Local Point Research Articles

Optimal performance and preliminary parameter matching for hydrogen fuel cell powertrain system of electric aircraft

Fuel cells are true net-zero carbon emission power sources for aircraft, which is highly sensitive to weight. In the initial phase of adapting hydrogen fuel cell systems for aircraft powertrains, preliminary design parameter matching remains premature. An explicit method for the performance optimization of aircraft hydrogen fuel cell powertrain systems and a process of preliminary parameter matching are proposed to address this problem. Performance and weight models of the fuel cell stack and its auxiliaries, the cathode air compressor subsystem, and the cooling subsystem are designed, and system performance at various altitudes and power output levels is calculated. The aircraft flight mission performance is synthesized and considered in the optimization process. The optimization result of system performance and the corresponding design parameters are then graphically illustrated as tern plots. Unlike the traditional iterative preliminary system parameter matching and optimization method, which explores the design space non-directionally and converges to a single local optimal point, the proposed explicit method sweeps the design space globally and obtains a group of design points with acceptable optimality. The system design process is boosted by a compact iterative loop. In the optimization practice, the cruise powertrain specific energy is improved by 6.5%. The relationship between specific system design parameters and system performance is displayed globally by the resulting tern plots. Multiple design guidelines are observed and proposed, and design scenarios are directly obtained from the graphs for further engineering processes.

Random Data Distribution for Efficient Parallel Point Cloud Processing

Abstract. Current point cloud data management systems and formats are heavily specialized and targeted solely towards visualization purposes and fail to address the diverse needs of progressive point cloud workflows like for example semantic segmentation using machine learning. We therefore propose a distributed data infrastructure for dynamic point cloud data management that can support interactive real-time visualization at scale while simultaneously serving as a platform for analytical tasks. By introducing random data distribution, we show that simple query fragmentation and efficient and effective parallelism at scale are possible. At the same time, arbitrary queries in space and time can be efficiently run over the infrastructure including query semantics which returns only a random sample of the query results or preferred points based on an importance dimension calculated, for example, from a local point density information as commonly done in point cloud visualization. To cope with the unknown amount of user-specific attributes and to support even multiple ways of deciding the importance of a given point (ground point removal, coverage of space, random subset) the system is designed to support all of them transparently as multidimensional range queries backed by spatial indices.

Erythrocyte aggregability, and fibrinogen during atrial fibrillation

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Academician N. Kipshidze University Clinic - Republican Hospital; IVANE BERITASHVILI CENTER OF EXPERIMENTAL BIOMEDICINE, Department of rheology and diagnostic analytical services. Introduction Despite all biomedical efforts, the prevalence of atrial fibrillation and its associated morbidity and mortality are increasing globally year after year. The disease's unfavorable epidemiological picture determined the reality of the project. There are many hypotheses to explain the pathogenesis of atrial fibrillation. All the hypotheses are based on mechanisms at local points and microwaves (in whole or in part). None of them consider the role of hemodynamics and hemorheology in the formation and progression of atrial fibrillation. It is also unknown how atrial fibrillation affects intracardiac macro- and microcirculation. Purpose The goals of this project were to determine how hemodynamics and hemorheology change in patients with atrial fibrillation. In order to gain a better understanding of the pathological processes underlying AF, we sought to access the relationships between hemodynamics, rheological properties,and the coagulation system of blood. Methods The Erythrocyte (RBC) Aggregation Index (EAI) was monitored in the development and validation cohorts (90 patients and 20 healthy individuals) using the textural analysis system (Tas-Plus, Leitz, Germany) and an innovative "Georgian technique" with the unique function of a quantitative assessment of the EAI. The method was developed by Georgian scientists and is well-known around the world for being direct, numerical, and precise. In all patients Coagulogram and 12-lead standard ECG were performed. Results According to the data obtained, the patients' rheological and coagulation system conditions are the same, and they are distinct from the control group. During the statistical processing of the total amount of fibrinogen (Fib), the patients were divided into three categories. The average Fib in Category I patients was 336.40 ±27,77 mg/dl, and the average EAI-RBC value was 32.8± 9.4. Category II: FIB: 373.00±22.65mg/dl; EAI-RBC: 39.8±13.8 and Category III: FIB:471.27±21.58 mg/dl; EAI-RBC: 42.6±10.4. The control group: FIB: 301±18,1 mg/dl, EAI-RBC: 25.6±1.29. Fibrinogen and the Index of Erythrocyte Aggregation were found to have a linear relationship. The higher the concentration of fibrinogen, the more severe the hemorheological disorders. It should be noted that the categories were chosen without taking into account the forms of atrial fibrillation. Conclusions The findings clearly show the existence of two parallel mechanisms in the body. These are the coagulation systems, characterized by dynamic equilibrium, and the rheology systems, which lack a physiologically balanced opposing system. Coagulation control does not accurately describe hemorheological parameters, which are one of the most powerful mechanisms for atrial fibrillation formation In order to better understand the diseases' occurrence and progression, the simultaneous studies of fibrinogen concentration and rheological parameters are especially important for both fundamental and practical medicine.

