Wolf Method Research Articles

Development of an algorithm for proton dose calculation in magnetic fields.

The advantages of proton therapy can be further enhanced with online magnetic resonance imaging (MRI) guidance. One of the challenges in the realization of MRI-guided proton therapy (MRPT) is accurately calculating the radiation dose in the presence of magnetic fields. This study aims to develop an efficient and accurate proton dose calculation algorithm adapted to the presence of magnetic fields. An analytical-numerical radiation dose calculation algorithm, Proton and Ion Dose Engine (PRIDE), was developed. The algorithm combines the pencil beam algorithm (PBA) with a novel iterative voxel-based ray-tracing algorithm. The new ray-tracing method uses fewer assumptions and ensures broader applicability for proton beam trajectory prediction in magnetic fields, and has been compared to Wolf's method and Schellhammer's method. The accuracy of PRIDE algorithm was validated on three phantoms and two practical plans (one single-field water plan and one prostate tumor plan) in different magnetic field strengths up to 3.0 T. The validation was performed by comparing the results against the Monte Carlo (MC) simulations, using the global gamma index criteria of 2%/2mm and 3%/3mm with a 10% threshold. PRIDE showed good agreement with MC in homogeneous and slab heterogeneous phantom, achieving gamma passing rates (%GPs) above 99% for 2%/2mm criteria when magnetic field strength is not greater than 1.5 T. Although the agreement decreased for scenarios involving high proton energy (240 MeV) and strong magnetic field (3.0 T), the 2%/2mm %GPs still remained above 98%. In lateral heterogeneous phantom, the accuracy of PRIDE decreased due to the PBA's limitation. For the two practical plans in different magnetic fields, %GPs exceeded 98% and 99% for 2%/2mm and 3%/3mm criteria, respectively. PRIDE can perform efficient and accurate proton dose calculation in magnetic fields up to 3.0 T, and is expected to work as a useful tool for proton dose calculation in MRPT.

A modified neural network method for computing the Lyapunov exponent spectrum in the nonlinear analysis of dynamical systems

This study presents a modified neural network method for computing the Lyapunov exponent spectrum in non-linear dynamical systems. Its mathematical description is an introduction. The proposed modified neural network method allows the addition of bias and constant neurons to the neural network topology and the use of different numbers of activation functions to suit different cases. Various algorithms for computing Lyapunov exponents, such as the Benettin method, the Wolf method, the Rosenstein method, the Kantz method, the Synchronisation method, the Sano-Sawada algorithm and the proposed modification of the neural network method, are used for classical problems in nonlinear dynamics. These problems include the generalised Hénon map, the chaotic attractor of the Baier-Klein map, and the vibrations of mechanical systems such as the flexible Bernoulli-Euler beam and the flexible functionally graded porous closed cylindrical shell under alternating load. The comparative analyses presented in this study are aimed at validating the accuracy and effectiveness of the methods, and at identifying the most relevant approaches for different types of systems and classes of problems. The proposed method is demonstrated to be superior to existing methods based on time-series evaluation in terms of sample size and accuracy. Furthermore, it does not require the initial system equations.

Frank–Wolfe and friends: a journey into projection-free first-order optimization methods

AbstractInvented some 65 years ago in a seminal paper by Marguerite Straus-Frank and Philip Wolfe, the Frank–Wolfe method recently enjoys a remarkable revival, fuelled by the need of fast and reliable first-order optimization methods in Data Science and other relevant application areas. This review tries to explain the success of this approach by illustrating versatility and applicability in a wide range of contexts, combined with an account on recent progress in variants, both improving on the speed and efficiency of this surprisingly simple principle of first-order optimization.

A novel development of hybrid maximum power point tracking controller for solar pv systems with wide voltage gain DC-DC converter

Now, the present power generation and distribution companies are working on renewable energy systems because their features are low-level atmospheric pollution, producing less greenhouse pollutants, more reliability, good static performance, and high robustness. In this work, the sunlight Photovoltaic (PV) system is selected because of its advantages are easily available in the atmosphere, high flexibility, zero carbon footprint, easy to maintain, and less transportation cost. However, solar networks produce nonlinear I-V characteristics. Due to the non-linear nature of the solar system, the extraction of peak voltage from the PV module is a very tough task. So, in this article, a variable modified step grey wolf method is integrated with the adaptive-neuro-fuzzy-inference-system to improve the energy production of solar systems. The features of this proposed maximum power point tracking controller are fast identification of the solar system operating point, generating the less fluctuated oriented converter load power, providing more MPP tracking accuracy, less dependence on the solar system installation, and useful for all environmental bad weather conditions. Another problem of solar systems is less voltage production which is improved by introducing a wide voltage gain-boost converter circuit. The features of this converter circuit are less development cost because it does not require more power electronics switches. Here, the proposed IGWM with an AFLC-fed sunlight system is investigated by using MATLAB/Simulink.

