow may contribute to power losses in the distribution system. In this paper, the voltages and active power losses are evaluated with the change in wind speed for the IEEE 15 Bus system by conducting load flow analysis in MATLAB. The results reveal optimized solutions that contribute to reduced power losses, increased renewable energy generation, and improved voltage profiles. This research underscores the potential of PSO-based optimization in conforming more efficient and sustainable distribution networks. [ABSTRACT FROM AUTHOR]

A method for K-prototype clustering, which can process mixed data types, is proposed first. An algorithm for information security evaluation of hybrid clusters based on K-prototypes was constructed. This method makes full use of the excellent global optimization performance of PSO and effectively solves the defect that the K-protection function quickly falls into local optimization. Simulation results show that the proposed method can effectively prevent local extreme values and improve overall performance. [ABSTRACT FROM AUTHOR]

The determination of the optimal random field element (RFE) size is crucial in soil slope reliability analysis as it governs the trade-off between precision in failure probability calculations and computational efficiency. Given the substantial computational burden associated with smaller RFE sizes, studies on their impact on slope failure probability are scarce. This research examines the influence of RFE size on failure probability and safety factor, employing the Karhunen–Loève expansion to generate random fields and integrating the simplified Bishop method with particle swarm optimization (PSO) to assess slope stability. Through Monte Carlo Simulation (MCS), this study investigates the effects of the ratio of slope height to RFE size (H/De) on slope reliability metrics across two illustrative cases. Results reveal a notable influence of H/De on the distribution of safety factors (Fs) and failure probability (PF), with overestimation observed at smaller H/De ratios. When H/De exceeds 10 for Example 1 and 15 for Example 2, the Fs distribution patterns in both scenarios stabilize significantly, displaying minimal variability. The PF of Example 1 and Example 2 decreases with the increase of H/De and remains basically unchanged when H/De exceeds 10 and 15, respectively. Consequently, a recommended H/De ratio of 20 is proposed based on the analyzed cases, facilitating accurate calculations while mitigating computational overhead. [ABSTRACT FROM AUTHOR]

Objectives: Accurate prediction of ionospheric total electron content (TEC) is of great research significance to improve the accuracy of global navigation satellite system (GNSS) navigation and positioning. Methods: We propose an ionospheric TEC prediction model based on particle swarm optimization (PSO) algorithm and Elman neural network (ENN). First, high accurate regional ionospheric map (RIM) in China is set up by using the GNSS observation data from the crustal movement observation network of China (CMONOC) in 2018. Second, the RIM TEC data are used for forecasting research. Results: During the quiet period and disturbance period, the best time scale for training set is 21 d. For the 5 d sliding prediction, the root mean square error (RMSE) of the PSO-ENN model during the quiet period and disturbance period are decreased by 27.6% and 20.5%, respectively. When using the global ionospheric map (GIM) published by center for orbit determination in Europe (CODE) in China region for different grid points prediction, the average RMSE of the PSO-ENN model is lower than the CODE 1-day predicted GIM (C1PG) by back propagation neural network (BPNN) model and ENN model. For the 30-day sliding prediction in August 2018 based on RIM TEC in China region, the average RMSE of the PSO-ENN model are 24.8% and 14.3% lower than that of BPNN and ENN models, respectively. Conclusions: The proposed model has better prediction accuracy and stability both in the quiet period and disturbance period. [ABSTRACT FROM AUTHOR]

This paper introduces a novel Particle Swarm Optimization (RLPSO) algorithm based on reinforcement learning, embodying a fundamental symmetry between global and local search processes. This symmetry aims at addressing the trade-off issue between convergence speed and diversity in traditional algorithms. Traditional Particle Swarm Optimization (PSO) algorithms often struggle to maintain good convergence speed and particle diversity when solving multi-modal function problems. To tackle this challenge, we propose a new algorithm that incorporates the principles of reinforcement learning, enabling particles to intelligently learn and adjust their behavior for better convergence speed and richer exploration of the search space. This algorithm guides particle learning behavior through online updating of a Q-table, allowing particles to selectively learn effective information from other particles and dynamically adjust their strategies during the learning process, thus finding a better balance between convergence speed and diversity. The results demonstrate the superior performance of this algorithm on 16 benchmark functions of the CEC2005 test suite compared to three other algorithms. The RLPSO algorithm can find all global optimum solutions within a certain error range on all 16 benchmark functions, exhibiting outstanding performance and better robustness. Additionally, the algorithm's performance was tested on 13 benchmark functions from CEC2017, where it outperformed six other algorithms by achieving the minimum value on 11 benchmark functions. Overall, the RLPSO algorithm shows significant improvements and advantages over traditional PSO algorithms in aspects such as local search strategy, parameter adaptive adjustment, convergence speed, and multi-modal problem handling, resulting in better performance and robustness in solving optimization problems. This study provides new insights and methods for the further development of Particle Swarm Optimization algorithms. [ABSTRACT FROM AUTHOR]

The liner shipping schedules determine the container transportation time and the arrival time of ships, which has a significant influence on the shipper selection behavior and the transportation demand. This paper addresses the container liner shipping schedule optimization with shipper selection behavior considered. Our problem is formulated as a mixed-integer nonlinear programming model, where the shipper selection behavior is evaluated by a nested logit model. A particle swarm optimization (PSO) framework embedded with CPLEX solver is designed, by combining the constraint relaxations and the linearization techniques with the heuristic rules. The numerical experiments are conducted based on the Persian Gulf route of COSCO SHIPPING LINES. The results show that: the total freight demand is increased by 23% and the weekly operation revenue is increased by 31% after considering shipper selection. Besides, we find that the planned ship speed should be increased for time-preference shippers with electronic or refrigerated products, while it should be decreased for price-preference shippers with general or bulk cargoes. These conclusions can provide decision support for the operation practice of liner shipping schedule design. [ABSTRACT FROM AUTHOR]

