Nonparallel Plane Research Articles

Multi-Instance Nonparallel Tube Learning.

In multi-instance nonparallel plane learning (NPL), the training set is comprised of bags of instances and the nonparallel planes are trained to classify the bags. Most of the existing multi-instance NPL methods are proposed based on a twin support vector machine (TWSVM). Similar to TWSVM, they use only a single plane to generalize the data occurrence of one class and do not sufficiently consider the boundary information, which may lead to the limitation of their classification accuracy. In this article, we propose a multi-instance nonparallel tube learning (MINTL) method. Distinguished from the existing multi-instance NPL methods, MINTL embeds the boundary information into the classifier by learning a large-margin-based ϵ -tube for each class, such that the boundary information can be incorporated into refining the classifier and further improving the performance. Specifically, given a K -class multi-instance dataset, MINTL seeks K ϵ -tubes, one for each class. In multi-instance learning, each positive bag contains at least one positive instance. To build up the ϵk -tube of class k , we require that each bag of class k should have at least one instance included in the ϵk -tube. Moreover, except for one instance included in the ϵk -tube, the remaining instances in the positive bag may include positive instances or irrelevant instances, and their labels are unavailable. A large margin constraint is presented to assign the remaining instances either inside the ϵk -tube or outside the ϵk -tube with a large margin. Substantial experiments on real-world datasets have shown that MINTL obtains significantly better classification accuracy than the existing multi-instance NPL methods.

Vision Measurement Method Based on Plate Glass Window Refraction Model in Tunnel Construction.

Due to the harsh environment of high humidity and dust in tunnel construction, the vision measurement system needs to be equipped with an explosion-proof glass protective cover. The refractive effect of the plate glass window invalidates the pinhole model. This paper proposes a comprehensive solution for addressing the issue of plane refraction. First, the imaging model for non-parallel plane refraction is established based on dynamic virtual focal length and the Rodriguez formula. Further, due to the failure of the epipolar constraint principle in binocular vision systems caused by plane refraction, this paper proposes the epipolar constraint model for independent refractive plane imaging. Finally, an independent refraction plane triangulation model is proposed to address the issue of triangulation failure caused by plane refraction. The RMSE of the depth of field errors in the independent refraction plane triangulation model is 2.9902 mm before correction and 0.3187 mm after correction. The RMSE of the positioning errors before and after correction are 3.5661 mm and 0.3465 mm, respectively.

Novel and pragmatic exploration of variation in glottic parameters in non-parallel versus parallel vocal cord CT planes with potential reporting pitfalls.

Oblique orientation of vocal cord demands strict compliance, by technicians and clinicians, to the recommended parallel plane CT scan of larynx. Repercussions of non-compliance has never been investigated before. We aimed to observe influence of non-parallel vocal cord plane CT scan on qualitative and quantitative glottic parameters, keeping parallel plane CT as a standard for comparison. Simultaneous identification of potential suboptimal imaging sequelae as a result of unformatted CT plane was also identified. In this study we included 95 normal adult glottides and retrospectively analyzed their anatomy in two axial planes, non-parallel plane ① and parallel to vocal cord plane ②. Qualitative (shape, structures at glottic level) and quantitative (anterior commissure ACom, vocal cord width VCw, anteroposterior AP, transverse Tr, cross-sectional area CSA) glottic variables were recorded. Multivariate statistical analysis was used to predict pattern and their impact on glottic anatomy. Plane ① displayed supraglottic features in glottis; adipose (90.5%) and split thyroid laminae (70.6%). Other categorical variables: atypical shape, submental structures and multilevel vertebral crossing were also in majority. All glottic dimensions varied significantly between two planes with most in ACom (-5.8mm) and CSA (-15.0 mm2). In contrast, plane ② manifested higher VCw (>73%), Tr (66.3%), CSA (64.2%) and AP (44.2%) measurements. On correlation analysis, variation in ACom, CSA, Tr was positively associated with VC or plane obliquity (p<0.05). This variability was more in obese and short necked subjects. Change in one parameter also modified other significantly i.e., ACom versus AP and CSA versus Tr. Results indicated statistically significant change in subjective and objective anatomical parameters of glottis on non-application of appropriate CT larynx protocol for image analysis hence highlighting importance of image reformation.