A modified EM-type algorithm to estimate semi-parametric mixtures of non-parametric regressions

Semi-parametric Gaussian mixtures of non-parametric regressions (SPGMNRs) are a flexible extension of Gaussian mixtures of linear regressions (GMLRs). The model assumes that the component regression functions (CRFs) are non-parametric functions of the covariate(s) whereas the component mixing proportions and variances are constants. Unfortunately, the model cannot be reliably estimated using traditional methods. A local-likelihood approach for estimating the CRFs requires that we maximize a set of local-likelihood functions. Using the Expectation-Maximization (EM) algorithm to separately maximize each local-likelihood function may lead to label-switching. This is because the posterior probabilities calculated at the local E-step are not guaranteed to be aligned. The consequence of this label-switching is wiggly and non-smooth estimates of the CRFs. In this paper, we propose a unified approach to address label-switching and obtain sensible estimates. The proposed approach has two stages. In the first stage, we propose a model-based approach to address the label-switching problem. We first note that each local-likelihood function is a likelihood function of a Gaussian mixture model (GMM). Next, we reformulate the SPGMNRs model as a mixture of these GMMs. Lastly, using a modified version of the Expectation Conditional Maximization (ECM) algorithm, we estimate the mixture of GMMs. In addition, using the mixing weights of the local GMMs, we can automatically choose the local points where local-likelihood estimation takes place. In the second stage, we propose one-step backfitting estimates of the parametric and non-parametric terms. The effectiveness of the proposed approach is demonstrated on simulated data and real data analysis.

Multi-scale network with attention mechanism for underwater image enhancement

Underwater images suffer from severe degradation due to the complicated environments. Though numerous approaches have been proposed, accurately correcting color bias meanwhile effectively enhancing contrast is still a difficult problem. In order to address this issue, a dedicated designed Multi-scale Network with Attention mechanism (MNA) is introduced in this work. Concretely, MNA contains four key characteristics: (a) setting more convolution layers in shallow flows, (b) letting connections from high-level to adjacent low-level stream progressively, (c) simplifying dual attention mechanism embedding it in conventional residual block, (d) exploiting channel attention module to fuse multi-scale information rather than conventional summation operation. Extensive experiments demonstrate that our MNA achieves better performance than some well-recognized technologies. Meanwhile, ablation study proves the effectiveness of each component in our MNA. In addition, extended applications demonstrate the improvement of our MNA in local feature points matching and image segmentation.

Net escape velocity, transfer probability, and travel time distributions within a cross-ventilated room model sheltered by urban-like block array

Natural ventilation offers significant advantages, especially in terms of energy conservation. However, most published articles have focused on the ventilation flow rate to determine the average contaminant concentration, while few have examined local ventilation distributions. Therefore, isothermal steady-state Reynolds-averaged Navier–Stokes simulations were conducted to analyze the airflow and scalar concentration fields of a cross-ventilation model sheltered by buildings. Based on these fields, we generated the spatial distributions of ventilation indices, namely, the net escape velocity (NEV), point-to-point indices transfer probability (TP), and travel time (TT), to discuss the dispersion of scalars emitted from local points in a room with natural ventilation. The NEV, which indicates the direction of the scalar discharge from each cell, exhibited a distribution distinct from the advection velocity because of concentration gradient effect. Higher TPs were observed when evaluated from a contaminant source to a target point following the NEV streamlines. Meanwhile, lower TTs were observed when evaluated in a point near the source and also from a contaminant source to a target point following the NEV streamlines. Although TP and TT are independent ventilation indices, a negative correlation between them was observed. Instead of the ventilation flow rate, the detailed structure of scalar dispersion can now be described by simultaneously analyzing the ventilation efficiency indices, which provide information on the general contaminant transport direction, and the probability and duration of transfer from a source to a target point. This potentially helps in the design of ventilation system especially in room lay-out to avoid cross-contamination.