Oilfield Sewage System Scheduling is a complicated, large-scale, nonlinear system problem with multiple variables. The complexity of the sewage system pipeline network connection grows along with the ongoing building of oilfield stations, and the shortcomings of the sewage system water quantity scheduling program based on human experience decision-making become increasingly apparent. The key to solving this problem is to realize the digital and intelligent scheduling of sewage systems. Taking the sewage system of an oil production plant in Daqing oilfield as the research object, the water scheduling model of the sewage system is established in this paper. Aiming at the complex nonlinear characteristics of the model, the Levy flight speed updating operator, the adaptive stochastic offset operator, and the Brownian motion selection optimization operator are established by taking advantage of the particle swarm optimization (PSO) and the cuckoo search (CS) algorithm. Based on these operators, a hybrid PSO-CS algorithm is proposed, which jumps out of the local optimum and has a strong global search capability. Comparing PSO-CS with other algorithms on the CEC2022 test set, it was found that the PSO-CS algorithm ranked first in all 12 test functions, proving the excellent solving performance of the PSO-CS algorithm. Finally, the PSO-CS is applied to solve a water scheduling model for the sewage system of an oil production plant in Daqing Oilfield. It is found that the scheduling plan optimized by PSO-CS has a 100% water supply rate to the downstream water injection station, and the total energy consumption of the scheduling plan on the same day is reduced from 879.95 × 106 m5/d to 712.84 × 106 m5/d, which is a 19% reduction in energy consumption. The number of water balance stations in the sewage station increased by 7, which effectively improved the water resource utilization rate of the sewage station. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering 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.)

Tropospheric delay influences high-precision navigation positioning and precipitable water vapor retrieval with the Global Navigation Satellite System (GNSS). Existing Zenith Tropospheric Delay (ZTD) models often struggle to accurately capture the non-linear variations in tropospheric delay. Therefore, this study employs the coupled Particle Swarm Optimization (PSO) algorithm with the Genetic Algorithm Back Propagation (GABP) neural network, combined with ERA5 reanalysis meteorological data, to develop an optimized model (PSO-GABP) for ZTD in the Chinese mainland. Nevertheless, ZTD data at the target point are obtained through four different methods: the integration method, model method, and GPT3 models at varying resolutions (EZTD_P, EZTD_S, GPT3_1, and GPT3_5). The analysis reveals the following: (1) The Root Mean Square (RMS) errors of the ZTD values obtained through these different methods are 1.86 cm, 3.42 cm, 3.99 cm, and 4.09 cm, respectively, when verified against GNSS_ZTD data from 2016. The optimized model yields ZTD values with the RMS error of 0.98 cm, 1.96 cm, 2.34 cm, and 2.36 cm, representing improvements of 47.3%, 42.7%, 41.4%, and 42.3% compared to the pre-optimization results. These improvements are significant; (2) The predictive capability of the constructed ZTD model is evaluated using GNSS_ZTD data from 2019 as a reference. The PSO_EZTD_P model demonstrates excellent accuracy and practicality in the Chinese mainland. As a result, the tropospheric delay optimization model based on the PSO-GABP neural network can provide valuable references for real-time GNSS navigation positioning and precipitable water vapor detection in the Chinese mainland.

Purpose: The purpose of this research was to provide a model for predicting time series of financial information based on the Lyapunov representation of information using chaos theory.Method: This research is applied in its purpose, which is conducted using a quantitative approach. The research ranks as descriptive-causal accounting research based on actual information in companies' financial statements. The research method is the "post-event" type and was carried out using chaos theory and Saida's method based on the Lyapunov view.Findings: The findings showed that during the ADF test, the null hypothesis was rejected at a level of less than 5% type 1 error and 95% confidence, and it shows that the data is not static. During the substitution analysis test and its significance level, the behavior of the time series of the main financial information is significantly different compared to their substitutes. The obtained value was calculated to describe the production process of all data sets for μ = 2, ApEnMax equal to 0.65 and rMax equal to 0.32, and for μ = 3, ApEnMax equal to 0.6 and rMax equal to 0.44. The value of the Lyapunov profile in stability at a certain point is less than zero and in the limited cycle of stability is equal to zero and in chaos, it is greater than zero and smaller than ∞, and in noise it is equal to ∞.Conclusion: The results show that higher returns, encourage investors to invest and increase the flow of capital. It is believed that companies' stock returns are a function of systematic risk, and systematic risk represents the changes in the return rate of a share compared to the changes in the return rate of the entire stock market.

