Showing papers in "International Journal of Automation and Computing in 2014"

Optimal Control of Nonlinear Inverted Pendulum System Using PID Controller and LQR: Performance Analysis Without and With Disturbance Input

Abstract: Linear quadratic regulator (LQR) and proportional-integral-derivative (PID) control methods, which are generally used for control of linear dynamical systems, are used in this paper to control the nonlinear dynamical system. LQR is one of the optimal control techniques, which takes into account the states of the dynamical system and control input to make the optimal control decisions. The nonlinear system states are fed to LQR which is designed using a linear state-space model. This is simple as well as robust. The inverted pendulum, a highly nonlinear unstable system, is used as a benchmark for implementing the control methods. Here the control objective is to control the system such that the cart reaches a desired position and the inverted pendulum stabilizes in the upright position. In this paper, the modeling and simulation for optimal control design of nonlinear inverted pendulum-cart dynamic system using PID controller and LQR have been presented for both cases of without and with disturbance input. The Matlab-Simulink models have been developed for simulation and performance analysis of the control schemes. The simulation results justify the comparative advantage of LQR control method.

Adaptive Tracking Control of an Autonomous Underwater Vehicle

Abstract: This paper presents the trajectory tracking control of an autonomous underwater vehicle (AUV). To cope with parametric uncertainties owing to the hydrodynamic effect, an adaptive control law is developed for the AUV to track the desired trajectory. This desired state-dependent regressor matrix-based controller provides consistent results under hydrodynamic parametric uncertainties. Stability of the developed controller is verified using the Lyapunov's direct method. Numerical simulations are carried out to study the efficacy of the proposed adaptive controller.

Strategies and Methods for Cloud Migration

Abstract: Legacy system migration to the cloud brings both great challenges and benefits, so there exist various academic research and industrial applications on legacy system migration to the cloud. By analyzing the research achievements and application status, we divide the existing migration methods into three strategies according to the cloud service models integrally. Different processes need to be considered for different migration strategies, and different tasks will be involved accordingly. The similarities and differences between the migration strategies are discussed, and the challenges and future work about legacy system migration to the cloud are proposed. The aim of this paper is to provide an overall presentation for legacy system migration to the cloud and identify important challenges and future research directions.

Combination of Model-based Observer and Support Vector Machines for Fault Detection of Wind Turbines

Abstract: Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontal-axis wind turbine composed of three blades and a full converter. The support vector approach is data-based and is therefore robust to process knowledge. It is based on structural risk minimization which enhances generalization even with small training data set and it allows for process nonlinearity by using flexible kernels. In this work, a radial basis function is used as the kernel. Different parts of the process are investigated including actuators and sensors faults. With duplicated sensors, sensor faults in blade pitch positions, generator and rotor speeds can be detected. Faults of type stuck measurements can be detected in 2 sampling periods. The detection time of offset/scaled measurements depends on the severity of the fault and on the process dynamics when the fault occurs. The converter torque actuator fault can be detected within 2 sampling periods. Faults in the actuators of the pitch systems represents a higher difficulty for fault detection which is due to the fact that such faults only affect the transitory state (which is very fast) but not the final stationary state. Therefore, two methods are considered and compared for fault detection and isolation of this fault: support vector machines and a Kalman-like observer. Advantages and disadvantages of each method are discussed. On one hand, support vector machines training of transitory states would require a big amount of data in different situations, but the fault detection and isolation results are robust to variations in the input/operating point. On the other hand, the observer is model-based, and therefore does not require training, and it allows identification of the fault level, which is interesting for fault reconfiguration. But the observability of the system is ensured under specific conditions, related to the dynamics of the inputs and outputs. The whole fault detection and isolation scheme is evaluated using a wind turbine benchmark with a real sequence of wind speed.

