**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**286

# Search results for: Gradient

##### 286 Mathematical Modeling of the Working Principle of Gravity Gradient Instrument

**Authors:**
Danni Cong,
Meiping Wu,
Hua Mu,
Xiaofeng He,
Junxiang Lian,
Juliang Cao,
Shaokun Cai,
Hao Qin

**Abstract:**

**Keywords:**
Gravity gradient,
accelerometer,
gravity gradient sensor,
single-axis rotation modulation.

##### 285 Simulating Gradient Contour and Mesh of a Scalar Field

**Authors:**
Usman Ali Khan,
Bismah Tariq,
Khalida Raza,
Saima Malik,
Aoun Muhammad

**Abstract:**

**Keywords:**
MATLAB,
Gradient,
Contour,
Scalar Field,
Mesh

##### 284 A New Modification of Nonlinear Conjugate Gradient Coefficients with Global Convergence Properties

**Authors:**
Ahmad Alhawarat,
Mustafa Mamat,
Mohd Rivaie,
Ismail Mohd

**Abstract:**

**Keywords:**
Conjugate gradient method,
conjugate gradient
coefficient,
global convergence.

##### 283 Research on the Correlation of the Fluctuating Density Gradient of the Compressible Flows

**Authors:**
Yasuo Obikane

**Abstract:**

**Keywords:**
Turbulence Modeling ,
Density Gradient Correlation,
Compressible

##### 282 Segmentation of Noisy Digital Images with Stochastic Gradient Kernel

**Authors:**
Abhishek Neogi,
Jayesh Verma,
Pinaki Pratim Acharjya

**Abstract:**

**Keywords:**
Image segmentation,
edge Detection,
noisy images,
spatialfilters,
stochastic gradient kernel.

##### 281 Dynamic Measurement System Modeling with Machine Learning Algorithms

**Authors:**
Changqiao Wu,
Guoqing Ding,
Xin Chen

**Abstract:**

**Keywords:**
Dynamic system modeling,
neural network,
normal
equation,
second order gradient descent.

##### 280 Green Function and Eshelby Tensor Based on Mindlin’s 2nd Gradient Model: An Explicit Study of Spherical Inclusion Case

**Authors:**
A. Selmi,
A. Bisharat

**Abstract:**

Using Fourier transform and based on the Mindlin's 2^{nd} gradient model that involves two length scale parameters, the Green's function, the Eshelby tensor, and the Eshelby-like tensor for a spherical inclusion are derived. It is proved that the Eshelby tensor consists of two parts; the classical Eshelby tensor and a gradient part including the length scale parameters which enable the interpretation of the size effect. When the strain gradient is not taken into account, the obtained Green's function and Eshelby tensor reduce to its analogue based on the classical elasticity. The Eshelby tensor in and outside the inclusion, the volume average of the gradient part and the Eshelby-like tensor are explicitly obtained. Unlike the classical Eshelby tensor, the results show that the components of the new Eshelby tensor vary with the position and the inclusion dimensions. It is demonstrated that the contribution of the gradient part should not be neglected.

**Keywords:**
Eshelby tensor,
Eshelby-like tensor,
Green’s function,
Mindlin’s 2nd gradient model,
Spherical inclusion.

##### 279 Flexural Strength Design of RC Beams with Consideration of Strain Gradient Effect

**Authors:**
Mantai Chen,
Johnny Ching Ming Ho

**Abstract:**

The stress-strain relationship of concrete under flexure is one of the essential parameters in assessing ultimate flexural strength capacity of RC beams. Currently, the concrete stress-strain curve in flexure is obtained by incorporating a constant scale-down factor of 0.85 in the uniaxial stress-strain curve. However, it was revealed that strain gradient would improve the maximum concrete stress under flexure and concrete stress-strain curve is strain gradient dependent. Based on the strain-gradient-dependent concrete stress-strain curve, the investigation of the combined effects of strain gradient and concrete strength on flexural strength of RC beams was extended to high strength concrete up to 100 MPa by theoretical analysis. As an extension and application of the authors’ previous study, a new flexural strength design method incorporating the combined effects of strain gradient and concrete strength is developed. A set of equivalent rectangular concrete stress block parameters is proposed and applied to produce a series of design charts showing that the flexural strength of RC beams are improved with strain gradient effect considered.