Abstract In this paper, a microstrip Wilkinson power divider (MWPD) based on particle swarm optimization (PSO) algorithm is designed, simulated, and fabricated using novel resonators. In addition, attenuators and open-ended stubs are incorporated to generate a broad cut-off band and reduce unwanted harmonics. The proposed power divider has a central frequency of 1 GHz. The performance of each used resonator is analyzed based on lumped-element circuit models.The L and C parameters of the equivalent circuit of the used resonators are predicted and optimized with the assistance of the PSO method. The subsequent phase was the fabrication of the proposed MWPD, after which its performance was evaluated in the light of the results obtained from the simulation. It was discovered that there was a high degree of concordance between the two. On the other hand, the fabricated circuit has several benefits, including a suitable S12 of − 3.15 dB, a high return loss of less than − 24 dB at the operating frequency, a compact size of 0.058 $${\varvec{\lambda}}_{{\varvec{g}}}$$ λ g × 0.064 $${\varvec{\lambda}}_{{\varvec{g}}}$$ λ g , and the ability to remove undesired harmonics. The results show a high level of suppression of the unwanted harmonics (up to the 16th harmonic) and a great responsiveness in the passband, while having very low ripple. As a result, the proposed circuit may be used in a wide variety of electronic devices, such as radar transmitter and receiver circuits, and many other high-frequency systems.

This research aims to study the non-linear parametric vibration of laminated composite shallow (LCS) shells with the optimal fiber angles exposed to external and parametric excitations, including primary and 1:2 internal resonances. In this regard, optimal fiber angles are found with implementations of the P-T method for the objective functions and utilization of the particle swarm optimization (PSO). Also, the non-linear model of the shallow shells is established based on the stress function and the first-order shear deformation theory (FSDT). According to FSDT, Hooke's law, von-Kármán equation, Hamilton's principle, and Galerkin method, two-degree-of-freedom non-linear ordinary differential governing equations are discretized. [ABSTRACT FROM AUTHOR]

In this paper, a microstrip Wilkinson power divider (MWPD) based on particle swarm optimization (PSO) algorithm is designed, simulated, and fabricated using novel resonators. In addition, attenuators and open-ended stubs are incorporated to generate a broad cut-off band and reduce unwanted harmonics. The proposed power divider has a central frequency of 1 GHz. The performance of each used resonator is analyzed based on lumped-element circuit models.The L and C parameters of the equivalent circuit of the used resonators are predicted and optimized with the assistance of the PSO method. The subsequent phase was the fabrication of the proposed MWPD, after which its performance was evaluated in the light of the results obtained from the simulation. It was discovered that there was a high degree of concordance between the two. On the other hand, the fabricated circuit has several benefits, including a suitable S12 of − 3.15 dB, a high return loss of less than − 24 dB at the operating frequency, a compact size of 0.058 λ g × 0.064 λ g , and the ability to remove undesired harmonics. The results show a high level of suppression of the unwanted harmonics (up to the 16th harmonic) and a great responsiveness in the passband, while having very low ripple. As a result, the proposed circuit may be used in a wide variety of electronic devices, such as radar transmitter and receiver circuits, and many other high-frequency systems. [ABSTRACT FROM AUTHOR]

This paper studies the issue of capacity allocation in multi-rank distribution channel management, a topic that has been significantly overlooked in the existing literature. Departing from conventional approaches, hierarchical priority rules are introduced as constraints, and an innovative assignment integer programming model focusing on capacity selection is formulated. This model goes beyond merely optimizing profit or cost, aiming instead to enhance the overall business orientation of the firm. We propose a greedy algorithm and a priority-based binary particle swarm optimization (PB-BPSO) algorithm. Our numerical results indicate that both algorithms exhibit strong optimization capabilities and rapid solution speeds, especially in large-scale scenarios. Moreover, the model is validated through empirical tests using real-world data. The results demonstrate that the proposed approaches can provide actionable strategies to operators, in practice. [ABSTRACT FROM AUTHOR]

The performance of the optimized three-section squared-off cascade is compared with that of a match abundance ratio cascade (MARC) for the enrichment of 92Mo and 100Mo isotopes from their natural composition to 99.99%. Two codes, namely, 3SQC–PSO and MARC–PSO, are developed for optimizing 200 centrifuge cascades using the particle swarm optimization (PSO) algorithms. The objective function is to maximize the cascade capacity, recovery factor, and the D-function and to minimize the total interstage flow. A comparison of the results shows that a MARC is able to enrich 100Mo with the highest recovery and production capacity. Due to the fact that more than one separation step is required for the separation of 92Mo, a MARC is beyond the capability to separate it to 99.99%, but a squared-off cascade is capable of making this. Therefore, if the goal is the separation of all or some of isotopes of an element, the squared-off cascade is the preferred option for economical industrialization and reducing installation. [ABSTRACT FROM AUTHOR]

Searching an optimal value of the neural emulator adaptive rate presents a great problem. Indeed, a new scheme of neural emulators based on the Particle Swarm Optimization (PSO) algorithm for nonlinear systems is adopted in this paper. The main goal of this approach consists in adjusting effectively the neural emulator adaptive rate in order to accelerate the convergence speed and to improve the precision degree. The obtained results are compared with those reached with an intelligent tuning strategy. An experimental validation of the new emulator adaptation is carried on chemical reactor. Efficiency of the proposed method is proved according to the obtained performances. [ABSTRACT FROM AUTHOR]

This study aims to improve the predictive ability of financial brand data. help investors and decision-makers better identify different financial brands' performance, and reduce unnecessary financial risks. The stock prices of various financial brands are undertaken as the experimental objects. The characteristics of financial data and the original model network structure are analyzed based on the Internet of Things (IoT). The long short-term memory (LSIM) neural and the particle swarm optimization algorithm (PSO algorithm) are adopted to optimize the model's parameters, The autoregressive integrated moving average model (ARIMA) algorithm is used to analyze the spatio-temporal data, and a data prediction model for the financial brand based on the ARIMA-PSO-LSTM algorithm is proposed. In addition, the validity and effectiveness of the proposed model are proved through the analysis and testing of specific example data. It is found that the model based on the IoT shows higher processing speed in contrast to the conventional method: the model proposed in this study shows smaller errors and improved accuracy in dam prediction than the PSO-LSTM model and the LSTM model; the model presents better fitting effects and better performance compared with the latest research model. The experimental results reveal that combining the three algorithms can integrate the advantages of various algorithms, and the three algorithms can complement each other, thereby improving the ability of financial data analysis. The model established in this study shows high accuracy and good adaptability in identifying financial brands and predicting financial data. [ABSTRACT FROM AUTHOR]

