Flight Distribution Research Articles

An Efficient Modified Black Widow Optimized Node Localization in Wireless Sensor Network

Wireless Sensor Network (WSN) is an encouraging technology in various applications like atmosphere monitoring, vehicular systems, and targeting devices. Localization in WSN is performed to identify the current position or coordinates of sensor nodes. The crucial task of localization is to find coordinates without manual configuration and expensive hardware like GPS. This work proposes a new localization algorithm using Modified Black Widow Optimization (MBWO). The proposed method involves an optimal selection of anchor nodes to reduce localization errors. Black Widow Optimization (BWO) is a naturally inspired optimization algorithm encouraged by the unique coupling behaviour of black widow spiders. The performance of black widow optimization is improved by levy flight distribution to escape from local minima. Experimental results show that the proposed MBWO-optimized localization achieves a higher localized node count with minimized localized error compared to other optimization algorithms.

Gaussian process regression with levy flight optimization: Advanced AR66 adsorption studies

The coal fly ash (CFA), which is the residue generated by coal-fired power plants, was converted into a valuable zeolite material known as zeolite P (ZNa-P) through thermal and acid pretreatments followed by microwave radiation. Various analytical techniques were utilized to analyze the resulting zeolite, including X-ray diffraction, scanning electron microscopy, Fourier transform infrared spectroscopy, thermogravimetric analysis, BET analysis, and zeta potential measurement. The efficiency of ZNa-P in eliminating anionic dyes from aqueous solutions was exhibited by successfully removing acid red dye 66 (AR66) from a solution composed of water. To optimize the removal process, Central Composite Design (CCD) was applied to investigate the impact of four main parameters: solution pH, initial dye concentration, adsorbent mass, and contact time. The generated CCD database was modeled using Gaussian process regression (GPR) with the Lévy flight distribution (LFD) optimization algorithm. The GPR model was then used to determine optimal conditions for maximum AR66 absorption (3405.3 mg/g), with a pH of 2, initial dye concentration of 1000 mg/L, adsorbent mass of 0.2 g/L, and contact time of 11 minutes. Furthermore, the GPR model exhibited significantly lower error (32.58 mg/g) in predicting the experimental values compared to the CCD model (204.92 mg/g), highlighting the efficiency and superiority of the GPR model in this study.

Design and Development of Quadcopter for Agro-Chemical Spray in Agricultural Field

The advent of Agricultural Drones promises to revolutionize modern farming practices by employing autonomous drones for crop surveillance and pesticide application. Outfitting quadcopters with spraying mechanisms presents numerous advantages over traditional methods. Firstly, it facilitates precise and targeted application of agro-chemicals, thereby minimizing wastage and environmental pollution. Furthermore, quadcopters exhibit the capability to effortlessly navigate challenging terrains, accessing areas previously unreachable by conventional spraying equipment and ensuring comprehensive crop coverage. Additionally, their autonomous operation or remote human supervision enhances operational efficiency, resulting in reduced labor costs. This initiative explores the integration of unmanned aerial vehicles (UAVs) into agricultural practices, specifically emphasizing their role in precisely spraying agro-chemicals. The project encompasses the entire development cycle, from design and development to the deployment of customized agricultural drones capable of autonomous flight and accurate pesticide distribution. The incorporation of quadcopters for precise agro-chemical spraying marks a significant advancement in agricultural technology, offering precision, efficiency, and sustainability. Ongoing research, development, and collaboration play a crucial role in realizing the transformative benefits of this innovative approach, ultimately enhancing agricultural productivity and environmental stewardship.