TriClsNet: Surface Reconstruction via Graph-based Triangle Classification

In this paper, we introduce TriClsNet, a novel learning-based network that reconstructs surfaces by reframing the triangle classification problem as a graph node classification problem. An improved graph-based triangle classification module is employed to aggregate information from neighboring triangles, effectively leveraging local neighborhood information and enhancing triangle classification accuracy. Additionally, a self-supervised learning branch is incorporated to predict point cloud normals, aiding our network in better learning local point cloud features. Furthermore, a new loss function is designed to guide our network in effective multi-task learning, encompassing both graph node classification and normal prediction. Comparative experimental results on ShapeNet demonstrate that our method can efficiently perform surface reconstruction, outperforming existing methods in the aspects of preserving surface details, reducing holes, and generalization.

DATFNets-dynamic adaptive assigned transformer network for fire detection

Fires cause severe damage to the ecological environment and threaten human life and property. Although the traditional convolutional neural network method effectively detects large-area fires, it cannot capture small fires in complex areas through a limited receptive field. At the same time, fires can change at any time due to the influence of wind direction, which challenges fire prevention and control personnel. To solve these problems, a novel dynamic adaptive distribution transformer detection framework is proposed to help firefighters and researchers develop optimal fire management strategies. On the one hand, this framework embeds a context aggregation layer with a masking strategy in the feature extractor to improve the representation of low-level and salient features. The masking strategy can reduce irrelevant information and improve network generalization. On the other hand, designed a dynamic adaptive direction conversion function and sample allocation strategy to fully use adaptive point representation while achieving accurate positioning and classification of fires and screening out representative fire samples in complex backgrounds. In addition, to prevent the network from being limited to the local optimum and discrete points in the sample from causing severe interference to the overall performance, designed a weighted loss function with spatial constraints to optimize the network and penalize the discrete points in the sample. The mAP in the three baseline data sets of FireDets, WildFurgFires, and FireAndSmokes are 0.871, 0.909, and 0.955, respectively. The experimental results are significantly better than other detection methods, which proves that the proposed method has good robustness and detection performance.

Model checking distributed temporal logic

Abstract The distributed temporal logic (DTL) is a logic for reasoning about temporal properties of distributed systems from the local point of view of the system’s agents, which are assumed to execute sequentially and to interact by means of synchronous event sharing. Different versions of DTL have been provided over the years for a number of different applications, reflecting different perspectives on how non-local information can be accessed by each agent. In this paper, we propose an automata-theoretic model checking algorithm for DTL. To this end, we propose a notion of distributed transition system that will be used to specify the system to be verified. The properties that the system should meet are specified in DTL. In order to capture the models of these properties, we propose the notions of generalized distributed Büchi automaton and of distributed Büchi automaton. With these concepts, we are able to adapt results from automata-theoretic approaches to model checking in LTL to the distributed case.

LGNet: Local and global point dependency network for 3D object detection

3D object detection is a challenging task in autonomous driving industry scenarios. Many pre-existing methods employ the set-abstraction operation for generating key-point representations, which, however, cannot learn the long-range context dependency properly. In addition, the pooling operator, which only focuses on maximum channel response, is adopted to aggregate features of neighbor points without semantic information. To fix these issues, we propose LGNet, a new framework that simultaneously captures local and global point dependencies for enhancing 3D object detection. Specifically, we first introduce a new local point-graph pooling module to compute point-to-point correlations in a local region and aggregate features from neighboring points. To further capture the long-range dependency in a global context, we devised a global point-aware module to integrate local and global features at higher resolution. Experiments on the KITTI 3D detection dataset and Waymo Open Dataset benchmark show that LGNet achieves state-of-the-art performance in multiple classes. We will upload the code on https://github.com/MWPony/LGNet.

3D drift correction for super-resolution imaging with a single laser light.