Study of MHD nanofluid flow with fuzzy volume fraction in thermal field-flow fractionation

This paper discusses the magnetohydrodynamic Jeffery–Hamel nanofluid flow (MHD-JHNF) between two rigid non-parallel plane walls. It enhances the study of simple Jeffery–Hamel flow problems to the MHD nanofluid flow problems with uncertain volume fractions. It examines the impact of various parameters, such as channel angles and Reynolds number with magnetic field and nanoparticles, on the fuzzy velocity profiles. The nanoparticle volume fraction is considered an uncertain parameter by using a triangular fuzzy number ranging from 0.0 to 0.2. A novel double parametric form-based homotopy analysis approach with its convergence analysis is introduced to examine the fuzzy velocity profiles at distinct convergent and divergent channel positions and fuzzy velocity boundaries at other channels and illustrates the efficiency of the process. Finally, Maple software is used to make the numerical simulations, and the results are checked with the available results for specific cases in crisp environments.

Effect of heat transfer on hybrid nanofluid flow in converging/diverging channel using fuzzy volume fraction

This work explores the magneto-hydrodynamics (MHD) Jeffery–Hamel nanofluid flow between two rigid non-parallel plane walls with heat transfer by employing hybrid nanoparticles, especially Cu and Cu-Al_{2}O_{3}. Here the MHD nanofluid flow problem is extended with fuzzy volume fraction and heat transfer with diverse nanoparticles to cover the influence of thermal profiles with hybrid nanoparticles on the fuzzy velocity profiles. The nanoparticle volume fraction is described with a triangular fuzzy number ranging from 0 to 5%. A novel double parametric form-based homotopy analysis approach is considered to study the fuzzy velocity and temperature profiles with hybrid nanoparticles in both convergent and divergent channel positions. Finally, the efficiency of the proposed method has been demonstrated by comparing it with the available results in a crisp environment for validation.

A New In Situ Coaxial Capacitive Sensor Network for Debris Monitoring of Lubricating Oil.

Wear debris monitoring of lubricant oil is an important method to determine the health and failure mode of key components such as bearings and gears in rotatory machines. The permittivity of lubricant oil can be changed when the wear debris enters the oil. Capacitive sensing methods showed potential in monitoring debris in lubricant due to the simple structure and good response. In order to improve the detection sensitivity and reliability, this study proposes a new coaxial capacitive sensor network featured with parallel curved electrodes and non-parallel plane electrodes. As a kind of through-flow sensor, the proposed capacitive sensor network can be in situ integrated into the oil pipeline. The theoretical models of sensing mechanisms were established to figure out the relationship between the two types of capacitive sensors in the sensor network. The intensity distributions of the electric field in the coaxial capacitive sensor network are simulated to verify the theoretical analysis, and the effects of different debris sizes and debris numbers on the capacitance values were also simulated. Finally, the theoretical model and simulation results were experimentally validated to verify the feasibility of the proposed sensor network.

New Analytical Study for Nanofluid between Two Non-Parallel ‎Plane Walls (Jeffery-Hamel Flow)‎

The aim of this paper is to analyze the problem of magneto hydrodynamic Jeffrey-Hamel flow (JHF) with nanoparticles. The governing equations for this problem are reduced to an ordinary differential equation and it is solved using new analytical method (NAM) and fourth-order Runge-Kutta Method (RK ∼ 4). The NAM is an iterative method that relies mainly on derivatives with Taylor expansion interference. In addition, the velocity profile has been computed and shown for various values of physical parameters. The objective of the present work is to investigate the effect of the angles between the plates, Reynold number, magnetic number and nanoparticles volume fraction on the velocity profile.