Prompt gamma timing for proton range verification with TlBr and TlCl as pure Cherenkov emitters

Objective. Prompt gamma timing (PGT) uses the detection time of prompt gammas emitted along the range of protons in proton radiotherapy to verify the position of the Bragg peak (BP). Cherenkov detectors offer the possibility of enhanced signal-to-noise ratio (SNR) due to the inherent physics of Cherenkov emission which enhances detection of high energy prompt gamma rays relative to other induced uncorrelated signals. In this work, the PGT technique was applied to 3 semiconductor material slabs that emit only Cherenkov light for use in a full scale system: a 3 × 3 × 20 mm3 TlBr, a 12 × 12 × 12 mm3 TlBr, and a 5 × 5 × 5 mm3 TlCl. Approach. A polymethyl methacrylate (PMMA) target was exposed to a 67.5 MeV, 0.5 nA proton beam and shifted in 3 mm increments at the Crocker nuclear laboratory (CNL) in Davis, CA, USA. A fast plastic scintillator coupled to a photomultiplier tube (PMT) provided the start reference for the proton time of flight. Time of flight (TOF) distributions were generated using this reference and the gamma-ray timestamp in the Cherenkov detector. Main results. The SNR of the proton correlated peaks relative to the background was 20, 29, and 30 for each of the three samples, respectively. The upper limit of the position resolutions with the TlCl sample were 2 mm, 3 mm, and 5 mm for 30k, 10k, and 5k detected events, respectively. The time distribution of events with respect to the reference reproduced with clarity the periodicity of the beam, implying a very high SNR of the Cherenkov crystals to detect prompt gammas. Background presence from the neutron-induced continuum, prompt gammas from deuterium, or positron activation were not observed. Material choice and crystal dimensions did not seem to affect significantly the outcome of the results. Significance. These results show the high SNR of the pure Cherenkov emitters TlBr and TlCl for the detection of prompt gammas in a proton beam with current of clinical significance and their potential for verifying the proton range. The accuracy in determining shifts of the BP was highly dependent on the number of events acquired, therefore, the performance of these detectors are expected to vary with different beam conditions such as current, pulse repetition, and proton bunch width.

Improved Driving Training-Based Optimization Algorithm Using Levy Flight and Crowding Distance Techniques

This study presents an improved version of the Driving Training-Based Optimization (DTBO) algorithm, the Improved Driving Training-Based Optimization (IDTBO). The work addresses fundamental issues in selecting drivers and learners for the conventional DTBO, which substantially impact the algorithm's accuracy and convergence speed. Two significant improvements are proposed: including the crowding distance technique for more diverse driver and learner selection and incorporating the Levy Flight distribution for better exploration and local optima avoidance. The IDTBO's performance is evaluated using twelve benchmark functions, including unimodal and high-dimensional multimodal optimization functions. The results indicate that IDTBO performs exceptionally well, with more extraordinary exploitation ability on unimodal functions and consistent achievement of the global optima. The proposed IDTBO demonstrated exceptional exploration capabilities on high-dimensional multimodal functions and performed competitively with other algorithms in the literature. From six functions, the IDTBO obtained zero optimal values. Again, the rate of convergence analysis demonstrates that IDTBO finds optimal solutions in fewer iterations, demonstrating its capacity to balance exploration and exploitation. To assess the strength of the IDTBO in solving real-world problems, the improved DTBO is further tested on two practical benchmark engineering problems. The IDTBO again produced a competitive performance against other algorithms in the literature. The study shows that IDTBO is a valuable metaheuristic algorithm that can tackle various real-world optimization problems.

Thermodynamic study and the development of a support vector machine model for predicting adsorption behavior of orange peel-derived beads in wastewater treatment

This study investigates the use of orange peels as a precursor for synthesizing sodium alginate-encapsulated beads for methylene blue (MB) removal. The prepared beads (BOP1 and BOP2) underwent characterization through FTIR, XRF, SEM and TGA. Subsequently, the impacts of various factors, including temperature, the initial pH, initial concentration, salt and humic acid, are studied. The adsorption isotherms show high adsorbed quantities of 764.92 and 659.78 mg/g for BOP1 and BOP2 respectively, while the obtained data are best described by the monolayer with two energies (MMTE) model, which is then used to perform a thermodynamic study of the MB adsorption mechanism. Additionally, the adsorption kinetics data are modeled using three models, with the PFO model identified as the most appropriate. The regenerated beads demonstrate the ability to be reused up to 7 cycles, The effects of NaCl and humic acid on MB adsorption reveal that NaCl inhibits adsorption due to competition with Na+, while humic acid has no effect. Finally, a support vector machine (SVM) model optimized by the Levy Flight Distribution Optimization (LFD) algorithm is developed and found to be capable of accurately predicting the adsorption behavior of the prepared beads. This model is then used in optimizing the process conditions for maximal MB removal. Overall, this study demonstrates that the prepared beads could be potential low-cost and environmentally friendly adsorbents for wastewater treatment applications.

