FR Method Research Articles

Avalanche susceptibility mapping with the use of frequency ratio, fuzzy and classical analytical hierarchy process for Uzungol area, Turkey

As expressed in many studies in literature, the dynamic nature of snow avalanches is affected by the meteorological, environmental, topographical and human-related factors of the region. To better appreciate and forecast the snow avalanche, one of the hydro-geological disasters, becomes possible thanks to the approaches that take these parameters into consideration. They are commonly seen in mountainous areas, cause loss of life and property when they reach settlements, energy transmission lines, highways, ski resorts and other structures. Field studies, GIS, remote sensing, and various numerical avalanche modeling programs are used in determining avalanche-prone zones. There are various methods for producing avalanche susceptibility maps. In this study, susceptibility maps were created using the Frequency Ratio (Fr), Analytical Hierarchy Process (AHP), and Fuzzy-AHP (FAHP) methods for avalanche potential, in the tourism region of Uzungol, Trabzon located in northeastern Turkey. There has been no literature so far in the application of Fuzzy-AHP methods on avalanche susceptibility analysis, which has been used in other fields. This work will be the first case study on this subject. The results were compared according to existing land and inventory data; the success rates of the methods were found to be 89.12% for Fr, 71.15% for AHP and 73.24% for FAHP, and the effectiveness of the Fuzzy-AHP method was further tested. Comparing the results of the different methods, it has been found out that the Fr method is the most successful, while the AHP and FAHP methods provide approximate results.

An efficient hybrid conjugate gradient method with sufficient descent property for unconstrained optimization

In order to take advantage of the strong theoretical properties of the FR method and computational efficiency of the method, we present a new hybrid conjugate gradient method based on the convex combination of these methods. In our method, the search directions satisfy the sufficient descent condition independent of any line search. Under some standard assumptions, we established global convergence property of our proposed method for general functions. Numerical comparisons on some test problems from the CUTEst library illustrate the efficiency and robustness of our proposed method in practice.

Global convergence of new conjugate gradient method with inexact line search

In this paper, We propose a new nonlinear conjugate gradient method (FRA) that satisfies a sufficient descent condition and global convergence under the inexact line search of strong wolf powell. Our numerical experiment shaw the efficiency of the new method in solving a set of problems from the CUTEst package, the proposed new formula gives excellent numerical results at CPU time, number of iterations, number of gradient ratings when compared to WYL, DY, PRP, and FR methods.

A modified of FR method to solve unconstrained optimization

There are many methods derived from the conjugate gradient method, the most famous of which is the FR method (Fletcher–Reeves). Most of the methods are found to solve large unconstrained optimization problems. In this paper, we made a modified to the FR method, so that it achieves better numerical results as well as the conditions of global convergence. The numerical experiment showed the efficiency and robustness of the new method.

Energetics and Kinetics of Peptide-Lipid Membrane Interaction: A Molecular Dynamics Study

Quantitative characterization of the binding strength of peptides to lipid bilayers is crucial in understanding the molecular mechanism of cellular processes triggered by peptide-lipid interactions. In general, single molecule methods, such as high precision AFM based dynamic force spectroscopy, can be used to measure the dissociation force distribution, P(F), and the corresponding force dependent rupture rate, k(F), of individual peptides bound to a lipid bilayer. However, the interpretation of the results require extensive theoretical and computational modeling that implies the knowledge of the free energy profile (potential of mean force or PMF) of the system as a function of the separation between peptide and membrane surface. To this end, we have used all atom MD simulations, to calculate the PMF of three representative Wimley-White pentapeptides (Ac-WLXLL, with guest residues X=R,I and L) interacting with two different lipid bilayers (zwitterionic POPC and charged POPG). The PMF, as a function of the distance between the residue W and the plane of the membrane, was calculated by using (i) the equilibrium umbrella-sampling method, and (ii) the nonequilibrium FR method. Both methods yielded comparable PMFs. In addition, the FR method also permitted the calculation of the position dependent diffusion coefficient of the peptide. In each case, the values of the activation energy (free energy barrier) and activation length extracted from the PMFs appear to be comparable to previous experimental and computational studies. The obtained energetic and kinetic parameters have been successfully used to fit P(F) and k(F) measured in AFM experiments.