Single-molecule localization microscopy (SMLM) enables three-dimensional (3D) super-resolution imaging of nanoscale structures within biological samples. However, prolonged acquisition introduces a drift between the sample and the imaging system, resulting in artifacts in the reconstructed super-resolution image. Here, we present a novel, to our knowledge, 3D drift correction method that utilizes both the reflected and scattered light from the sample. Our method employs the reflected light of a near-infrared (NIR) laser for focus stabilization while synchronously capturing speckle images to estimate the lateral drift. This approach combines high-precision active compensation in the axial direction with lateral post-processing compensation, achieving the abilities of 3D drift correction with a single laser light. Compared to the popular localization events-based cross correlation method, our approach is much more robust, especially for datasets with sparse localization points.

Enhanced Path Planning and Obstacle Avoidance Based on High-Precision Mapping and Positioning.

High-precision positioning and multi-target detection have been proposed as key technologies for robotic path planning and obstacle avoidance. First, the Cartographer algorithm was used to generate high-quality maps. Then, the iterative nearest point (ICP) and the occupation probability algorithms were combined to scan and match the local point cloud, and the positions and attitudes of the robot were obtained. Furthermore, Sparse Matrix Pose Optimization was carried out to improve the positioning accuracy. The positioning accuracy of the robot in x and y directions was kept within 5 cm, the angle error was controlled within 2°, and the positioning time was reduced by 40%. An improved timing elastic band (TEB) algorithm was proposed to guide the robot to move safely and smoothly. A critical factor was introduced to adjust the distance between the waypoints and the obstacle, generating a safer trajectory, and increasing the constraint of acceleration and end speed; thus, smooth navigation of the robot to the target point was achieved. The experimental results showed that, in the case of multiple obstacles being present, the robot could choose the path with fewer obstacles, and the robot moved smoothly when facing turns and approaching the target point by reducing its overshoot. The proposed mapping, positioning, and improved TEB algorithms were effective for high-precision positioning and efficient multi-target detection.

A multi-objective particle swarm optimization based on local ideal points

Numerous multi-objective evolutionary algorithms have recently been proposed for the leader selection or archive maintenance process via using reference sets. Despite the potential of this strategy has been demonstrated in the existing literatures, a significant drawback is that this methodology requires additional parameters or predefined reference points, which leads to increasing the complexity in real-world applications and reducing the versatility in addressing various optimization challenges. Novel to this study, a new convergence contribution (CC) evaluator without extra parameters and predefined reference points is presented for convergence evaluation, where the inspiration is drawn from the concept of an ideal point on a Pareto front and the divide-and-conquer technique. Specifically, the local ideal points are introduced in this paper by dynamically fabricating from the approximate Pareto front. Furthermore, the CC evaluator and parallel cell distance (PCD) are cooperatively integrated into a multi-objective particle swarm optimization (MOPSO/CP) to enhance both the global best solution selection and archive maintenance strategies. Comparative experiments on 21 benchmark test functions exhibited that the performances in terms of inverted generational distance and hypervolume of the proposed MOPSO/CP was the best among those of the chosen competitive algorithms. The significant role of the cooperative mechanism and the CC evaluator is further verified by ablation studies. The superiority of the designed algorithm against its competitors is unequivocally highlighted by the experimental results.

Interference fading suppression with fault-tolerant Kalman filter in phase-sensitive OTDR

A multi-sensor information fusion algorithm based on fault-tolerant Kalman filter is proposed in phase-sensitive optical time-domain reflectometer (Φ-OTDR) system, for achieving fading-free distributed vibration sensing. Firstly, a fault-tolerant dual-core complementary array model is designed. The Rayleigh scattering signal denoising, and vibration existence judgment of localization points are carried out to obtain the differentiated frequency demodulation results of the sensing points of the dual-core fiber array. Then a fault-tolerant control strategy is used to determine the sensor weight coefficients and vibration judgment coefficients during data fusion processing, and array data fusion is carried out based on time series data using Kalman filter to realize error value identification and filling. The advantage of this method is the combination of redundant data in a complementary way to improve the system stability. The frequency response ranges from 10 Hz to 2400 Hz and the localization accuracy is 98.33%. The influence of key parameters on the frequency demodulation performance of fault-tolerant Kalman filter is discussed, and a standard deviation of 14.6 Hz and an average error of 7.6 Hz are obtained. The demodulation frequency data matrix obtained by the classical demodulation method has a demodulation error probability of 89.18%, which proves the widespread existence of demodulation errors in vibration signals. The fusion error of demodulation frequency is reduced to 0.25 Hz, the frequency demodulation accuracy reaches 100%, and the demodulation error caused by interference attenuation can be completely eliminated. This system based on fault-tolerant Kalman filter has the characteristics of simple multiplexing structure, interference fading resistance and stable demodulation performance.