A multi-method assessment of the regional sensitivities between flight altitude and short-term O3 climate warming from aircraft NO x emissions

Flight altitude is relevant to the climate effects resulting from aircraft emissions. Other research has shown that flying higher within the troposphere leads to larger warming from O3 production. Aircraft NO x emissions are of particular interest, as they lead to warming via the short-term production of O3, but also to reduced warming via processes like CH4 depletion. We focus on short-term O3 production, as it constitutes one of aviation’s largest warming components. Understanding how O3 formation varies altitudinally throughout the upper troposphere/lower stratosphere is essential for designing climate-compatible aircraft and routing. We quantify this variation by performing simulations with a global atmospheric chemistry model for three representative cruise altitudes, five regions and two seasons using three methods: Eulerian tagging, perturbation and Lagrangian tagging. This multi-method, regional approach overcomes limitations of previous studies that utilize only one of these methods and apply global emission inventories biased towards present-day flight distributions, thus limiting their applicability to future aviation scenarios. Our results highlight that underrepresenting emissions in areas with growing flight activity (e.g. Asia Pacific) may lead to significant, regional underestimations of the altitudinal sensitivity of short-term NO x -related O3 warming effects in certain cases. We find that emitting in Southern regions, like Australasia, leads to warming larger by a factor of two when compared to global averages. Our findings also suggest that flying lower translates to lower warming from short-term O3 production and that this effect is strongest during the local summer. We estimate differences ranging from a factor of 1.2–2.6 between tagging and perturbation results that are attributable to non-linearities of NO x -O3 chemistry, and derived regional correction factors for a widely-used sub-model. Overall, we stress that a combination of all three methods is necessary for a robust assessment of aviation climate effects as they address fundamentally different questions.

Positioning multiple optical network units in fiber-wireless networks: An efficient hybrid [formula omitted]-harmonic means clustering approach

Wireless-optical broadband access network (WOBAN) or fiber-wireless (FiWi) is a robust and cost-effective solution to provide high bandwidth network connectivity to mobile subscribers. Optical network unit (ONU) placement is a topological optimization problem that plays a vital role in effective deployment of FiWi network. Recently, some artificial intelligence-based approaches for positioning ONUs were proposed and generated promising results. However, the reported performance in the literature is far from the ideal state. This issue emphasizes the necessity of introducing new placement methods or improving the performance of existing methods. This paper presents a hybrid clustering algorithm for finding the optimal position of multiple ONUs. The proposed backtracking K-harmonic means (BKHM) is an integration of K-harmonic means (KHM) and a backtracking search algorithm (BSA). BKHM not only efficiently specifies the optimal positions of ONUs but also significantly decreases the implementation cost. To demonstrate the performance of the BKHM, we evaluate it on well-known placement benchmarks compared with BSA, KHM, whale optimization algorithm (WOA), enhanced backtracking search (EBS), harris hawks optimization (HHO), and chaotic local search-based levy flight distribution (CLF) algorithms. The experimental results and statistical analysis proved that the proposed BKHM algorithm outperformed its counterparts on most benchmarks in terms of cost optimization, convergence accuracy, and solution quality.

A novel opposition-based hybrid cooperation search algorithm with Nelder–Mead for tuning of FOPID-controlled buck converter