VD-GAN: A Unified Framework for Joint Prototype and Representation Learning From Contaminated Single Sample per Person

Single sample per person (SSPP) face recognition with a ${c}$ ontaminated biometric ${e}$ nrolment database (SSPP-ce FR) is an emerging practical FR problem, where the SSPP in the enrolment database is no longer standard but contaminated by nuisance facial variations such as expression, lighting, pose, and disguise. In this case, the conventional SSPP FR methods, including the patch-based and generic learning methods, will suffer from serious performance degradation. Few recent methods were proposed to tackle SSPP-ce FR by either performing prototype learning on the contaminated enrolment database or learning discriminative representations that are robust against variation. Despite that, most of these approaches can only handle a specified single variation, e.g., pose, but cannot be extended to multiple variations. To address these two limitations, we propose a novel Variation Disentangling Generative Adversarial Network (VD-GAN) to jointly perform prototype learning and representation learning in a unified framework. The proposed VD-GAN consists of an encoder-decoder structural generator and a multi-task discriminator to handle universal variations including single, multiple, and even mixed variations in practice. The generator and discriminator play an adversarial game such that the generator learns a discriminative identity representation and generates an identity-preserved prototype for each face image, while the discriminator aims to predict face identity label, distinguish real vs. fake prototype, and disentangle target variations from the learned representations. Qualitative and quantitative evaluations on various real-world face datasets containing single/multiple and mixed variations demonstrate the effectiveness of VD-GAN.

Eriobotrya japonica Lindl. Kernels: Kinetics of Thermal Degradation under Inert Atmosphere Using Model-Free and Fitting Methods

A kinetic study of the pyrolysis process of raw Eriobotrya japonica Lindl. Kernels (RLK) was investigated using a thermogravimetric analyzer. The weight loss was measured in a nitrogen atmosphere. The samples were heated over a range of temperature from 298 K to 873 K with four different heating rates of 5, 10, 15, 20 K min-1. Mass loss (TGA) and derivative mass loss (DTG) measurements indicate that the increase in heating rate has no noticeable effect on the thermal degradation of the RLK. The results obtained from the thermal decomposition process indicate that there are three main stages such as dehydration, active, and passive pyrolysis. TGA curves indicate that active pyrolysis of RLK is between 160 and 450 °C. In this interval, a shoulder followed by a peak exists on the DTG plots. The shoulder corresponds to the decomposition of hemicelluloses, the first peak to that of cellulose. Lignin decomposes through all temperature range. The kinetic parameters such as activation energy and pre-exponential factor were obtained for two degradation steps by isoconversional model-free methods proposed by FWO, KAS, Kissinger, Tang, MKN, and FR, with degradation mode being: f(α)=(1-α)n with n = 1 for FR and g(α)=-Ln(1- α) for the other methods. The activation energy and pre-exponential factor obtained by the Kissinger method are 173 kJ/mol and 1.9×1016 min-1. While for free model methods, the average kinetic parameters calculated are 172-248 kJ.mol-1 and 5,30×1020 for integral methods (FWO, KAS, Tang and MKN) and 190-271 kJ.mol-1 and 1.77×1022 min-1 for differential Fr method. The activation energy decreases in the final stages of the process. The energy required for hemicellulose degradation is lower than that of cellulose. The most probable reaction functions have thus been determined for these two stages by Coats-Redfern and Criado method, leading to greatly improved calculation performance over the entire conversion range. The reaction, second-order F2, describes the pyrolysis reaction models of RLK. With the Arrhenius parameters obtained from the fitting model of CR, we attempt to reconstruct the temperature-dependent mass conversion curves and have resulted in generally acceptable results. Based on the Arrhenius parameter values obtained by Kissinger equation, the changes in entropy, enthalpy and Gibbs free energy, and lifetime predictions have been estimated concerning the thermal degradation processes of RLK.