Using Taylor-Approximated Gradients to Improve the Frank–Wolfe Method for Empirical Risk Minimization

Using Taylor-Approximated Gradients to Improve the Frank–Wolfe Method for Empirical Risk Minimization

A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection

Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease’s therapy is significantly impacted by early identification of this illness. Preprocessing is the initial detecting stage in enhancing the quality of skin images by removing undesired background noise and objects. This study aims is to compile preprocessing techniques for skin cancer imaging that are currently accessible. Researchers looking into automated skin cancer diagnosis might use this article as an excellent place to start. The fully convolutional encoder–decoder network and Sparrow search algorithm (FCEDN-SpaSA) are proposed in this study for the segmentation of dermoscopic images. The individual wolf method and the ensemble ghosting technique are integrated to generate a neighbour-based search strategy in SpaSA for stressing the correct balance between navigation and exploitation. The classification procedure is accomplished by using an adaptive CNN technique to discriminate between normal skin and malignant skin lesions suggestive of disease. Our method provides classification accuracies comparable to commonly used incremental learning techniques while using less energy, storage space, memory access, and training time (only network updates with new training samples, no network sharing). In a simulation, the segmentation performance of the proposed technique on the ISBI 2017, ISIC 2018, and PH2 datasets reached accuracies of 95.28%, 95.89%, 92.70%, and 98.78%, respectively, on the same dataset and assessed the classification performance. It is accurate 91.67% of the time. The efficiency of the suggested strategy is demonstrated through comparisons with cutting-edge methodologies.

Fast quadratic model predictive control based on sensitivity analysis and Wolfe method

AbstractThis paper proposes a new algorithm based on sensitivity analysis and the Wolfe method to solve a sequence of parametric quadratic programming (QP) problems such as those that arise in quadratic model predictive control (QMPC). The Wolfe method, based on Karush–Kuhn–Tucker conditions, has been used to convert parametric QP problems to parametric linear programming (LP) problems, and then the sensitivity analysis is applied to solve the sequence of the parametric LP problems. This strategy obtains sensitivity analysis‐based QMPC (SA‐QMPC) algorithm. It is proved that the computational complexity of SA‐QMPC is for a region of the initial conditions and for sufficiently small sampling time and all initial conditions, where and are the horizon time and dimension of the state vector, respectively. Numerical results indicate the potential and properties of the proposed algorithm.

L1-norm optimisation of rank deficient GNSS networks by an improved Grey Wolf method

An improved grey wolf optimization (GWO) is proposed for direct L1-norm optimization in rank deficient GNSS networks to detect outliers since the standard GWO and the global optimization (GO) algorithms cannot deal with such problems. In contrast to the traditional method for L1-norm minimization which is solved by a cumbersome search of linear programming (LP) problem, in this algorithm, one only requires to input the target function. Furthermore, it has a greater ability to detect gross errors of observables with low reliability compared to traditional methods such as LP. Moreover, it does not consider the correct observables as blunders in contrast to some algorithms such as the iteratively reweighted least-squares (IRLS) method, which may consider them as outliers. The numerical results of real and simulated GNSS networks approve the efficiency of the new algorithm.