A Progressive Deployment Strategy for Cooperative Adaptive Cruise Control to Improve Traffic Dynamics

Abstract: Cooperative adaptive cruise control (CACC) vehicles are intelligent vehicles that use vehicular ad hoc networks (VANETs) to share traffic information in real time. Previous studies have shown that CACC could have an impact on increasing highway capacities at high market penetration. Since reaching a high CACC market penetration level is not occurring in the near future, this study presents a progressive deployment approach that demonstrates to have a great potential of reducing traffic congestions at low CACC penetration levels. Using a previously developed microscopic traffic simulation model of a freeway with an on-ramp -- created to induce perturbations and trigger stop-and-go traffic, the CACC system's effect on the traffic performance is studied. The results show significance and indicate the potential of CACC systems to improve traffic characteristics which can be used to reduce traffic congestion. The study shows that the impact of CACC is positive and not only limited to a high market penetration. By giving CACC vehicles priority access to high-occupancy vehicle (HOV) lanes, the highway capacity could be significantly improved with a CACC penetration as low as 20%.

Study of Human Action Recognition Based on Improved Spatio-temporal Features

Abstract: Most of the exist action recognition methods mainly utilize spatio-temporal descriptors of single interest point while ignoring their potential integral information, such as spatial distribution information. By combining local spatio-temporal feature and global positional distribution information (PDI) of interest points, a novel motion descriptor is proposed in this paper. The proposed method detects interest points by using an improved interest point detection method. Then, 3-dimensional scale-invariant feature transform (3D SIFT) descriptors are extracted for every interest point. In order to obtain a compact description and efficient computation, the principal component analysis (PCA) method is utilized twice on the 3D SIFT descriptors of single frame and multiple frames. Simultaneously, the PDI of the interest points are computed and combined with the above features. The combined features are quantified and selected and finally tested by using the support vector machine (SVM) recognition algorithm on the public KTH dataset. The testing results have showed that the recognition rate has been significantly improved and the proposed features can more accurately describe human motion with high adaptability to scenarios.

GLCM Based Extraction of Flame Image Texture Features and KPCA-GLVQ Recognition Method for Rotary Kiln Combustion Working Conditions

Abstract: According to the pulverized coal combustion flame image texture features of the rotary-kiln oxide pellets sintering process, a combustion working condition recognition method based on the generalized learning vector (GLVQ) neural network is proposed. Firstly, the numerical flame image is analyzed to extract texture features, such as energy, entropy and inertia, based on grey-level co-occurrence matrix (GLCM) to provide qualitative information on the changes in the visual appearance of the flame. Then the kernel principal component analysis (KPCA) method is adopted to deduct the input vector with high dimensionality so as to reduce the GLVQ target dimension and network scale greatly. Finally, the GLVQ neural network is trained by using the normalized texture feature data. The test results show that the proposed KPCA-GLVQ classifier has an excellent performance on training speed and correct recognition rate, and it meets the requirement for real-time combustion working condition recognition for the rotary kiln process.

Fuzzy PID Control of Space Manipulator for Both Ground Alignment and Space Applications

Abstract: Considering gravity change from ground alignment to space applications, a fuzzy proportional-integral-differential (PID) control strategy is proposed to make the space manipulator track the desired trajectories in different gravity environments The fuzzy PID controller is developed by combining the fuzzy approach with the PID control method, and the parameters of the PID controller can be adjusted on line based on the ability of the fuzzy controller Simulations using the dynamic model of the space manipulator have shown the effectiveness of the algorithm in the trajectory tracking problem Compared with the results of conventional PID control, the control performance of the fuzzy PID is more effective for manipulator trajectory control

Simulation of Analog Costas Loop Circuits

Abstract: The analysis of stability and numerical simulation of Costas loop circuits for the high-frequency signals is a challenging task. The problem lies in the fact that it is necessary to observe very fast time scale of input signals and slow time scale of signal's phases simultaneously. To overcome this difficulty, it is possible to follow the classical ideas of Gardner and Viterbi to construct a mathematical model of Costas loop, in which only slow time change of signal's phases and frequencies is considered. Such an construction, in turn, requires the computation of phase detector characteristic, depending on the waveforms of the considered signals. In this paper, the problems of nonlinear analysis of Costas loops and the approaches to the simulation of the classical Costas loop, the quadrature phase shift keying (QPSK) Costas loop, and the two-phase Costas loop are discussed. The analytical method for the computation of phase detector characteristics of Costas loops is described.