Effective air quality prediction models are crucial for the timely prevention and control of air pollution. However, previous models often fail to fully consider air quality's temporal and spatial distribution characteristics. In this study, Xi'an City is used as the study area. Data from 1 January 2019 to 31 October 2020 are used as the training set, while data from 1 November 2020 to 31 December 2020 are used as the test set. This paper proposes a multi-time and multi-site air quality prediction model for Xi'an, leveraging a deep learning network model based on APSO-CNN-Bi-LSTM. The CNN model extracts the spatial features of the input data, the Bi-LSTM model extracts the time series features, and the PSO algorithm with adaptive inertia weight (APSO) optimizes the model's hyperparameters. The results show that the model achieves the best results in terms of MAE and RMSE. Compared to the PSO-SVR, BPTT, CNN-LSTM, and GA-ACO-BP models, the MAE improved by 9.375%, 6.667%, 2.276%, and 4.975%, while the RMSE improved by 8.371%, 8.217%, 6.327%, and 5.293%. These significant improvements highlight the model's accuracy and its promising application prospects. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

A model-based study is conducted to optimize the dynamic performance of a solenoid actuator in a compressed natural gas (CNG) injector. A mechanical model, an electromagnetic model, and an optimum model using particle swarm optimization (PSO) algorithm are built to depict operation and optimize the dynamic response times of the injector solenoid actuator. Effects of the number of coil turns (N), spring stiffness (k), and gap between plunger and cylinder wall (g), on the dynamic characteristics of the injector solenoid actuator are investigated. Simulation results show that, when k increases from 560 to 2560 N/m, and g increases from 0.1 to 0.5 mm, the opening response time increases 35.41% and 48.03%, respectively; however, the closing response time of the injector solenoid actuator reduces 27.27% and 45.25%, respectively. When N is increased from 342 to 552, the opening response time reduces 35.48%; nevertheless, the closing response time increases considerably with a 195.45% increment. Using PSO algorithm, it is found that the optimum version of the injector solenoid actuator has improvements for both opening and closing response times, in which the opening response time reduces 11.41% when compared with the original version. [ABSTRACT FROM AUTHOR]

As the current mainstream high-precision ADC architecture, sigma-delta ADC is extensively employed in a wide range of domains and applications. This paper presents the design of a highly efficient cascaded digital decimation filter for sigma-delta ADCs, emphasizing the suppression of high folding band noise and the achievement of a flat passband. Additionally, this study addresses the critical balance between filter performance and power consumption. An inserting zero (IZ) filter is incorporated into a cascaded integrator comb (CIC) filter to enhance aliasing suppression. The IZ filter and compensation filter are optimized using the particle swarm optimization (PSO) algorithm to achieve greater noise attenuation and smaller passband ripple. The designed filter achieves a noise attenuation of 93.4 dB in the folding band and exhibits an overall passband ripple of 0.0477 dB within a bandwidth of 20 KHz. To decrease the power consumption in the filter design, polyphase decomposition has been applied. The filter structure is implemented on an FPGA, processing a 5-bit stream from a 64-times oversampling rate and third-order sigma-delta modulator. The signal-to-noise ratio (SNR) of the output signal reaches 91.7 dB. For ASIC design, the filter utilizes 180 nm CMOS technology with a power consumption of 0.217 mW and occupies a layout area of 0.72 mm2. The post-layout simulation result indicates that the SNR remains at 91.7 dB. [ABSTRACT FROM AUTHOR]

The global renewable energy portfolio is experiencing a growing proportion of shipboard photovoltaic (PV) energy. Grid-connected system has become the development trend of photovoltaic power generation technology in marine applications because of its high energy utilization efficiency. However, in the grid-connected process, harmonic pollution is easily caused by time-varying marine environment disturbance, which affects the quality of grid-connected power. The current recurrent neural network Levenberg-Marquardt backpropagation (CRNN-LM-BP) method, a control methodology, is introduced in this paper to ensure the robustness, stability, and consistency of the system. In addition, the inductance-capacitance-inductance (LCL) filter is optimized using particle swarm optimization (PSO) to minimize the presence of high frequency harmonics. The system that has been developed possesses the ability to improve the accuracy of steady-state control, operate with high efficiency and effectiveness, and maintain a total harmonic distortion of 1.73 %. Concurrently, streamlining the Jacobian matrix in the LM algorithm expedites convergence. It enables the system to improve its dynamic response, minimize excess, and quickly return to a stable condition when faced with load variations, a wide range of filter parameter adjustments, and voltage fluctuations. The suggested method’s effectiveness and applicability are evaluated by comparing it to the traditional proportional-resonant control, CRNN-BP control, using Matlab/Simulink and the real-time simulator OPAL-RT5700..