This paper introduces a novel metaheuristic algorithm named the opposition-based cooperation search algorithm with Nelder–Mead (OCSANM). This enhanced algorithm builds upon the cooperation search algorithm (CSA) by incorporating opposition-based learning (OBL) and the Nelder–Mead simplex search method. The primary application of this algorithm is the design of a fractional-order proportional–integral–derivative (FOPID) controller for a buck converter system. A comprehensive evaluation is conducted using statistical boxplot analysis, nonparametric statistical tests and convergence response comparisons to assess the algorithm’s performance and confirm its superiority over CSA. Furthermore, the FOPID-controlled buck converter system based on OCSANM is compared with two top-performing algorithms: one using a hybridized approach of Lévy flight distribution with simulated annealing (LFDSA) and the other employing the improved hunger games search (IHGS) algorithm. This comparison encompasses transient and frequency responses, performance indices and robustness analysis. The results reveal the notable advantages of the proposed OCSANM-based system, including 25.8% and 8.7% faster rise times, 26% and 8.8% faster settling times compared with the best-performing approaches, namely LFDSA and IHGS, respectively. In addition, the OCSANM-based system exhibits a 34.7% and 9.6% wider bandwidth than the existing approaches-based systems. Incorporating voltage and current responses of the buck converter’s switched circuit with the OCSANM-based FOPID controller further underscores the algorithm’s effectiveness. To provide a comprehensive assessment, the paper also compares the proposed approach’s time and frequency domain responses with those of 17 other state-of-the-art approaches attempting to control buck converter systems similarly. These findings affirm the effectiveness of the OCSANM in designing FOPID controllers for buck converter systems.

A Modified Artificial Hummingbird Algorithm for solving optimal power flow problem in power systems

Optimal power flow (OPF) problem solution is a crucial task for the operators and decision makers to assign the best setting of the system components to obtain the most economic, environmental, and technical suitable state. Artificial Hummingbird Algorithm is a recent optimization algorithm that has been applied to solving several optimization problems. In this paper, a Modified Artificial Hummingbird Algorithm (MAHA) is proposed for improving the performance of the orignal Artificial Hummingbird Algorithm as well as effectivelly solve the OPF problem. The proposed MAHA is based on improving the searching capability by boosting the exploitation using the bandwidth motion around the best solution, while the exploration process is improved using the Levy flight distribution motion and the fitness-distance balance selection. This modified version helps overcome issues such as stagnation, premature convergence, and a propensity for local optima when tackling complex, nonlinear, and non-convex optimization problems like OPF. In order to confirm the effectiveness of the proposed algorithm, a series of tests are conducted on 23 standard benchmark functions, including CEC2020. The resulting outcomes are then compared to those obtained using other algorithms such as fitness-distance balance selection-based stochastic fractal search (FDBSFS), antlion optimizer (ALO), whale optimization algorithm (WOA), sine-cosine algorithm (SCA), fitness-distance balance and learning based artificial bee colony (FDB-TLABC), and traditional artificial hummingbird algorithm (AHA).The proposed algorithm is evaluated by solving the OPF problem with multiple objective functions on the IEEE 30-bus system. These objectives include fuel cost, fuel cost with valve loading effects, power losses, emissions, and voltage profile. Additionally, the algorithm's effectiveness is further assessed by testing it on single objective functions using medium and large-scale IEEE 57 and 118-bus networks.The results obtained by the proposed MAHA demonstrate its power and superiority for solving the OPF problem as well as the standard benchmark functions , surpassing the performance of other reported techniques.

An efficient lung nodule detection model using multiscale hybrid 2D-3D dilation assisted adaptive Trans-Res-Unet++ with hybrid heuristic mechanism

As lung nodules are only more widely detectable once they have moved to other lung sections, it is highly challenging to anticipate the incidence of lung cancer at the beginning stages. Even before radiologists and other specialized medical professionals determine whether a lung nodule exists or not, the danger increases as time increases. The demand for Computer-Aided Detection (CAD) systems to accurately classify lung nodules into malignant or benign types has arisen as a result of improvements in medical imaging techniques like Computed Tomography (CT) scans, as this prevents delays in lung nodule diagnosis. In order to identify the lung nodule and deliver better and more prompt timely therapy, a new deep learning-based Lung Nodule Detection Model (LNDM) is created in this paper. Lung nodule detection and image collection make up the ideal functions of the created model. The lung nodule images are initially gathered from the common standard datasets. Then, the acquired images are used with Multiscale Hybrid 2D-3D Dilation assisted Adaptive Trans-Res-UNet++ (MHD-ATRes-UNet++) approaches for lung nodule diagnosis. This method combines ResNet and UNet++ with a Multiscale mechanism, a 2D-3D dilation layer, and an adaptive transformer layer. The Golden Eagle Optimizer (GEO) and the Levy Flight Distribution Algorithm (LFD) are combined to generate a Population-based Levy Flight Distribution-Golden Eagle Optimization (PLFD-GEO) algorithm, which is used in this instance to detect lung nodules. Eventually, the constructed network produces lung nodule detection results. Using a variety of performance indicators, the created lung nodule detection model's effectiveness is examined.