Duan–Rach modified Adomian decomposition method (DRMA) for viscoelastic fluid flow between nonparallel plane walls

The main goal of this work is to examine the viscoelastic fluid flow velocity and skin friction coefficient through the Duan–Rach modified Adomian decomposition technique. The effects of various physical parameters of interest such as Reynolds number ($$ \text{Re} $$), channel half-angle ($$ \alpha $$) and Weissenberg number ($$ W_{i} $$) are investigated. Results revealed that the reversal flow is entirely excluded with the increase in Weissenberg number $$ W_{i} $$ for both convergent and divergent flows. Afterward, the earned analytical data are compared with numerical data and those available in the literature. From this comparison, results indicate a great agreement and therefore support the Duan–Rach approach’s applicability, validity and higher accuracy.

Heat transfer through converging-diverging channels using Adomian decomposition method

This study consists of proposing a new mathematical method to develop a new model for evaluating thermal distributions throughout convergent-divergent channels between non-parallel plane walls in Jeffery Hamel flow. Subsequently, dimensionless equations that govern temperature fields and velocity are numerically tackled via the Runge–Kutta-Fehlberg approach based on the shooting method. Additionally, an analytical study is performed by applying an effective computation technique named Adomian Decomposition Method. Determining the effect of Reynolds and Prandtl numbers on the heat transfer and fluid velocity inside converging/diverging channels can be mentioned as the fundamental purpose of this research. Based on the results obtained for dimensionless velocity and thermal distributions, a supreme match can be observed between numerical and analytical results indicating the adopted ADM method is valid, applicable, and has great precision.

Short-range supercontinuum-based lidar for temperature profiling.

We developed a short-range light detection and ranging system using a supercontinuum (SC) source spectrally tailored to cover the ro-vibrational transition energies of desired components of a flue gas. The system enables remote measurements of the gas parameters, including temperature and concentration which play a key role in the performance of combustion power plants. The technique requires only one inspection window and, thus, can be used in combustion units with limited access. It exploits differential absorption between specific wavelength bands of the gas absorption spectrum. The transmittance of an individual wavelength band is derived from the detected backscattered temporal intensity of the SC pulses. We demonstrate water vapor temperature measurement in the range of 400°C-900°C in a laboratory furnace with the use of only two wavelength bands. Using more than two wavelength bands, the technique can be further extended to simultaneously measure temperature and concentration. By varying the direction of the incident beam in a non-parallel plane, a full 3D profile is also obtainable.

A closed-form contact model for gas face seals during the opened operation

PurposeFace contact has a strong impact on the service life of non-contacting gas face seals; the current research which mainly focuses on the face contact had appeared during the startup or shutdown operation. This paper aims to present a closed-form contact model of a gas face seal during the opened operation.Design/methodology/approachReferring to the axial rub-impact model of rotor dynamics, a closed-form contact model is developed under a nonparallel plane contact condition that corresponds to the local face contact of sealing rings arising from some disturbances during the opened operation. The closed-form contact model and a direct numerical contact model are performed on Gaussian surfaces to compare the contact behavior.FindingsThe closed-form contact model is in a good agreement with the direct numerical contact model. However, the closed-form contact model cannot involve the influence of grooves on the sealing ends. The error is eliminated in some other types of gas face seals such as coned gas face seals. Besides non-contacting face seals, the closed-form model can be applied to the axial rub impact of rotor dynamics.Originality valueA closed-form contact model of a gas face seal is established during the opened operation. The closed-form contact model is validated by a direct numerical contact model. The closed-form contact model also suits for axial rub-impact of rotor dynamics.

KNN-based least squares twin support vector machine for pattern classification

The least squares twin support vector machine (LSTSVM) generates two non-parallel hyperplanes by directly solving a pair of linear equations as opposed to solving two quadratic programming problems (QPPs) in the conventional twin support vector machine (TSVM), which makes learning speed of LSTSVM faster than that of the TSVM. However, LSTSVM fails to discover underlying similarity information within samples which may be important for classification performance. To address the above problem, we apply the similarity information of samples into LSTSVM to build a novel non-parallel plane classifier, called K-nearest neighbor based least squares twin support vector machine (KNN-LSTSVM). The proposed method not only retains the superior advantage of LSTSVM which is simple and fast algorithm but also incorporates the inter-class and intra-class graphs into the model to improve classification accuracy and generalization ability. The experimental results on several synthetic as well as benchmark datasets demonstrate the efficiency of our proposed method. Finally, we further went on to investigate the effectiveness of our classifier for human action recognition application.