**Keywords:**
Beams,
Equivalent concrete stress block,
Flexural strength,
Strain gradient.

##### 278 Learning Flexible Neural Networks for Pattern Recognition

**Authors:**
A. Mirzaaghazadeh,
H. Motameni,
M. Karshenas,
H. Nematzadeh

**Abstract:**

**Keywords:**
Back propagation,
Flexible,
Gradient,
Learning,
Neural network,
Pattern recognition.

##### 277 Hybrid Gravity Gradient Inversion-Ant Colony Optimization Algorithm for Motion Planning of Mobile Robots

**Authors:**
Meng Wu

**Abstract:**

Motion planning is a common task required to be fulfilled by robots. A strategy combining Ant Colony Optimization (ACO) and gravity gradient inversion algorithm is proposed for motion planning of mobile robots. In this paper, in order to realize optimal motion planning strategy, the cost function in ACO is designed based on gravity gradient inversion algorithm. The obstacles around mobile robot can cause gravity gradient anomalies; the gradiometer is installed on the mobile robot to detect the gravity gradient anomalies. After obtaining the anomalies, gravity gradient inversion algorithm is employed to calculate relative distance and orientation between mobile robot and obstacles. The relative distance and orientation deduced from gravity gradient inversion algorithm is employed as cost function in ACO algorithm to realize motion planning. The proposed strategy is validated by the simulation and experiment results.

**Keywords:**
Motion planning,
gravity gradient inversion algorithm,
ant colony optimization.

##### 276 Impact of Viscous and Heat Relaxation Loss on the Critical Temperature Gradients of Thermoacoustic Stacks

**Authors:**
Zhibin Yu,
Artur J. Jaworski,
Abdulrahman S. Abduljalil

**Abstract:**

**Keywords:**
Critical temperature gradient,
heat relaxation,
stack,
viscous effect.

##### 275 A Refined Nonlocal Strain Gradient Theory for Assessing Scaling-Dependent Vibration Behavior of Microbeams

**Authors:**
Xiaobai Li,
Li Li,
Yujin Hu,
Weiming Deng,
Zhe Ding

**Abstract:**

**Keywords:**
Euler-Bernoulli Beams,
free vibration,
higher order
inertia,
nonlocal strain gradient theory,
velocity gradient.

##### 274 Comparison of Three Versions of Conjugate Gradient Method in Predicting an Unknown Irregular Boundary Profile

**Authors:**
V. Ghadamyari,
F. Samadi,
F. Kowsary

**Abstract:**

**Keywords:**
Boundary elements,
Conjugate Gradient Method,
Inverse Geometry Problem,
Sensitivity equation.

##### 273 An Improved Conjugate Gradient Based Learning Algorithm for Back Propagation Neural Networks

**Authors:**
N. M. Nawi,
R. S. Ransing,
M. R. Ransing

**Abstract:**

The conjugate gradient optimization algorithm is combined with the modified back propagation algorithm to yield a computationally efficient algorithm for training multilayer perceptron (MLP) networks (CGFR/AG). The computational efficiency is enhanced by adaptively modifying initial search direction as described in the following steps: (1) Modification on standard back propagation algorithm by introducing a gain variation term in the activation function, (2) Calculation of the gradient descent of error with respect to the weights and gains values and (3) the determination of a new search direction by using information calculated in step (2). The performance of the proposed method is demonstrated by comparing accuracy and computation time with the conjugate gradient algorithm used in MATLAB neural network toolbox. The results show that the computational efficiency of the proposed method was better than the standard conjugate gradient algorithm.