Neuro‐Adaptive Finite Time Composite Fault Tolerant Control for Attitude Control Systems of Satellites

AbstractAn adaptive finite time composite fault tolerant control strategy based on an optimized neural network for Attitude control systems (ACSs) of satellites is proposed considering the state time‐varying delays, concurrent actuator and sensor faults, system uncertainties, modelable external disturbance and operating noise. An uncertain time‐varying state space model for ACSs of satellites is established, and sensor faults are equivalent to actuator‐like faults. A disturbance observer is designed for estimating the modelable external disturbance, and an improved dwarf mongoose optimization (DMO) algorithm based on the Levy flight distribution is utilized to optimize the basis function of hyperbasis function neural networks to better estimate the augmented actuator faults that include the actuator fault and the actuator‐like fault. Furthermore, an adaptive finite time composite fault‐tolerant controller is proposed, which includes the delay‐dependent feedback control law, disturbance estimation based‐disturbance compensation law and the adaptive fault compensation law based on the augmented fault estimation using the improved DMO‐hyper basis function neural network. The finite time boundness of the close‐loop dynamics to the uncertainties, operating noise, and augmented actuator faults and the robustness of the measurement to the uncertainties, operating noise and augmented actuator faults are analyzed, and the observer and controller design is formulated as the linear matrix inequalities. Simulation examples for ACSs in different working conditions are considered to exhibit the proposed method's effectiveness.

Improving Kinnow Fruit Classification with Feature Weighting using Modified Levy Flight Distribution-Inspired Sunflower Optimization Technique

In this paper, Feature weighting is used to create an intelligent and effective classification method for Kinnow fruits. Feature weighting approach is used because it improves classification performance more than feature selection methods. The modified sunflower optimization algorithm (SFO) is proposed to search the optimal feature weights and parametric values of k-Nearest Neighbour (kNN).The levy flight distribution operator has been utilised to enhance the convergence speed of the sunflower optimization algorithm by improving the local and global search ability of the optimization algorithm. Also, the algorithmic parameter of the SFO algorithm has been adaptively selected using the linear time varying adaption method. In addition, tanh normalization technique is used for the data pre-processing to reduce the influence of outliers and dominating features before the feature weighting method. The findings suggest that the proposed wrapper based approach feature weighting technique is more capable of achieving higher accuracy than the existing strategies.

Accuracy assessment of UAS photogrammetry and structure from motion in surveying and mapping

Rapid and accurate surveying has always attracted great interest in all scientific and industrial activities that require high-resolution topographic data. The latest automation and advancement in geomatics engineering are remote sensing solutions using Unmanned Aerial Systems (UAS) and Structure from Motion (SfM) with Multi-View Stereo (MVS) photogrammetry. This research aimed to find the influence of flight height, Ground Control Point (GCP), and software on the geometric accuracy of UAS-SfM-derived Digital Surface Models (DSMs) and orthoimages, as well as to analyze and evaluate the accuracy of UAS-SfM as a rapid and low-cost alternative to conventional survey methods. To achieve the aim of the study, aerial surveys using a fixed-wing UAS and field surveys using RTK GNSS and total station were conducted. A total of 16 photogrammetric projects were processed using different GCP configurations, and detailed statistical analysis was performed on the results. Moreover, the contribution of cross flight on bundle adjustment was investigated empirically by conducting a combined photogrammetric image processing. The analysis revealed that flight height, GCP number and distribution, and the processing software significantly affect products' quality and accuracy. Evaluation of the achieved accuracies was made based on the American Society for Photogrammetry and Remote Sensing (ASPRS) positional accuracy standard for digital geospatial data. The findings of this study revealed that using the optimal flight height and GCP configuration, 3D models, orthomosaics and DSMs can be rapidly reconstructed from 2D images with the quality and accuracy sufficient for most terrain analysis applications, including civil engineering projects.