Influence of viscous dissipation on a copper oxide nanofluid in an oblique channel: Implementation of the KKL model

This paper aims to study the flow of a nanofluid in the presence of viscous dissipation in an oblique channel (nonparallel plane walls). For thermal conductivity of the nanofluid, the KKL model is utilized. Water is taken as the base fluid and it is assumed to be containing the solid nanoparticles of copper oxide. The appropriate set of partial differential equations is transformed into a self-similar system with the help of feasible similarity transformations. The solution of the model is obtained analytically and to ensure the validity of analytical solutions, numerically one is also calculated. The homotopy analysis method (HAM) and the Runge-Kutta numerical method (coupled with shooting techniques) have been employed for the said purpose. The influence of the different flow parameters in the model on velocity, thermal field, skin friction coefficient and local rate of heat transfer has been discussed with the help of graphs. Furthermore, graphical comparison between the local rate of heat transfer in regular fluids and nanofluids has been made which shows that in case of nanofluids, heat transfer is rapid as compared to regular fluids.

Analytical and numerical investigation of thermal radiation effects on flow of viscous incompressible fluid with stretchable convergent/divergent channels

This article, investigates the flow of a viscous incompressible fluid between two nonparallel plane walls, known as Jeffery-Hamel flow, under the influence of thermal radiation. The walls are capable of stretching and shrinking rectilinearly. With the help of suitable similarity variables, the dimensional partial differential equations, governing the flow, are transformed into a coupled system of ordinary differential equations. The Soret and Dufour effects are also taken into consideration while modeling the flow. The analytical solution of the flow model is then obtained by employing Adomian's decomposition method. The same problem has also been solved numerically with the help of fourth order Runge-Kutta scheme. A comparison between the analytical and numerical solutions has been presented and discussed. The influence of different non-dimensional parameters appearing in the flow model on the velocity, temperature and concentration profiles is discussed with the help of graphs. The rates of heat and mass transfer at the walls are also investigated.

Series Solution of Nanofluid Flow and Heat Transfer Between Stretchable/Shrinkable Inclined Walls

In this research, the steady nanofluid flow and heat transfer characteristics in Jeffery–Hamel flow when the stationary rigid nonparallel plates are permitted to stretch or shrink are investigated. Using appropriate transformations, the momentum and energy equations that govern velocity and temperature fields are converted into nonlinear ordinary differential equations. These resulting equations are solved analytically by applying Adomian decomposition method (ADM) and numerically using a fourth order Runge Kutta method featuring shooting technique. In addition, the skin friction coefficient and the Nusselt number as well as the velocity and temperature profiles are investigated subject to various parameters of interest, namely Reynolds number, Prandtl number, Eckert number, nanoparticle volume fraction and stretching/shrinking parameter. The results indicate that the stretchable or shrinkable walls with the presence of alumina nanoparticles in a water base fluid produces more heat and enhances significantly the heat transfer between nonparallel plane walls. It is also demonstrated that the analytical results match perfectly with those of numerical Runge Kutta method, thus justifying the higher accuracy of the ADM. Finally, a discussion whether the nanofluid problem can be interpreted in terms of regular fluid is given.

A sparse method for least squares twin support vector regression

Recently, some nonparallel plane regressors, such as twin support vector regression (TSVR), and least squares TSVR (LSTSVR), have been proposed and have attracted much attention. However, these algorithms are not sparse, which would make their learning speed low. In this paper, we propose a novel nonparallel plane regressor, which can automatically select the relevant features. Firstly, we introduce a regularization term to the objective function of LSTSVR, which can guarantee two quadratic programming problems (QPPs) are strong convex, implying that the proposed algorithm can obtain the global but unique solution. Secondly, the primal formulation is converted to a linear programming (LP) problem. Then, we solve the dual of the LP formulation by minimizing its exterior penalty problem, which would make our method yield very sparse solutions. In other words, this method can suppress input features so that it can obtain comparable regression performance when using fewer computational time. Numerical experiments on artificial dataset and benchmark datasets demonstrate the feasibility and validity of the proposed algorithm.