Quantification of chaos in a time series generated from a traffic flow model using the extended entropic chaos degree

Several Traffic flow models have been proposed to address the underlying causes of traffic congestion and develop effective methods for its suppression. Typically, these models are represented using differential or difference equations. Traffic flow models can exhibit chaotic behaviors. Traffic situations become unstable if chaos prevails in the traffic flow. Therefore, the conditions under which chaos occurs in the traffic flow and the intensity of chaos at that time must be investigated. The Lyapunov exponent is used for quantifying the chaos in the dynamical systems. However, computing the Lyapunov exponent over time series without a dynamical map is challenging. Recently, the extended entropic chaos degree has been introduced as an extension of the entropic chaos degree. The extended entropic chaos degree can be computed directly for any time series. Analytically, the extended entropic chaos degree equals the sum of all the Lyapunov exponents for multidimensional non-periodic maps. Moreover, the extended entropic chaos degree is mathematically shown to equal the sum of one positive and one negative Lyapunov exponents for two-dimensional typical chaotic maps, such as a generalized Baker’s map, and a standard map.In this study, we utilize the extended entropic chaos degree to quantify chaos in a time series with less computing complexity compared with conventional methods. First, we demonstrate that chaos can be quantified in a time series generated from a traffic flow model using an extended entropic chaos degree. Lyapunov exponent for a time series of time traveling between two adjacent traffic lights in the traffic flow model is not directly computable because its dynamics are not given as an explicit differential map. Second, we demonstrate that the computational complexity of the extended entropic chaos degree only corresponds to O(M). However, the computational complexities of the estimated Lyapunov exponents using the Wolf method are O(M2), where M is the number of points on a one-dimensional time series. Thus, we show that the extended chaos degree can quantify the chaos in the traffic flow model, such as one vehicle moving through a sequence of traffic lights in one dimension, with less complexity, where the chaos occurs depending on the timing of signal switching.

Unleashing wastewater heat Recovery's potential in smart building systems: Grey wolf-assisted optimization aided by artificial neural networks

This article presents an innovative and efficient way to address residential dwellings' substantial heating needs. The primary objective is to utilize the heat from wastewater to enhance energy efficiency through a control framework based on predetermined rules. This framework aims to increase the incoming air temperature at the air handling unit. A thorough evaluation is carried out to analyze all aspects of the proposed system compared to an identical system that does not incorporate the wastewater heat recovery process. The practicality of the concept is assessed for a residential building located in Beijing, China, employing the TRNSYS software. The most optimal operating condition is achieved via the grey wolf optimizer and TOPSIS decision-making approach equipped with the artificial neural network using MATLAB. Then, the proposed system's performance under optimal conditions is compared with similar works in the literature. According to the results, compared to the conventional system, a higher performance efficiency of 6 % and lower levelized cost of heating of 15.6 $/MWh is obtained by implementing the wastewater heat recovery process. The parametric study results also demonstrate a conflicting change in techno-economic and environmental indicators when altering the primary decision variables, highlighting the necessity for multi-criteria optimization. What stands out from the optimization outcomes is that the grey wolf method increases the efficiency and CO2 saving by around 5.2 and 531.2 kg/year while reducing the levelized cost of heating by about 13.6 $/MWh, respectively. The optimization results reveal that this condition is attained by raising the heat exchanger's effectiveness and the number of residences and decreasing the wastewater temperature. According to the scatter distribution of key parameters, the energy wheel effectiveness has low sensitivity, and the optimal points of tank volume are distributed within the range of 3 m3 and 4 m3.

Brownian dynamics simulations and Ornstein-Zernike equation for charged fluids using the Wolf potential

From a computational point of view, a promising description for charged-stabilized fluids is the so-called Wolf method, which is faster than the standard Ewald summation technique. Using Monte Carlo computer simulations, it was recently shown that the static correlation functions of strong electrolytes obtained with the Wolf potential satisfy the Stillinger–Lovett sum rules, which are a consequence of perfect screening, and are directly related to the local electroneutrality condition and the electrostatic screening within the Debye–Hückel regime. In this contribution, the Wolf potential is adapted to Brownian dynamics simulations to study both the structural and dynamical properties of charged fluids. In addition, we have solved the Ornstein-Zernike equation with the Wolf potential using the HNC closure relation. The resulting radial distributions agree well with the simulation data and previous results obtained with the Ewald technique. Simulation results using Brownian dynamics for weakly charged colloidal dispersions and asymmetric electrolytes are presented. The results include mean square displacement, self-intermediate scattering, and shear relaxation function, with no consideration of hydrodynamic interactions. Including the Wolf potential in Brownian dynamics simulations opens up the possibility of studying interesting transport phenomena in the low Reynolds and Peclet regime, even in those cases where hydrodynamic interactions become relevant.