**Keywords:**
Adaptive gain variation,
back-propagation,
activation function,
conjugate gradient,
search direction.

##### 272 Conjugate Gradient Algorithm for the Symmetric Arrowhead Solution of Matrix Equation AXB=C

**Authors:**
Minghui Wang,
Luping Xu,
Juntao Zhang

**Abstract:**

*AXB=C*and the associate optimal approximation problem are considered for the symmetric arrowhead matrix solutions in the premise of consistency. The convergence results of the method are presented. At last, a numerical example is given to illustrate the efficiency of this method.

**Keywords:**
Iterative method,
symmetric arrowhead matrix,
conjugate gradient algorithm.

##### 271 Advanced Neural Network Learning Applied to Pulping Modeling

**Authors:**
Z. Zainuddin,
W. D. Wan Rosli,
R. Lanouette,
S. Sathasivam

**Abstract:**

This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear underlying phenomena contained in the data set and to partly eliminate the burden of having to specify completely the structure of the model. Two different types of neural networks were used for the application of pulping problem. A three layer feed forward neural networks, using the Preconditioned Conjugate Gradient (PCG) methods were used in this investigation. Preconditioning is a method to improve convergence by lowering the condition number and increasing the eigenvalues clustering. The idea is to solve the modified odified problem M-1 Ax= M-1b where M is a positive-definite preconditioner that is closely related to A. We mainly focused on Preconditioned Conjugate Gradient- based training methods which originated from optimization theory, namely Preconditioned Conjugate Gradient with Fletcher-Reeves Update (PCGF), Preconditioned Conjugate Gradient with Polak-Ribiere Update (PCGP) and Preconditioned Conjugate Gradient with Powell-Beale Restarts (PCGB). The behavior of the PCG methods in the simulations proved to be robust against phenomenon such as oscillations due to large step size.

**Keywords:**
Convergence,
pulping modeling,
neural networks,
preconditioned conjugate gradient.

##### 270 Modeling of Pulping of Sugar Maple Using Advanced Neural Network Learning

**Authors:**
W. D. Wan Rosli,
Z. Zainuddin,
R. Lanouette,
S. Sathasivam

**Abstract:**

This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear underlying phenomena contained in the data set and to partly eliminate the burden of having to specify completely the structure of the model. Two different types of neural networks were used for the application of Pulping of Sugar Maple problem. A three layer feed forward neural networks, using the Preconditioned Conjugate Gradient (PCG) methods were used in this investigation. Preconditioning is a method to improve convergence by lowering the condition number and increasing the eigenvalues clustering. The idea is to solve the modified problem where M is a positive-definite preconditioner that is closely related to A. We mainly focused on Preconditioned Conjugate Gradient- based training methods which originated from optimization theory, namely Preconditioned Conjugate Gradient with Fletcher-Reeves Update (PCGF), Preconditioned Conjugate Gradient with Polak-Ribiere Update (PCGP) and Preconditioned Conjugate Gradient with Powell-Beale Restarts (PCGB). The behavior of the PCG methods in the simulations proved to be robust against phenomenon such as oscillations due to large step size.

**Keywords:**
Convergence,
Modeling,
Neural Networks,
Preconditioned Conjugate Gradient.

##### 269 Moving Object Detection Using Histogram of Uniformly Oriented Gradient

**Authors:**
Wei-Jong Yang,
Yu-Siang Su,
Pau-Choo Chung,
Jar-Ferr Yang

**Abstract:**

Moving object detection (MOD) is an important issue in advanced driver assistance systems (ADAS). There are two important moving objects, pedestrians and scooters in ADAS. In real-world systems, there exist two important challenges for MOD, including the computational complexity and the detection accuracy. The histogram of oriented gradient (HOG) features can easily detect the edge of object without invariance to changes in illumination and shadowing. However, to reduce the execution time for real-time systems, the image size should be down sampled which would lead the outlier influence to increase. For this reason, we propose the histogram of uniformly-oriented gradient (HUG) features to get better accurate description of the contour of human body. In the testing phase, the support vector machine (SVM) with linear kernel function is involved. Experimental results show the correctness and effectiveness of the proposed method. With SVM classifiers, the real testing results show the proposed HUG features achieve better than classification performance than the HOG ones.