Regularization feature selection projection twin support vector machine via exterior penalty

In the past years, non-parallel plane classifiers that seek projection direction instead of hyperplane for each class have attracted much attention, such as the multi-weight vector projection support vector machine (MVSVM) and the projection twin support vector machine (PTSVM). Instead of solving two generalized eigenvalue problems in MVSVM, PTSVM solves two related SVM-type problems to obtain the two projection directions by solving two smaller quadratic programming problems, similar to twin support vector machine. In order to suppress input space features, we propose a novel non-parallel classifier to automatically select significant features, called regularization feature selection projection twin support vector machine (RFSPTSVM). In contrast to the PTSVM, we first incorporate a regularization term to ensure the optimization problems are convex, and then replace all the terms with L1-norm ones. By minimizing an exterior penalty function of the linear programming problem and using a fast generalized Newton algorithm, our RFSPTSVM obtains very sparse solutions. For nonlinear case, the method utilizes minimal number of kernel functions. The experimental results on toy datasets, Myeloma dataset, several UCI benchmark datasets, and NDCC generated datasets show the feasibility and effectiveness of the proposed method.

Heat transfer of copper/water nanofluid flow through converging-diverging channel

The main objective of this work is to investigate analytically the steady nanofluid flow and heat transfer characteristics between nonparallel plane walls. Using appropriate transformations for the velocity and temperature, the basic nonlinear partial differential equations are reduced to the ordinary differential equations. Then, these equations have been solved analytically and numerically for some values of the governing parameters, Reynolds number, Re, channel half angle, α, Prandtl number, Pr, and Eckert number, Ec, using Adomian decomposition method and the Runge-Kutta method with mathematic package. Analytical and numerical results are searched for the skin friction coefficient, Nusselt number and the velocity and temperature profiles. It is found on one hand that the Nusselt number increases as Eckert number or channel half-angle increases, but it decreases as Reynolds number increases. On the other hand, it is also found that the presence of Cu nanoparticles in a water base fluid enhances heat transfer between nonparallel plane walls and in consequence the Nusselt number increases with the increase of nanoparticles volume fraction. Finally, an excellent agreement between analytical results and those obtained by numerical Runge-Kutta method is highly noticed.

Color image classification and retrieval through ternary decision structure based multi-category TWSVM

In this paper, we propose Ternary Decision Structure based multi-category twin support vector machines (TDS-TWSVM) classifier. Twin support vector machines (TWSVM) formulation deals with finding non-parallel plane classifier which is obtained by solving two related Quadratic Programming Problems (QPPs). The proposed TDS-TWSVM classifier is an extension of TWSVM so as to handle multi-class data and is more efficient in terms of training and testing time of classifiers. For a K-class problem, a balanced ternary structure requires ⌈log3K⌉ comparisons for evaluating a test sample. The experimental results depict that TDS-TWSVM outperforms One-Against-All TWSVM (OAA-TWSVM) and binary tree-based TWSVM (TB-TWSVM) in terms of classification accuracy. We have shown the efficacy of the proposed algorithm via image classification and further for image retrieval. Experiments are performed on a varied range of benchmark image databases with 5-fold cross validation.

Application of the Generalized Decomposition Method for Solving the Nonlinear Problem of Jeffery–Hamel Flow

In this paper the nonlinear problem of the two-dimensional flow of an incompressible viscous fluid between nonparallel plane walls is investigated analytically. The third-order nonlinear differential equation governing the dynamic field of the considered flow has been treated by a new analytical method called the generalized decomposition method (GDM). Indeed, this method introduces a new strategy of decomposition that can use all necessary information concerning the terms of series solutions and the nonlinear term Nu. This paper investigates, on the one hand, the velocity distribution in convergent and divergent channels for various Reynolds numbers, Re and various channel half-angles, ?, and, on the other hand, the compares our results with the results obtained by the fourth-order Runge Kutta method and other applied analytical methods, thus showing the higher accuracy of the adopted generalized decomposition method.