Optimasi Rata-rata Produksi Jagung di Kabupaten Limapuluh Kota Menggunakan Pemrograman Kuadratik Metode Wolfe

This research was an experimented research that purpose to form a mathematical model for optimizing average corn production in Fifty Cities District with the constraint that the harvested area should not be more than the planted area and solve the model with the Wolfe quadratic programming method. The model of mathematical in the research was a model by formed nonlinear using the method of least squares. Then use the Wolfe method to form a new linear objective function and constraints in the form of Karush Kuhn-Tucker conditions. The results showed that the optimal average corn production in Fifty Cities District was 49.00456 tons/Ha with an optimal corn harvest area of 656.256 Ha.

Design performance optimization of laser beam welded joints made for vehicle chassis application using deep neural network-based Krill Herd method

The welding to be performed must be defect free, offer lower residual stress and strain, be compact in size and withstand different load conditions. However, the existing investigations in this scenario are still not modernized. Therefore, in this study, a specific welding method called laser beam welding (LBW) is performed and different weld parameters have been inspected and analysed. Advanced instruments based on the non-destructive (ND) are implemented to find the variable LBW responses such as weld bead defects, residuals and strain. The experimentation has been designed using Design Expert software, response surface methodology (RSM) and Box Behnken design (BBD) and verified by analysis of variance (ANOVA) analysis and FIT statistics. Moreover, a hybrid deep neural network-based Krill Herd optimization (DNN-KHO) is implemented to predict the output parameters like, undercut (µm), overlap (µm), total strain (mm/mm) and residual stress (MPa) during welding. The proposed DNN-KHO was also used to optimize LBW input parameters such as, peak power (W), weld speed (mm/s), gas flow rate (l/min) and beam diameter (µm) simultaneously. Predictions show that the proposed DNN-KHO algorithm outperformed by 21.53%, 45.428% and 41.31% higher in accuracy compared to respective hybrid random forest based grey wolf method (RF-GWO), RF and DNN predictions.

A METHOD FOR ESTIMATING THE ACCURACY OF THE TIE POINTS CLOUD: THE CASE STUDY OF PALAZZO DE FALCO

Abstract. In the context of cultural heritage documentation and conservation, an accurate 3D model is essential to properly describe an artifact of historical relevance and provide the expert user with verified metric data. In this paper we propose a workflow aimed at estimating errors in the image orientation steps, which is useful for improving the accuracy of the sparse cloud (composed of Tie Points). Some parameters provided as input to the processing software, often overlooked by the user in favour of the default ones, are analysed. Specifically, tests are carried out using the commercial software package Agisoft Metashape and involve the analysis of three parameters: (i) the accuracy of measuring the Ground Control Points (GCPs) provided by the user; (ii) the accuracy of the Tie Points (TPs) computed by the software; and (iii) the accuracy of collimating the GCPs on the image. The accuracy of the GCPs is estimated by implementing the formulas of error propagation in MATLAB, considering the three concurring sources of error: the coordinate measurement with the Total Station (TS), the coordinate transformation from the local system to the UTM/ETRF00 System, and the GNSS measurement for estimating the transformation parameters. The transformation parameters are calculated using the Bursa Wolf method. The collimation accuracy of GCPs on the image is estimated by the reprojection error of each single GCPs on the image plane. The accuracy of the GCPs will be estimated by the standard error per unit weight (SEUW), accurate GCPs being expected to have a SEUW value close to unity. SEUW is also the overall indicator for estimating the accuracy of the TPS cloud because it is influenced, in different ways, by all three parameters analysed. Test Area is a historic building located in the town of Fisciano, a few kilometres from the city of Salerno, known as Palazzo De Falco. The results highlight how these parameters, if not properly considered, can significantly affect the final 3D model.

Asphaltene precipitation described with a Yukawa SAFT-VR/MSA equation of state

We combine the Statistical Associating Fluid Theory for Potentials of Variable Range (SAFT-VR) and the Mean Spherical Approximation (MSA) to develop an equation of state to model asphaltene precipitation in petroleum fluids including the measured asphaltene onset pressures (AOP’s) of a reservoir live oil. In this model, dispersive solvent–solvent and asphaltene–solvent interactions in reservoir fluid mixtures are represented by Yukawa potentials. The approach is based in the modeling of polar and ionic interactions via Yukawa potentials, as originally proposed by the Generalized Mean Spherical Approximation Theory, and also in recent studies in molecular simulation of charged fluids using non-Ewald effective pair interactions, as in the Wolf method.