**Keywords:**
Moving object detection,
histogram of oriented gradient histogram of oriented gradient,
histogram of uniformly-oriented gradient,
linear support vector machine.

##### 268 An Improved Learning Algorithm based on the Conjugate Gradient Method for Back Propagation Neural Networks

**Authors:**
N. M. Nawi,
M. R. Ransing,
R. S. Ransing

**Abstract:**

**Keywords:**
Back-propagation,
activation function,
conjugategradient,
search direction,
gain variation.

##### 267 Signature Recognition Using Conjugate Gradient Neural Networks

**Authors:**
Jamal Fathi Abu Hasna

**Abstract:**

**Keywords:**
Signature Verification,
MATLAB Software,
Conjugate Gradient,
Segmentation,
Skilled Forgery,
and Genuine.

##### 266 A Simple Heat and Mass Transfer Model for Salt Gradient Solar Ponds

**Authors:**
Safwan Kanan,
Jonathan Dewsbury,
Gregory Lane-Serff

**Abstract:**

A salinity gradient solar pond is a free energy source system for collecting, convertingand storing solar energy as heat. In thispaper, the principles of solar pond are explained. A mathematical model is developed to describe and simulate heat and mass transferbehaviour of salinity gradient solar pond. MATLAB codes are programmed to solve the one dimensional finite difference method for heat and mass transfer equations. Temperature profiles and concentration distributions are calculated. The numerical results are validated with experimental data and the results arefound to be in good agreement.

**Keywords:**
Finite Difference method,
Salt-gradient solar-pond,
Solar energy,
Transient heat and mass transfer.

##### 265 Comparison between XGBoost, LightGBM and CatBoost Using a Home Credit Dataset

**Authors:**
Essam Al Daoud

**Abstract:**

Gradient boosting methods have been proven to be a very important strategy. Many successful machine learning solutions were developed using the XGBoost and its derivatives. The aim of this study is to investigate and compare the efficiency of three gradient methods. Home credit dataset is used in this work which contains 219 features and 356251 records. However, new features are generated and several techniques are used to rank and select the best features. The implementation indicates that the LightGBM is faster and more accurate than CatBoost and XGBoost using variant number of features and records.

**Keywords:**
Gradient boosting,
XGBoost,
LightGBM,
CatBoost,
home credit.

##### 264 Yield Onset of Thermo-Mechanical Loading of FGM Thick Walled Cylindrical Pressure Vessels

**Authors:**
S. Ansari Sadrabadi,
G. H. Rahimi

**Abstract:**

In this paper, thick walled Cylindrical tanks or tubes made of functionally graded material under internal pressure and temperature gradient are studied. Material parameters have been considered as power functions. They play important role in the elastoplastic behavior of these materials. To clarify their role, different materials with different parameters have been used under temperature gradient. Finally, their effect and loading effect have been determined in first yield point. Also, the important role of temperature gradient was also shown. At the end the study has been results obtained from changes in the elastic modulus and yield stress. Also special attention is also given to the effects of this internal pressure and temperature gradient in the creation of tensile and compressive stresses.

**Keywords:**
FGM,
Cylindrical pressure tubes,
Small deformation
theory,
Yield onset,
Thermal loading.

##### 263 Affine Projection Adaptive Filter with Variable Regularization

**Authors:**
Young-Seok Choi

**Abstract:**

**Keywords:**
Affine projection,
regularization,
gradient descent,
system identification.

##### 262 Long Term Stability of an Experimental Insulated-Model Salinity-Gradient Solar Pond

**Authors:**
N. W. K. Jayatissa,
R. Attalage,
Prabath Hewageegana,
P. A. A. Perera,
M. A. Punyasena

**Abstract:**

Per capita energy usage in any country is exponentially increasing with their development. As a result, the country’s dependence on the fossil fuels for energy generation is also increasing tremendously creating economic and environmental concerns. Tropical countries receive considerable amount of solar radiation throughout the year, use of solar energy with different energy storage and conversion methodologies is a viable solution to minimize the ever increasing demand for the depleting fossil fuels. Salinity gradient solar pond is one such solar energy application. This paper reports the characteristics and performance of a thermally insulated, experimental salinity-gradient solar pond, built at the premises of the University of Kelaniya, Sri Lanka. Particular stress is given to the behavior of the evolution of the three layer structure exist at the stable state of a salinity gradient solar pond over a long period of time, under different environmental conditions. The operational procedures required to maintain the long term thermal stability are also reported in this article.