Abstract A structured approach to managing reactive power is imperative within the context of power systems. Among the restructuring initiatives in the electrical sector, power systems have undergone delineation into three principal categories: generation, transmission, and distribution entities, each of which is overseen by an independent system operator. Notably, active power emerges as the predominant commodity transacted within the electrical market, with the autonomous grid operator assuming the responsibility of ensuring conducive conditions for the execution of energy contracts across the transmission infrastructure. Ancillary services, comprising essential frameworks for energy generation and delivery to end-users, encompass reactive power services pivotal in the regulation of bus voltage. Of particular significance among the array of ancillary services requisite in a competitive market milieu is the provision of adequate reactive power to uphold grid safety and voltage stability. A salient impediment to the realization of energy contracts lies in the inadequacy of reactive power within the grid, which poses potential risks to its operational safety and voltage equilibrium. The optimal allocation of the reactive power load is predicated upon presumptions of consistent outcomes within the active power market. Under this conceptual framework, generators are afforded continual compensation for the provision of reactive power indispensable for sustaining their active energy production endeavors.

We theoretically propose and numerically analyze a small-size Wilkinson power divider (WPD) based on a novel ring-shaped resonator that can create a wide bandwidth in the passband of the power divider. The WPD can attenuate 9 disturbing harmonics while the maximum insertion loss at the working frequency of 2.6GHz is about −3.08 dB. The structure consists of a pair of asymmetric cylindrical-shaped suppressors above and below the main transmission line. The overall dimensions of the structure are 8.5 mm × 11.8 mm, and the input return loss and isolation parameters are −41 dB and −17.3 dB, respectively, in the working frequency. Also, the fundamental frequency can be adjusted by changing the dimensions of the resonators and suppressors. The particle swarm optimization (PSO) optimization algorithm has been used to analyze and calculate the inductor and capacitor values of the equivalent LC circuit of the proposed WPD. In the passband (1.4GHz to 3.3GHz) of this power divider, the output return loss and isolation are less than −15 dB and the insertion loss is between −3.08 dB and −3.13 dB, so the fractional bandwidth (FBW) is 73%. The structure is compact and easy to be fabricated and has potential applications in communication devices.

Along with the increasing low-carbon demand of the power system, the access of a high percentage of renewable energy resources to the distribution network has a large impact on the voltage fluctuation of the system and reduces the operational reliability. In this paper, we consider utilizing the reactive capacity of distributed resources to participate in system voltage regulation to reduce node loss of load probability (LOLP) caused by node overvoltage faults and propose an overvoltage risk regulation strategy for the interaction between distribution network operators (DSOs) and distributed users in the framework of the Stackelberg game. First, the nodes are clustered and analyzed based on the two-dimensional indexes of node voltage regulation ability, and different voltage regulation compensation tariffs are assigned. Second, the cost-benefit model of voltage regulation for the leader and follower sides and the node LOLP model are constructed to measure the reliability of the system. The Stackelberg game is used to co-optimize the two parties' compensation tariffs and voltage regulation strategies. The optimal solution of voltage regulation under the equilibrium of the game is obtained by solving using the particle swarm optimization (PSO) algorithm. Based on the IEEE-33 node system, a case study is carried out to verify that the proposed overvoltage risk regulation strategy can maximize the benefits of the regulator participants while enhancing the operational reliability of the system. [ABSTRACT FROM AUTHOR]

A structured approach to managing reactive power is imperative within the context of power systems. Among the restructuring initiatives in the electrical sector, power systems have undergone delineation into three principal categories: generation, transmission, and distribution entities, each of which is overseen by an independent system operator. Notably, active power emerges as the predominant commodity transacted within the electrical market, with the autonomous grid operator assuming the responsibility of ensuring conducive conditions for the execution of energy contracts across the transmission infrastructure. Ancillary services, comprising essential frameworks for energy generation and delivery to end-users, encompass reactive power services pivotal in the regulation of bus voltage. Of particular significance among the array of ancillary services requisite in a competitive market milieu is the provision of adequate reactive power to uphold grid safety and voltage stability. A salient impediment to the realization of energy contracts lies in the inadequacy of reactive power within the grid, which poses potential risks to its operational safety and voltage equilibrium. The optimal allocation of the reactive power load is predicated upon presumptions of consistent outcomes within the active power market. Under this conceptual framework, generators are afforded continual compensation for the provision of reactive power indispensable for sustaining their active energy production endeavors. [ABSTRACT FROM AUTHOR]

In this paper, a voltage control system for an applicable Boost converter system under disturbances is presented. The voltage adjustment performance of the Boost converter is governed by a feedback PID controller system. The realism of the proposed power system is validated through using the Simscape environment, which is adopted to model and simulate the PID based Boost converter system. The presented Simscape system is more applicable in real-time than the Simulink model as the model of the Simscape components and devices is built based on practical considerations like tolerance and parasitic elements. PSO optimization algorithm is utilized to develop the performance of the PID controller through operating the converter system based on best values for PID gain parameters. The simulation design of the proposed power system is implemented in the Matlab tool and its results are presented and discussed to evaluate the performance of the presented feedback PID controller. To validate the robustness of the converter performance the voltage adjustment behavior of the PID controller is assessed based on disturbance of supply voltage, reference voltage and resistive load working conditions. Simulation results revealed the ability of the PID controller to reject the supply voltage disturbances and enable the Boost output to track the demand input trajectories effectively. [ABSTRACT FROM AUTHOR]