Optimisation of the hybrid grey wolf method in cluster-based wireless sensor network using edge computing

Optimisation of the hybrid grey wolf method in cluster-based wireless sensor network using edge computing

A rule-based energy management strategy for a low-temperature solar/wind-driven heating system optimized by the machine learning-assisted grey wolf approach

This work presents an innovative, practical, and cost-effective solution for advancing state-of-the-art intelligent building energy systems and aiding the intended worldwide green transition with maximum renewable integration. The vanadium chloride cycle, electrolyzer unit, and Alkaline fuel cell are powered by the sun's and wind's energy to produce/store/use hydrogen. A rule-based control scheme is designed to provide a sophisticated interplay between the demand/supply sides, components, and local energy networks to reduce peak capacity, lower emissions, and save energy costs. TRNSYS is used to analyze and compare the techno-economic-environmental indicators of the conventional system and the suggested smart model for a multi-family building in Sweden. A grey wolf method is built in MATLAB with the help of machine learning to determine the optimum operating state with the maximum accuracy and the least amount of computational time. The results reveal that the suggested smart model considerably saves energy and money compared to the conventional system in Sweden while lowering CO2 emissions. According to the optimization results, the grey wolf optimizer and machine learning techniques enable greater total efficiency of 13 %, higher CO2 mitigation of 8 %, a larger cost saving of 38 %, and a reduced levelized energy cost of 41 $/MWh. The scatter distribution of important design parameters shows that altering the fuel cell current and electrode area considerably impacts the system's performance from all angles. The bidirectional connection of the proposed smart system with the heating and electrical networks through the rule-based controller demonstrates that it can supply the building's energy requirements for more than 300 days of the year. Eventually, the major contribution of the vanadium chloride cycle in the summer and the electrolyzer in the winter to the creation of hydrogen highlights the significance of renewable hybridization in reducing the dependence of buildings on energy networks.

The smoothed complexity of Frank–Wolfe methods via conditioning of random matrices and polytopes

Frank–Wolfe methods are popular for optimization over a polytope. One of the reasons is because they do not need projection onto the polytope but only linear optimization over it. To understand its complexity, a fruitful approach in many works has been the use of condition measures of polytopes. Lacoste-Julien and Jaggi introduced a condition number for polytopes and showed linear convergence for several variations of the method. The actual running time can still be exponential in the worst case (when the condition number is exponential). We study the smoothed complexity of the condition number, namely the condition number of small random perturbations of the input polytope and show that it is polynomial for any simplex and exponential for general polytopes. Our results also apply to other condition measures of polytopes that have been proposed for the analysis of Frank–Wolfe methods: vertex-facet distance (Beck and Shtern) and facial distance (Peña and Rodríguez). Our argument for polytopes is a refinement of an argument that we develop to study the conditioning of random matrices. The basic argument shows that for c>1 a d -by- n random Gaussian matrix with n \geq cd has a d -by- d submatrix with minimum singular value that is exponentially small with high probability. This also has consequences on known results about the robust uniqueness of tensor decompositions, the complexity of the simplex method and the diameter of polytopes.

High-Order Orbital and Spin Hall Effects at the Tight Focus of Laser Beams

In this paper, using a Richards–Wolf method, which describes the behavior of electromagnetic waves at the sharp focus, we show that high-order spin and orbital Hall effects take place at the focal plane of tightly focused laser beams. We reveal that four local subwavelength regions are formed at the focus of a linearly polarized optical vortex with unit topological charge, where the spin angular momentum behaves in a special way. Longitudinal projections of the spin angular momentum are oppositely directed in the adjacent regions. We conclude that this is because photons falling into the neighboring regions at the focus have the opposite spin. This newly observed phenomenon may be called a spin Hall effect of the 4-th order. We also show that tightly focusing the superposition of cylindrical vector beams of the m-th and zero-order produces 2m subwavelength regions in the focal plane, such that longitudinal projections of the orbital angular momentum are oppositely directed in the neighboring regions. This occurs because photons falling into the neighboring regions at the focus have the opposite signs of the on-axis projections of the orbital angular momentum. This phenomenon may be termed an orbital Hall effect of the 2m-th order.

Frank--Wolfe Methods with an Unbounded Feasible Region and Applications to Structured Learning

Frank--Wolfe Methods with an Unbounded Feasible Region and Applications to Structured Learning