**Keywords:**
Salt-gradient,
solar pond,
solar radiation,
renewable energy.

##### 261 An Image Segmentation Algorithm for Gradient Target Based on Mean-Shift and Dictionary Learning

**Authors:**
Yanwen Li,
Shuguo Xie

**Abstract:**

In electromagnetic imaging, because of the diffraction limited system, the pixel values could change slowly near the edge of the image targets and they also change with the location in the same target. Using traditional digital image segmentation methods to segment electromagnetic gradient images could result in lots of errors because of this change in pixel values. To address this issue, this paper proposes a novel image segmentation and extraction algorithm based on Mean-Shift and dictionary learning. Firstly, the preliminary segmentation results from adaptive bandwidth Mean-Shift algorithm are expanded, merged and extracted. Then the overlap rate of the extracted image block is detected before determining a segmentation region with a single complete target. Last, the gradient edge of the extracted targets is recovered and reconstructed by using a dictionary-learning algorithm, while the final segmentation results are obtained which are very close to the gradient target in the original image. Both the experimental results and the simulated results show that the segmentation results are very accurate. The Dice coefficients are improved by 70% to 80% compared with the Mean-Shift only method.

**Keywords:**
Gradient image,
segmentation and extract,
mean-shift algorithm,
dictionary learning.

##### 260 On the Algorithmic Iterative Solutions of Conjugate Gradient, Gauss-Seidel and Jacobi Methods for Solving Systems of Linear Equations

**Authors:**
H. D. Ibrahim,
H. C. Chinwenyi,
H. N. Ude

**Abstract:**

In this paper, efforts were made to examine and compare the algorithmic iterative solutions of conjugate gradient method as against other methods such as Gauss-Seidel and Jacobi approaches for solving systems of linear equations of the form Ax = b, where A is a real n x n symmetric and positive definite matrix. We performed algorithmic iterative steps and obtained analytical solutions of a typical 3 x 3 symmetric and positive definite matrix using the three methods described in this paper (Gauss-Seidel, Jacobi and Conjugate Gradient methods) respectively. From the results obtained, we discovered that the Conjugate Gradient method converges faster to exact solutions in fewer iterative steps than the two other methods which took much iteration, much time and kept tending to the exact solutions.

**Keywords:**
conjugate gradient,
linear equations,
symmetric and positive definite matrix,
Gauss-Seidel,
Jacobi,
algorithm

##### 259 Simulation of Effect of Current Stressing on Reliability of Solder Joints with Cu-Pillar Bumps

**Authors:**
Y. Li,
Q. S. Zhang,
H. Z. Huang,
B. Y. Wu

**Abstract:**

**Keywords:**
Simulation,
Cu-pillar bumps,
Electromigration,
Thermomigration.

##### 258 Effect of Adverse Pressure Gradient on a Fluctuating Velocity over the Co-Flow Jet Airfoil

**Authors:**
Morteza Mirhosseini,
Amir B. Khoshnevis

**Abstract:**

^{o}and this has due to the jet energized, while the angle of attack 20

^{o}has different. The airfoil cord based Reynolds number has 10

^{5}.

**Keywords:**
Adverse pressure gradient,
fluctuating velocity,
wall jet,
co-flow jet airfoil.

##### 257 Numerical Study on the Cavity-Induced Piping Failure of Embankment

**Authors:**
H. J. Kim,
G. C. Park,
K. C. Kim,
J. H. Shin

**Abstract:**

**Keywords:**
Cavity,
Embankment,
Hydraulic gradient,
Piping.