Renewable energy is an alternative type of energy that can be used continuously without fear of environmental pollution. One of the things that can be used to get new renewable energy is using solar panels where later the electrical energy produced will be stored in batteries. A part from the advantages of a battery that can store electrical energy, but if it is used excessively and causes the battery to experience a deep discharge condition, steps are needed to over come it.This study uses the Particle Swarm Optimization method as an auxiliary method. And in this research it has been found that the auto switch system can charge battery 1 and battery 2 when the voltage level is below 11.88V or the DoD is already at 55% and stops charging when the voltage level is above 12.86Vfor battery 1 and batteries 2. [ABSTRACT FROM AUTHOR]

The twin wire arc spray method (TWAS) is one among the efficient methods that is widely recognised and utilised for protecting components in surface coating technology due to its economic viability and adaptability. The quality of coating obtained in TWAS is excellent and the porosity is very low in comparison to other conventional methods. The TWAS spray parameters were optimized by single response optimization to obtain the minimum porosity utilizing statistical tools such as DOE and response surface methodology (RSM). Investigations focused on the influence of power, gas pressure and standoff distance. The analysis was done by three factors and five stages of analysis in the central composite design (CCD). The lowest porosity (1.21%) was obtained at optimum conditions of 5 kW power, 3.5 bar air pressure and 200mm stand-off distance, respectively, for TWAS coatings. The RSM, ANN and PSO is utilized to optimise the constructed model. The optimized value obtained through PSO and ANN were compared with the RSM. ANN gives a superior outcome when comparing PSO and RSM. It is validated by the response and contour plots in order to assist in selecting the spray parameters. The obtained results indicate that the coating porosity depends mostly on the arc current. [ABSTRACT FROM AUTHOR]

Turboshaft engines are different from each other due to manufacturing and installation tolerances. Hence, it is difficult to draw out the physical model from the average component maps and design points to represent the performance of the individual engine. The available test-bed data is usually less than the number of correction coefficients to update the maps, and it is the underdetermined state optimization issue. In this paper, we propose a novel reduced-order optimization, namely PSO-EKF algorithm, combined with prior state estimation for non-linear model correction of the turboshaft engine. This method combines the advantages of PSO and extended Kalman filter (EKF). PSO-EKF method converts partial parameters optimized by PSO into parameters directly solved by EKF. The order of optimization space is reduced. Using the correction coefficient function, a stability improvement strategy is designed to ensure the stability of the optimization process. Compared with the GA and PSO algorithms, experimental verification shows that the method has a faster convergence speed and smaller model error than the general PSO. After the performance map is updated by this method, the error of outputs of the individual model is within 1.4 %. [ABSTRACT FROM AUTHOR]

Dynamic modern healthcare systems rely heavily on the contributions of computer scientists. The diagnosis process is a team effort involving many people: patients, their families, healthcare providers, researchers, and policymakers. Computer technology plays a crucial role in supporting this effort by providing a number of essential services to all of these groups. In the early stages of many diseases, a diagnosis can be made automatically using a computer-aided system, with some degree of certainty. This paper presents a hybrid optimal deep learning-based model for tuberculosis disease recognition using MRI images. Several deep learning models are combined to extract the most relevant features from MRI images. In particular, we establish a combination between vision transformer (ViTs) and Efficient-Net models in order to maximize classification accuracy. We conducted experiments to investigate the accuracy of the proposed model using the Shenzhen and Montgomery data set, and found that it yielded substantially more accurate and better results than the state of-the-art works. [ABSTRACT FROM AUTHOR]

In this study, the optimization of the location of oil production and injection wells, the number of production and injection wells, and the economic evaluation in the water injection process has been carried out using three algorithms: PSO, GA, and HGAPSO. Optimizing the oil extraction process by optimizing well locations, the optimal number of production and injection wells, and economic evaluation can improve the performance and efficiency of the process. In this study, the optimization of the water injection process into oil reservoirs using the PSO, GA, and HGAPSO algorithms has been addressed. Additionally, economic evaluation, considering costs and profits from oil production using the best-optimized parameters, has been conducted, and the best Net Present Value (NPV) has been calculated. Initially, the optimal well location problem is modeled, taking into account reservoir characteristics and geological conditions. Using the PSO and GA algorithms, the best well location and number of water injection wells have been obtained, leading to improved efficiency and cost reduction. Then, the GA algorithm is used to combine and modify viewpoints and optimize the number and location of wells. This algorithm explores optimal solutions in the well location space. Economic evaluation is performed using the best-optimized parameters by the algorithms. Costs of water injection, oil production costs, and profits from increased oil production are considered. By calculating the best Net Present Value (NPV), optimal decisions regarding the water injection process and enhancing the performance of oil reservoirs are made. Simulation results indicate that the GA algorithm outperforms other algorithms and can be used as a robust and effective method for optimizing well locations and the number of production and injection wells in the oil injection process. Ultimately, this study assists the oil industry in making better decisions about optimizing water injection and improving the efficiency and profitability of the process using the introduced optimization algorithms. [ABSTRACT FROM AUTHOR]

Heart failure is an incurable disease and shows general symptoms. The presence of general symptoms contrary to specific indications makes early diagnosis difficult. This study aims to obtain clear outputs with 12 different features provided by the UCI public dataset in the research space where there are uncertainties regarding the diagnosis of heart failure. For this, a neural network-based Crocodile and Egyptian Plover (CEP) optimization algorithm has been developed. This algorithm is based on the phenomena of the Egyptian plover fed with food scraps from the crocodile's teeth, and it models mutual benefit. In the first stage of the model, the starting points of the Egyptian plovers are randomly assigned to the crocodile's mouth, and the eating amount of the Egyptian plovers is calculated with the determined parameters. Then, the proportion of plovers-specific traits in the entire population is determined for each iteration adaptively, and the local best and global best parameters are calculated using the cost function. With these processes continuing until the number of iterations is concluded, the proposed CEP algorithm converges to the global optimum without getting stuck to local optima. Finally, the artificial neural network that is capable of learning relationships and patterns between data and the CEP algorithm is used together to optimize success. To show the effectiveness of the proposed approach, its performance is compared with five other algorithms and five different datasets. In most cases, the near-optimal solutions obtained by this hybrid structure are better than the outputs obtained by similar algorithms. [ABSTRACT FROM AUTHOR]