Power equalization and optimization of photovoltaic module based on forward-flyback converter

This paper focuses on enhancing the energy extraction efficiency of photovoltaic (PV) modules through the use of a straightforward power converter and control algorithm. This research delves into the electrical characteristics of PV modules, explaining the concepts of global maximum power point, and local maximum power points. By integrating maximum power point tracking algorithms and differential power processing technology, an innovative scheme for power equalization and optimization of PV modules is introduced. The scheme is based on a single-switch multi-winding forward-flyback converter. Using the STP-340-72-Vfh-type PV module as a case study, a simulation model is developed with PLECS simulation software. The simulations cover 30 different irradiance scenarios. The findings illustrate the effectiveness of the proposed PV module power optimization system in achieving maximum power output under different irradiance conditions, achieving an average efficiency of 94.61%. This efficiency rate is 13.95% greater than that of existing global maximum power tracking schemes.

Evolving Improved Sampling Protocols for Dose–Response Modelling Using Genetic Algorithms with a Profile-Likelihood Metric

Practical limitations of quality and quantity of data can limit the precision of parameter identification in mathematical models. Model-based experimental design approaches have been developed to minimise parameter uncertainty, but the majority of these approaches have relied on first-order approximations of model sensitivity at a local point in parameter space. Practical identifiability approaches such as profile-likelihood have shown potential for quantifying parameter uncertainty beyond linear approximations. This research presents a genetic algorithm approach to optimise sample timing across various parameterisations of a demonstrative PK-PD model with the goal of aiding experimental design. The optimisation relies on a chosen metric of parameter uncertainty that is based on the profile-likelihood method. Additionally, the approach considers cases where multiple parameter scenarios may require simultaneous optimisation. The genetic algorithm approach was able to locate near-optimal sampling protocols for a wide range of sample number (n = 3–20), and it reduced the parameter variance metric by 33–37% on average. The profile-likelihood metric also correlated well with an existing Monte Carlo-based metric (with a worst-case r > 0.89), while reducing computational cost by an order of magnitude. The combination of the new profile-likelihood metric and the genetic algorithm demonstrate the feasibility of considering the nonlinear nature of models in optimal experimental design at a reasonable computational cost. The outputs of such a process could allow for experimenters to either improve parameter certainty given a fixed number of samples, or reduce sample quantity while retaining the same level of parameter certainty.

3D-AMM: a 3D artificial moment method for path planning of manipulator in multiple obstacles scenario

PurposeIn response to the issue of traditional algorithms often falling into local minima or failing to find feasible solutions in manipulator path planning. The purpose of this paper is to propose a 3D artificial moment method (3D-AMM) for obstacle avoidance for the robotic arm's end-effector.Design/methodology/approachA new method for constructing temporary attractive points in 3D has been introduced using the vector triple product approach, which generates the attractive moments that attract the end-effector to move toward it. Second, distance weight factorization and spatial projection methods are introduced to improve the solution of repulsive moments in multiobstacle scenarios. Third, a novel motion vector-solving mechanism is proposed to provide nonzero velocity for the end-effector to solve the problem of limiting the solution of the motion vector to a fixed coordinate plane due to dimensionality constraints.FindingsA comparative analysis was conducted between the proposed algorithm and the existing methods, the improved artificial potential field method and the rapidly-random tree method under identical simulation conditions. The results indicate that the 3D-AMM method successfully plans paths with smoother trajectories and reduces the path length by 20.03% to 36.9%. Additionally, the experimental comparison outcomes affirm the feasibility and effectiveness of this method for obstacle avoidance in industrial scenarios.Originality/valueThis paper proposes a 3D-AMM algorithm for manipulator path planning in Cartesian space with multiple obstacles. This method effectively solves the problem of the artificial potential field method easily falling into local minimum points and the low path planning success rate of the rapidly-exploring random tree method.