Enhanced Cascaded Converters for Switched Reluctance Motor-Fed Electric Vehicles

This manuscript proposes an optimization technique for electric vehicles (EVs) operated through solar photovoltaic (PV) panels by enabling the drive of a switched reluctance motor (SRM) and an enhanced cascaded converter (ECC). The proposed method is an improved Tunicate Swarm Algorithm (ITSA). The Lévy flight distributions are emerging at the conventional Tunicate Swarm Algorithm (TSA) in the modified Tunicate Swarm Algorithm, which enhances the capabilities of diversification searching and avoids the potential for stagnation. The major objective of the proposed method is to minimize the ripple present in the supply current and give optimal photovoltaic power extraction to the solar module. The proposed enhanced cascaded converter method consists of a single main converter and one auxiliary converter combined as a series structure for the distribution of minimal voltage across the switch to restrict the switch stress of dv/dt. These quantities of error are lessened by the controller of Proportional–integral; the PI gain controller is tuned to the error and provides a reference current signal by using the proposed method. An optimum voltage gain and continuous input current minimize the impact of less solar irradiation in EV applications. Finally, the performance of the proposed method is implemented in the MATLAB platform and compared with existing methods.

Two level blockchain-based privacy preservation framework in IoT with heuristic fusion mechanism-aided deep learning architecture

The Internet of Things (IoT) connects more objects used in the modern world. To connect IoT nodes, it needs maintenance services, robustness, frictionless authentication, and high security. Blockchain emerges as a workable solution by providing those important features. Several authentication problems, maintenance, and security with IoT systems are solved because of blockchain technology. The blockchain-based IoT network is an open source, and everyone may see encrypted keys and transactional information. So, from this public network, anyone can take crucial information about users. A new blockchain-adopted IoT-based privacy preservation approach is developed to provide higher security with less utilization of the cost requirements. Here, two levels of data privacy is accomplished by utilizing the deep learning approach. The first level of privacy is used for authenticating a person, where the authentication among collected data is performed using Modified Adaboost and Long Short-Term Memory (MABLSTM), and the authenticated data is stored in the blockchain database. After that, the second level of privacy is obtained by performing data encoding with the encoder part of Autoencoder, and further Elliptic Curve Cryptography (ECC) is used to encrypt the data. Hence, the encrypted data is given to the data decryption process, and this has been subjected to the decoder part of the autoencoder to reconstruct the original data. Moreover, the parameters in the autoencoder and the parameters within the MABLSTM are optimized with the help of the developed Modified Levy Flight Distribution with Grasshopper Optimization (MLFD-GO). However, the parameters like LSTM and Adaboost are optimized with the help of the MLFD-GO algorithm to enhance the performance in terms of accuracy and precision for providing the higher level authentication scheme to provide high level security. Finally, the performance of the developed blockchain-based privacy preservation approach is validated over recently developed techniques.

Energy Management of Microgrids with a Smart Charging Strategy for Electric Vehicles Using an Improved RUN Optimizer

Electric vehicles (EVs) and renewable energy resources (RERs) are widely integrated into electrical systems to reduce dependency on fossil fuels and emissions. The energy management of microgrids (MGs) is a challenging task due to uncertainty about EVs and RERs. In this regard, an improved version of the RUNge Kutta optimizer (RUN) was developed to solve the energy management of MGs and assign the optimal charging powers of the EVs for reducing the operating cost. The improved RUN optimizer is based on two improved strategies: Weibull flight distribution (WFD) and a fitness–distance balance selection (FDB) strategy, which are applied to the conventional RUN optimizer to improve its performance and searching ability. In this paper, the energy management of MGs is solved both at a deterministic level (i.e., without considering the uncertainties of the system) and while considering the uncertainties of the system, with and without a smart charging strategy for EVs. The studied MG consists of two diesel generators, two wind turbines (WTs), three fuel cells (FCs), an electrical vehicle charging station and interconnected loads. The obtained results reveal that the proposed algorithm is efficient for solving the EM of the MG compared to the other algorithms. In addition, the operating cost is reduced with the optimal charging strategy.