Effect of Fat on Protein Estimation in Milk and Its Correlation with Lactose in Different Milk Types: A Small-Scale Study

Background: Estimation of macronutrients like protein and lactose is important to assess the quality of milk. To estimate these two macronutrients, ten raw milk samples obtained from each group of different animals (cow, goat, buffalo), ten pasteurized cow milk and ten human milk samples were analysed. Methods: Bicinchoninic acid (BCA) method was used to estimate protein from different milk samples. Four different sample preparation protocols were compared to check the effect of fat on BCA based protein estimation: dilution (D), fat removal-protein precipitation (FR and PP), fat removal-dilution (FR and D) and dilution-fat removal (D and FR). For lactose quantification, ultrahigh-performance liquid chromatography-mass spectrometry-selected reaction monitoring (UHPLC-MS/SRM) method was developed and validated using 13C6 lactose as internal standard (ISTD).Result: Among these four different protocols, D and FR method showed consistent data for total protein content in animal milk (cow-3.16%, goat-3.21%, buffalo-3.81%, pasteurized-2.98%) and FR and PP showed consistent data in human milk samples (1.2%). Though BCA method is simple to use, proper sample preparation protocol has to be applied prior to protein estimation to avoid the interference due to fat or lactose. In case of lactose, inter-day validation showed the accuracy ranging from 97.13 to 100.54%, coefficient of variation varying between 0.1 to 1.53%, correlation R2=0.999. Lactose is in the range of 4.1 to 4.8% in animal milk and 6.6% in human milk samples. The internal ratio of lactose/protein (1.28 to 1.55 in animal milk and 5.33 in human milk) will be useful to differentiate human milk from animal milk type and to assess the milk quality.

New hybrid conjugate gradient method as a convex combination of FR and BA methods

Conjugate gradient method is the most used for solving the unconstrained optimization problem. In this paper we propose a new hybrid conjugate gradient method which is obtained from a convex combination of FR and BA. Under some conditions the given method have the sufficient descent and global convergence property. Numerical results and their performance support the effectiveness and robustness of our procedure.

A New Hybrid PRPFR Conjugate Gradient Method for Solving Nonlinear Monotone Equations and Image Restoration Problems

A new hybrid PRPFR conjugate gradient method is presented in this paper, which is designed such that it owns sufficient descent property and trust region property. This method can be considered as a convex combination of the PRP method and the FR method while using the hyperplane projection technique. Under accelerated step length, the global convergence property is gained with some appropriate assumptions. Comparing with other methods, the numerical experiments show that the PRPFR method is more competitive for solving nonlinear equations and image restoration problems.

Face re-identification challenge: Are face recognition models good enough?

Face re-identification (Re-ID) aims to track the same individuals over space and time with subtle identity class information in automatically detected face images captured by unconstrained surveillance camera views. Despite significant advances of face recognition systems for constrained social media facial images, face Re-ID is more challenging due to poor-quality surveillance face imagery data and remains under-studied. However, solving this problem enables a wide range of practical applications, ranging from law enforcement and information security to business, entertainment and e-commerce. To facilitate more studies on face Re-ID towards practical and robust solutions, a true large scale Surveillance Face Re-ID benchmark (SurvFace) is introduced, characterised by natively low-resolution, motion blur, uncontrolled poses, varying occlusion, poor illumination, and background clutters. This new benchmark is the largest and more importantly the only true surveillance face Re-ID dataset to our best knowledge, where facial images are captured and detected under realistic surveillance scenarios. We show that the current state-of-the-art FR methods are surprisingly poorfor face Re-ID. Besides, face Re-ID is generally more difficult in an open-set setting as naturally required in surveillance scenarios, owing to a large number of non-target people (distractors) appearing in open ended scenes. Moreover, the low-resolution problem inherent to surveillance facial imagery is investigated. Finally, we discuss open research problems that need to be solved in order to overcome the under-studied face Re-ID problem.