CompleteDT: Point cloud completion with information-perception transformers

In this work, we propose a novel point cloud completion network, called CompleteDT. To fully capture the 3D geometric structure of point clouds, we introduce an Information-Perception Transformer (IPT) that can simultaneously capture local features and global geometric relations. CompleteDT comprises a Feature Encoder, Query Generator, and Query Decoder. Feature Encoder extracts local features from multi-resolution point clouds to capture intricate geometrical structures. Query Generator uses the proposed IPT, utilizing the Point Local Attention (PLA) and Point Global Attention (PGA) modules, to learn local features and global correlations, and generate query features that represent predicted point clouds. The PLA captures local information within local points by adaptively measuring weights of neighboring points, while PGA adapts multi-head self-attention by transforming it into a layer-by-layer form where each head learns global features in a high-dimensional space of different dimensions. By dense connections, the module allows for direct information exchange between each head and facilitates the capture of long global correlations. By combining the strengths of both PLA and PGA, the IPT can fully leverage local and global features to facilitate CompleteDT to complete point clouds. Lastly, the query features undergo refining to generate a complete point cloud through the Query Decoder. Our experimental results demonstrate that CompleteDT outperforms current state-of-the-art methods, effectively learning from incomplete inputs and predicting complete outputs.

Granular3D: Delving into multi-granularity 3D scene graph prediction

This paper addresses the significant challenges in 3D Semantic Scene Graph (3DSSG) prediction, essential for understanding complex 3D environments. Traditional approaches, primarily using PointNet and Graph Convolutional Networks, struggle with effectively extracting multi-grained features from intricate 3D scenes, largely due to a focus on global scene processing and single-scale feature extraction. To overcome these limitations, we introduce Granular3D, a novel approach that shifts the focus towards multi-granularity analysis by predicting relation triplets from specific sub-scenes. One key is the Adaptive Instance Enveloping Method (AIEM), which establishes an approximate envelope structure around irregular instances, providing shape-adaptive local point cloud sampling, thereby comprehensively covering the contextual environments of instances. Moreover, Granular3D incorporates a Hierarchical Dual-Stage Network (HDSN), which differentiates and processes features of instances and their pairs at varying scales, leading to a targeted prediction of instance categories and their relationships. To advance the perception of sub-scene in HDSN, we design a Gather Point Transformer structure (GaPT) that enables the combinatorial interaction of local information from multiple point cloud sets, achieving a more comprehensive local contextual feature extraction. Extensive evaluations on the challenging 3DSSG benchmark demonstrate that our methods provide substantial improvements, establishing a new state-of-the-art in 3DSSG prediction, boosting the top-50 triplet accuracy by ＋2.8%.

Implementation of modified P&O and an adaptive fuzzy logic controller based MPPT tracking system under partial shading and variable environmental conditions

Most of the conventional Maximum Power Point Tracking (MPPT) Algorithms provide the finest efficiency during uniform irradiation conditions, but under variable irradiation or partial shading condition (PSC), the performance deteriorates and the solar PV system is unable to provide the maximum electricity out of the PV Modules. This degradation in performance occurs due to the presence of numerous local maximum power points (LMPP) and a single global maximum power point (GMPP) in the power versus voltage (P-V) characteristics and the perfect tracking of these LMPP is not possible with the prevailing MPPT algorithms. In order to eradicate this shortcoming, we have proposed the implementation of an adaptive Fuzzy Logic Controller (FLC) based on Perturb and Observe (P&O) technique by employing a boost converter with variable resistive load under uniform irradiance condition (UIC), dynamic atmospheric conditions (DAC) and PSC in this article. We also demonstrated the usage of a simple and improved P&O algorithm by employing a buck boost converter for faster tracking time. These two suggested approaches of FLC and P&O incorporate a variable load scenario and a stable load scenario respectively to address the problem of output voltage oscillation in a PV-based system. The FLC is designed to adjust the dynamic change in step size based on the rate of change of the yield power of the PV panel to reduce oscillations in output of the boost converter and the PV system. According to the results of this analysis, the recommended FLC-based P&O (FLC-P&O) algorithm outperforms the proposed P&O algorithm under DAC and PSC with respect to tracking speed, steady-state error, and dynamic response. Apparently, it can be concluded that the proposed FLC-P&O technique can be applicable to real-time systems for reliable and efficient operation of the boost converter and add to the stability and simplicity of the contemporary PV-based systems.