Feature Representation for Facial Expression Recognition Based on FACS and LBP

Abstract: In expression recognition, feature representation is critical for successful recognition since it contains distinctive information of expressions. In this paper, a new approach for representing facial expression features is proposed with its objective to describe features in an effective and efficient way in order to improve the recognition performance. The method combines the facial action coding system (FACS) and "uniform" local binary patterns (LBP) to represent facial expression features from coarse to fine. The facial feature regions are extracted by active shape models (ASM) based on FACS to obtain the gray-level texture. Then, LBP is used to represent expression features for enhancing the discriminant. A facial expression recognition system is developed based on this feature extraction method by using K nearest neighborhood (K-NN) classifier to recognize facial expressions. Finally, experiments are carried out to evaluate this feature extraction method. The significance of removing the unrelated facial regions and enhancing the discrimination ability of expression features in the recognition process is indicated by the results, in addition to its convenience.

Image Dehazing Based on Haziness Analysis

Abstract: We present two haze removal algorithms for single image based on haziness analysis. One algorithm regards haze as the veil layer, and the other takes haze as the transmission. The former uses the illumination component image obtained by retinex algorithm and the depth information of the original image to remove the veil layer. The latter employs guided filter to obtain the refined haze transmission and separates it from the original image. The main advantages of the proposed methods are that no user interaction is needed and the computing speed is relatively fast. A comparative study and quantitative evaluation with some main existing algorithms demonstrate that similar even better quality results can be obtained by the proposed methods. On the top of haze removal, several applications of the haze transmission including image refocusing, haze simulation, relighting and 2-dimensional (2D) to 3-dimensional (3D) stereoscopic conversion are also implemented.

Consensus Problem of Heterogeneous Multi-agent Systems with Time Delay under Fixed and Switching Topologies

Abstract: Consensus problem is investigated for heterogeneous multi-agent systems composed of first-order agents and second-order agents in this paper. Leader-following consensus protocol is adopted to solve consensus problem of heterogeneous multi-agent systems with time-varying communication and input delays. By constructing Lyapunov-Krasovkii functional, sufficient consensus conditions in linear matrix inequality (LMI) form are obtained for the system under fixed interconnection topology. Moreover, consensus conditions are also obtained for the heterogeneous systems under switching topologies with time delays. Simulation examples are given to illustrate effectiveness of the results.

Automatic Georeferencing of Images Acquired by UAV's

Abstract: This paper implements and evaluates experimentally a procedure for automatically georeferencing images acquired by unmanned aerial vehicles (UAV's) in the sense that ground control points (GCP) are not necessary. Since the camera model is necessary for georeferencing, this paper also proposes a completely automatic procedure for collecting corner pixels in the model plane image to solve the camera calibration problem, i.e., to estimate the camera and the lens distortion parameters. The performance of the complete georeferencing system is evaluated with real flight data obtained by a typical UAV.

N4SID and MOESP Algorithms to Highlight the Ill-conditioning into Subspace Identification

Abstract: In this paper, an analysis for ill conditioning problem in subspace identification method is provided. The subspace identification technique presents a satisfactory robustness in the parameter estimation of process model which performs control. As a first step, the main geometric and mathematical tools used in subspace identification are briefly presented. In the second step, the problem of analyzing ill-conditioning matrices in the subspace identification method is considered. To illustrate this situation, a simulation study of an example is introduced to show the ill-conditioning in subspace identification. Algorithms numerical subspace state space system identification (N4SID) and multivariable output error state space model identification (MOESP) are considered to study, the parameters estimation while using the induction motor model, in simulation (Matlab environment). Finally, we show the inadequacy of the oblique projection and validate the effectiveness of the orthogonal projection approach which is needed in ill-conditioning; a real application dealing with induction motor parameters estimation has been experimented. The obtained results proved that the algorithm based on orthogonal projection MOESP, overcomes the situation of ill-conditioning in the Hankel's block, and thereby improving the estimation of parameters.