This work presents an ultra-broadband metamaterial absorber in the wavelength range of 250–4000 nm. The absorber consists of a Ti disk resonator and a stack of TiO2/Ti square-shaped resonators, supported by TiO2/Ti thin layers. This arrangement of the layers offers a metal–insulator-metal configuration, enhancing the absorptivity of the structure. The effects of geometrical parameters, including the thickness of the resonators, the radius of the disk resonators, and the width of the square resonator, on the absorption spectrum of the absorber are investigated. To attain the highest average absorption, the particle swarm optimization (PSO) algorithm is employed. The simulation results obtained by the finite-difference time-domain method indicate that the average absorption can reach a high value of 96.25% over the studied wavelength range. The over 90% absorption bandwidth is 3509 nm. Additionally, the solar absorption of the absorber is 94.89%. The absorption is more than 80% even for incident angles up to 50° for both TM and TE polarizations. The proposed absorber is a very promising option for solar energy harvesting, photo-thermal technology, photo-detection, and thermal-photovoltaics applications due to its high over 90% bandwidth, independence on the polarization and angle of incident light, and ease of fabrication. [ABSTRACT FROM AUTHOR]

[Objective] Compound attitude control based on aerodynamic, thrust vector, direct force, and other manipulation structures is a hot research topic in modern aeronautics and astronautics. Additionally, improving the tracking ability of the system under high altitude and low aerodynamic efficiency is essential. To simplify the design, direct force is generally regarded as a continuous control variable in the study of compound attitude control for aircraft, which is different from the actual discrete engineering characteristics. In view of the large gap between direct force continuous processing and actual engineering characteristics, this study proposes control system construction and control parameter optimization methods based on the discrete characteristics of direct force. [Methods] First, based on the operational characteristics of each manipulation structure, a pulse modulator, which embodies the discrete characteristics of direct force, is used to construct the framework of the attitude control system. The Simulink simulation model of the compound aircraft attitude control system is constructed using MATLAB software according to the designed control system structure and the mathematical model of each link. Second, given the complex characteristics of nonlinear, strong coupling, and multiconstraints for the compound control system, the Particle Swarm Optimization (PSO) algorithm is adopted to optimize the control law and control assignment parameters based on the algorithm framework of intelligent control and intelligent distribution laws. Thus, an intelligent control system is constructed. Third, the research method for intelligent control problems is explained through digital simulation from three aspects: control algorithm, PSO algorithm parameters, and aircraft system parameters. [Results] The simulation results show that the proposed method greatly improves the control performance of the attitude-tracking problem, including steady-state error, rise time, adjustment time, and overshoot. It is obviously superior to the traditional PID control method in terms of tracking precision, speed, and security. Additionally, it is beneficial to overcome the difficulty of adjusting the control parameters caused by the change in the flight parameters. The proposed system can adapt to real-time changes in the flight environment and state, effectively exert the control efficiency of each executing agency, and improve the adaptive control ability of the system. Finally, based on the above research achievements, the experimental system for intelligent aircraft attitude control is designed and developed. This system is applied to the practical teaching of intelligent control theory and process control systems. It can also be used to strengthen students' understanding of the application and innovation of intelligent control theory in actual engineering problems from the aspects of attitude control principle, control structure design, mathematical model building, intelligent control algorithm design and implementation, and algorithm research. [Conclusions] This experimental system combines the engineering problems of aircraft attitude control with experimental teaching. By guiding students to project-based learning independently, it stimulates their interest in learning, effectively improves their ability to combine theory with practice, helps them to master scientific research methods, and cultivates and promotes students' practical and innovative abilities. In this way, an independent and open experimental research platform for the practical teaching of intelligent control courses and the cultivation of innovative talents is provided. [ABSTRACT FROM AUTHOR]