Thermal kinetics involved during the solid-state synthesis of Cr2AlC MAX phase

The formation of nanolaminated Cr2AlC MAX phase by using solid-state synthesis route has been investigated through thermal analysis technique. The mixture of chromium (Cr), aluminum (Al) and graphite (C) in 2:1.4:1 was subjected to differential thermal analysis in an argon atmosphere and heated up to 1250 °C, at multiple heating rates (10, 20, 30, 40 °C min−1). Two endothermic peaks (~ 666 °C and ~ 1053 °C) are observed during the synthesis of Cr2AlC MAX phase. The formation of Cr2AlC is also confirmed through XRD, FESEM, HR-TEM and SAED analysis. The kinetic triplets (activation energy, pre-exponential factor and reaction mechanism) involved during the synthesis of Cr2AlC were estimated. The activation energy and reaction mechanism were determined by using iso-conversional model-free methods (KAS, FWO and FR methods) and integral master plot method, respectively. The results indicated that F2 (second-order) reaction mechanism dominates the formation of Cr2AlC MAX phase.

A Fast Identification Method of Yield Strength of Materials Based on Bending Experimental Data

Identifying the yield strength of materials quickly and accurately is the key to realizing defect prediction and digital process control on the production line. This paper focuses on identifying the material yield strength based on bending deformation, analyzing the influence of different die fillets, punch fillets, and die spans on the curve shapes, determining the reasonable dimensions of the device, and developing them. Two methods for rapidly extracting the yield load are proposed—the window vector method (WV) and the fitting residual method (FR)—and compared with the double secant line method (CWA) and the one tenth thickness method (t/10). Because there is no direct correspondence between the yield load and the material performance parameters, the relevant equations were fitted using the experimental data. The linear correlation between load and yield strength determined by these four methods was close to 0.99. Finally, four kinds of sheets with high, medium and low yield strength were tested and compared with the observed results. The result shows that when the yield strength is small, the average error and the relevant model dispersion will increase. As the yield strength increases, the biases increase gradually. The prediction errors based on the t/10, WV, and FR methods were all below 4%.

Thermal kinetics and reactive mechanism of cellulose nitrate decomposition by traditional multi kinetics and modeling calculation under isothermal and non-isothermal conditions

Cellulose nitrate, also known as nitrocellulose (NC), is a highly flammable energetic cellulose derivatives formed by exposing cellulose to nitric acid, whose producing procedure has been greatly employed in daily life and military area. In this study, to explore its pyrolysis and combustion kinetics, NC samples were subjected to isothermal and non-isothermal DSC experiments. The isothermal experiments were carried at 150, 155, 160, and 165 °C, while the non-isothermal experiments were taken at 2, 4, 8, and 10 °C min−1 for heating rates. By model fitting methods, several classical reaction models with high fitting degree were obtained. The model corresponding to isothermal conditions was f(α) = 4(1 − α)[−ln(1 − α)]3/4, while for non-isothermal conditions the model was identified as f(α) = (1 − α)3. The variation of activation energy with conversion was calculated by model free methods including FR, KAS, FWO, and advanced Vyazovkin methods. Finally, based on the results of kinetic analysis, the adjustment function was introduced and the accurate reaction mechanism functions of NC were identified for isothermal and non-isothermal conditions, respectively. This study has a high guiding significance for the thermal decomposition and combustion kinetics of NC under isothermal and non-isothermal heating conditions.

Ensemble of Deep Convolutional Neural Networks with Gabor Face Representations for Face Recognition.