Revolutionizing Vehicle Cruise Control: An Elite Opposition-Based Pattern Search Mechanism Augmented INFO Algorithm for Enhanced Controller Design

This paper presents a groundbreaking approach to enhance the performance of a vehicle cruise control system—a crucial aspect of road safety. The work offers two key contributions. Firstly, a state-of-the-art metaheuristic algorithm is proposed by augmenting the performance of the weighted mean of vectors (INFO) algorithm using pattern search and elite opposition-based learning mechanisms. The resulting boosted INFO (b-INFO) algorithm surpasses the original INFO, marine predators, and gravitational search algorithms in terms of performance on benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions. Secondly, a novel proportional, fractional order integral, derivative plus double derivative with filter (P{I}^{lambda }DN{D}^{2}{N}^{2}) controller is proposed as a more efficient control structure for vehicle cruise control systems. An objective function is utilized to determine the optimal values for the controller parameters, and the proposed method's performance is compared against a range of recent approaches. Results demonstrate that the b-INFO algorithm-based P{I}^{lambda }DN{D}^{2}{N}^{2} controller is the most efficient and superior method for controlling a vehicle cruise control system. Moreover, this work represents the first report of a P{I}^{lambda }DN{D}^{2}{N}^{2} controller’s implementation for vehicle cruise control systems, underscoring the novelty and significance of this research. The proposed method's exceptional ability is further confirmed by comparisons with the genetic algorithm, ant lion optimizer, atom search optimizer, arithmetic optimization algorithm, slime mold algorithm, Lévy flight distribution algorithm, manta ray foraging optimization, and hunger games search-based proportional–integral–derivative (PID), along with Harris hawks optimization-based PID and fractional order PID controllers. This work marks a remarkable milestone toward safer and more efficient vehicle cruise control systems.

Experimental investigation on centrifugal granulation of molten yellow phosphorus slag

Centrifugal granulation with waste recovery was proposed to recover the waste heat from the molten yellow phosphorus slag produced by an electric arc furnace and reduce pollution. The yellow phosphorus slag was heated to the same temperature as industrial electric furnaces and then granulated by centrifugal granulation. The study mainly investigated the effect of operating parameters on particle diameter distribution, flight distance distribution, and fiber mass fraction during the granulation process. The results showed that the average diameter was mainly 1.5–3 mm, and tended to decrease with the decrease of the mass flow rate, the increase of the rotating speed, and the increase of the rotary cup diameter. The particle diameter obeyed a log-normal distribution. As the rotating speed increased and the rotary cup diameter increased, the average flight distance and the distribution of flight distance were dispersed. The average flight distance was related to the linear velocity at the edge of the rotary cup. Increasing slag temperature, mass flow rate, and decreasing rotary cup diameter can reduce the fiber mass fraction. Re and We numbers can determine whether fibers produce.

ОПТИМИЗАЦИОННАЯ МОДЕЛЬ ПЕРЕВОЗОК ТВЕРДЫХ КОММУНАЛЬНЫХ ОТХОДОВ ГОРОДСКОЙ АГЛОМЕРАЦИИ

An optimization model of transportation of municipal solid waste (MSW) of the urban agglomeration carried out by the WMS processing and disposal complex is proposed. Optimization is carried out by minimizing the costs of transportation of MSW. The solution of the problem of minimizing costs is carried out in two stages. At the first stage, optimal routes are built along which garbage trucks can transport MSW with minimal costs of a given type (fuel costs, financial costs, etc.). At the second stage, the optimal options for the distribution of garbage truck flights between the optimal routes built at the first stage are determined. Software has been developed for using the model in solving practical problems. In addition, a database has been developed to work with the initial data and the results of solving optimization problems. The results of experimental calculations and their analysis are presented. The model is focused on the urban agglomeration of St. Petersburg.