In order to solve the problem that it is difficult to accurately model the complex fluid domain in the negative pressure chamber of the robot in the process of wall climbing, and thus cannot quickly and steadily adsorb, a double closed-loop control strategy based on parameter identification and particle swarm optimization is proposed in this paper to improve the speed and stability of the adsorption response of the robot in the process of wall climbing. Firstly, a vacuum negative pressure wall climbing robot with a spring pressure regulating structure is developed, and the critical conditions of the robot's instability are solved by mechanical analysis. Then, the rigid-flexible coupling model of the whole machine was established based on mass, spring and Maxwell viscoelastic elements. The model parameters of the adsorption system were identified by vacuum adsorption test. Compared with the experimental data, the accuracy of the model was more than 80%. On this basis, PID controller is used to adjust the adsorption response speed of the robot, a double closed-loop stable adsorption control strategy is designed, and the control parameters of the adsorption system are adjusted by using the dynamic weight standard particle swarm optimization algorithm. Finally, simulation and prototype tests show that, the proposed control strategy can shorten the static and dynamic response time of the adsorption system by about 3.5 s and 1.4 s, the flow response time by about 1.15s, the maneuvering performance of the whole system is improved by about 26%, and the parameter overshoot and steady-state error are lower, which provides a theoretical reference for the stability control and engineering application of the wall-climbing robot. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things integrates modern technologies such as intelligent terminals, computer technology, and big data, achieving low cost and high applicability in industrial production processes, and improving industrial production efficiency. The offloading of IoT data traffic requires significant energy consumption due to limited mobile terminal device resources. For this reason, the author designs a method of IoT data traffic unloading based on mobile edge computing. The initialization simulation parameters include the number of mobile users, the number of base stations equipped with edge servers, fixed bandwidth, base station height, etc. Calculate the distance and channel power gain from each base station to mobile users, and optimize power allocation through algorithms such as simulated annealing and PSO. The binary PSO algorithm is used to maximize the welfare in edge computing. Finally, by comparing with local offloading, utilizing layered offloading methods, and collaborative computing offloading methods based on fiber wireless networks. Simulation results: The energy consumption of SAPA is generally higher than that of PSO, and with the increase of mobile users, the energy consumption of both algorithms shows a significant growth trend. Especially, SAPA's energy consumption is significantly higher than PSO when the number of users is 40 and 60. This indicates that in the mobile network environment, PSO algorithm has more advantages in energy consumption than SAPA, By using particle swarm optimization algorithm to further optimize energy consumption, it greatly saves energy consumption. In comparison to local execution, the proposed offloading method yields substantial energy savings of nearly 62.5 %. The maximum difference in energy consumption between the proposed method and the collaborative computing offloading method based on fiber optic wireless networks is 268 J, while the maximum difference compared to the layered offloading method is 150 J. These results highlight the strategy's ability to enhance the computational efficiency of high-priority tasks on edge servers, concurrently reducing latency and energy consumption for task completion. This underscores the effectiveness of the proposed offloading method in conserving energy and emphasizes its practical significance.

Urban Park Green Space (UPGS) is an essential component of cities, with a wide range of important services. There is a keener demand for green space in the process of urbanization. Previous studies have extensively evaluated the residents’ accessibility to green space, which belonging to the demand of UPGS’s social service function, but rarely combined the ecological service demand to conduct a refined calculation. This may lead to biased results in the evaluation of the satisfaction of green space. Considering the scarcity of urban land resources, it is difficult to support the efficient use of space based on these results. To fill this gap, this study evaluated the accessibility of various service functions that UPGS can provide from a variety of demand perspectives in central Beijing using multi-source geospatial big data. The results show that: (1) The accessibility results may be overestimated when ignoring eco-service demand. (2) Regions with worse or poor accessibility have the largest proportion, and accessibility presents an obvious low–high-low cyclic structure. (3) Urban peripheral areas are always in a significant disadvantaged position in terms of accessibility. Based on the accessibility results, the solution is further provided by using PSO algorithm to simulate the location of new parks to solve the problem of insufficient UPGS supply in the low accessibility area. Our study can provide better insights for city managers to formulate effective green space management strategies and references for optimization strategy.

Abstract With the development of Internet technology, the large number of network nodes and dynamic structure makes network security detection more complex, which requires the use of a multi-layer feedforward neural network to build a security threat detection model to improve network security protection. Therefore, the entropy model is adopted to optimize the particle swarm algorithm to decode particles, and then the single-peak and multi-peak functions are used to test and compare the particle entropy and fitness values to optimize the weights and thresholds in the multi-layer feedforward neural network. Finally, Suspicious Network Event Recognition Dataset discovered by data mining is sampled and applied to the entropy model particle swarm optimization for training. The test results show that there are four functions for the optimal mean and standard deviation in this algorithm, with values of 5.712e − 02, 4.805e − 02, 4.914e − 01, 1.066e − 01, 1.577e − 01, 1.343e − 01, and 2.089e + 01, 5.926, respectively. Overall, the algorithm proposed in the study is the best. Finally, the detection rate of attack types is calculated. The multi-layer feedforward neural network algorithm is 83.80%, the particle swarm optimization neural network algorithm is 91.00%, and the entropy model particle swarm optimization algorithm is 95.00%. The experiment shows that the research model has high accuracy in detecting network security threats, which can provide technical support and theoretical assistance for network security protection.

In order to meet the efficiency and smooth trajectory requirements of the casting sorting robotic arm, we propose a time-optimal trajectory planning method that combines a heuristic algorithm inspired by the behavior of the Genghis Khan shark (GKS) and segmented interpolation polynomials. First, the basic model of the robotic arm was constructed based on the arm parameters, and the workspace is analyzed. A matrix was formed by combining cubic and quintic polynomials using a segmented approach to solve for 14 unknown parameters and plan the trajectory. To enhance the smoothness and efficiency of the trajectory in the joint space, a dynamic nonlinear learning factor was introduced based on the traditional Particle Swarm Optimization (PSO) algorithm. Four different biological behaviors, inspired by GKS, were simulated. Within the premise of time optimality, a target function was set to effectively optimize within the feasible space. Simulation and verification were performed after determining the working tasks of the casting sorting robotic arm. The results demonstrated that the optimized robotic arm achieved a smooth and continuous trajectory velocity, while also optimizing the overall runtime within the given constraints. A comparison was made between the traditional PSO algorithm and an improved PSO algorithm, revealing that the improved algorithm exhibited better convergence. Moreover, the planning approach based on GKS behavior showed a decreased likelihood of getting trapped in local optima, thereby confirming the effectiveness of the proposed algorithm.