Most DCNN-based FR approaches typically employ grayscale or RGB color images as input representations of DCNN architectures. However, other effective face representation methods have been developed and incorporated into current practical FR systems. In light of this fact, the focus of our study is to employ Gabor face representations in the design of DCNN-based FR frameworks to improve FR performance. To this end, we develop a novel "Gabor DCNN (GDCNN)" ensemble method that effectively applies different and multiple Gabor face representations as inputs during the training and testing phases of a DCNN for FR applications. The proposed GDCNN ensemble method primarily consists of two parts: 1) GDCNN ensemble construction and 2) GDCNN ensemble combination. The goal of the former part is to build an ensemble of GDCNN members (i.e., base models), each learned with a particular type of Gabor face representation. The objective of the latter part is to adaptively combine multiple FR outputs of individual GDCNN members. We perform extensive experiments to evaluate our proposed method on four public face databases (DBs) using the associated standard evaluation protocols. Experimental results demonstrate that our approach exhibits significantly better FR performance than typical DCNN-based approaches that rely only on grayscale or color face images as input representations. In addition, the feasibility of our proposed GDCNN ensemble has been successfully demonstrated by making comparisons with other state-of-the-art DCNN-based FR methods.

Experimental and theoretical study on thermal kinetics and reactive mechanism of nitrocellulose pyrolysis by traditional multi kinetics and modeling reconstruction

Nitrocellulose (NC) has been applied in many fields of daily life and military industry, but its instability brings large danger during storage and usage, which greatly limits its application. In this study, decomposition behavior and reaction model of NC were explored. To obtain its kinetic triplets during decomposition, NC samples were heated in nitrogen atmosphere with different heating rates of 5, 10, 15, 20 °C min−1, respectively. A 3-th order reaction model was obtained by model fitting methods. Meanwhile, the relationship between activation energy and conversion was obtained by multi isoconversional methods, including KAS, FR, FWO, and Advanced Vyazovkin methods. Then the reaction model was reconstructed by introducing adjustment functions with result of an expression of f(α) = 19.38193α1.41177(1-α)4.60503.

An efficient modified PRP-FR hybrid conjugate gradient method for solving unconstrained optimization problems

In this paper, a new hybrid conjugate gradient method for solving unconstrained optimization problems is proposed. The proposed method inherits the features of the PRP, FR and NPRP conjugate gradient methods. The method satisfies the sufficient descent property independent of any line search and its global convergence is established under the strong Wolfe-Powell line search conditions. Numerical results show that the new algorithm is very competitive.

Global convergence of a modified Fletcher–Reeves conjugate gradient method with Wolfe line search

In this paper, we are concerned with the conjugate gradient methods for solving unconstrained optimization problems. we propose a modified Fletcher–Reeves (abbreviated FR) [Function minimization by conjugate gradients, Comput. J. 7 (1964) 149–154] conjugate gradient algorithm satisfying a parametrized sufficient descent condition with a parameter [Formula: see text] is proposed. The parameter [Formula: see text] is computed by means of the conjugacy condition, thus an algorithm which is a positive multiplicative modification of the Hestenes and Stiefel (abbreviated HS) [Methods of conjugate gradients for solving linear systems, J. Res. Nat. Bur. Standards Sec. B 48 (1952) 409–436] algorithm is obtained, which produces a descent search direction at every iteration that the line search satisfies the Wolfe conditions. Under appropriate conditions, we show that the modified FR method with the strong Wolfe line search is globally convergent of uniformly convex functions. We also present extensive preliminary numerical experiments to show the efficiency of the proposed method.

Study on Curing Kinetics of Fatty Amine/Epoxy Resin Using Non-Isothermal DSC Method

The curing processing of fatty amine/epoxy resin was studied by non-isothermal differential scanning calorimetry (DSC) at different heating rates. The curing kinetics mechanism function and the kinetic parameters were determined using n order reaction and autocatalytic reaction and the model-free method. The curing processing was determined by extrapolation method. The results indicated that n order reaction model does not accord with the basic reaction characteristics of curing system. While the DSC curves from the autocatalytic model can well agree with that of experiment. The whole curing reaction process accords with the autocatalytic reaction model. The results by using FWO and FR method can also confirm that the curing reaction of resin system belongs to the autocatalytic model.

Comments on ”New hybrid conjugate gradient method as a convex combination of FR and PRP methods”

In this note, we present a new theory as a modification and an alternative to S.Djordjevic?s Theorem (2.2), Here we rephrase the text of theory (2.2) by deleting condition (2.16), Notations and equation numbers as in S.Djordjevic.