Optimal Systematic Determination of Models' Base for Multimodel Representation: Real Time Application

Abstract: The multimodel approach is a powerful and practical tool to deal with analysis, modeling, observation, emulation and control of complex systems. In the modeling framework, we propose in this paper a new method for optimal systematic determination of models' base for multimodel representation. This method is based on the classification of data set picked out of the considered system. The obtained cluster centers are exploited to provide the weighting functions and to deduce the corresponding dispersions and their models' base. A simulation example and an experimental validation on a semi-batch reactor are presented to evaluate the effectiveness of the proposed method.

An Efficient Hop Count Routing Protocol for Wireless Ad Hoc Networks

Abstract: An efficient hop count route finding approach for mobile ad hoc network is presented in this paper. It is an adaptive routing protocol that has a tradeoff between transmission power and hop count for wireless ad hoc networks. During the route finding process, the node can dynamically assign transmission power to nodes along the route. The node who has received route request message compares its power with the threshold power value, and then selects a reasonable route according to discriminating algorithms. This algorithm is an effective solution scheme to wireless ad hoc networks through reasonably selected path to reduce network consumption. Simulation results indicate that the proposed protocol can deliver better performances with respect to energy consumption and end-to-end delay.

Developing Bi-CG and Bi-CR Methods to Solve Generalized Sylvester-transpose Matrix Equations

Abstract: The bi-conjugate gradients (Bi-CG) and bi-conjugate residual (Bi-CR) methods are powerful tools for solving nonsymmetric linear systems Ax = b. By using Kronecker product and vectorization operator, this paper develops the Bi-CG and Bi-CR methods for the solution of the generalized Sylvester-transpose matrix equation $\sum _{i = 1}^p({A_i}X{B_i} + {C_i}{X^{\rm{T}}}{D_i}) = E$ (including Lyapunov, Sylvester and Sylvester-transpose matrix equations as special cases). Numerical results validate that the proposed algorithms are much more efficient than some existing algorithms.

Issues in the Mining of Heart Failure Datasets

Abstract: This paper investigates the characteristics of a clinical dataset using a combination of feature selection and classification methods to handle missing values and understand the underlying statistical characteristics of a typical clinical dataset. Typically, when a large clinical dataset is presented, it consists of challenges such as missing values, high dimensionality, and unbalanced classes. These pose an inherent problem when implementing feature selection and classification algorithms. With most clinical datasets, an initial exploration of the dataset is carried out, and those attributes with more than a certain percentage of missing values are eliminated from the dataset. Later, with the help of missing value imputation, feature selection and classification algorithms, prognostic and diagnostic models are developed. This paper has two main conclusions: 1) Despite the nature of clinical datasets, and their large size, methods for missing value imputation do not affect the final performance. What is crucial is that the dataset is an accurate representation of the clinical problem and those methods of imputing missing values are not critical for developing classifiers and prognostic/diagnostic models. 2) Supervised learning has proven to be more suitable for mining clinical data than unsupervised methods. It is also shown that non-parametric classifiers such as decision trees give better results when compared to parametric classifiers such as radial basis function networks (RBFNs).

An Improved Gravitational Search Algorithm for Dynamic Neural Network Identification

Abstract: Gravitational search algorithm (GSA) is a newly developed and promising algorithm based on the law of gravity and interaction between masses. This paper proposes an improved gravitational search algorithm (IGSA) to improve the performance of the GSA, and first applies it to the field of dynamic neural network identification. The IGSA uses trial-and-error method to update the optimal agent during the whole search process. And in the late period of the search, it changes the orbit of the poor agent and searches the optimal agent's position further using the coordinate descent method. For the experimental verification of the proposed algorithm, both GSA and IGSA are testified on a suite of four well-known benchmark functions and their complexities are compared. It is shown that IGSA has much better efficiency, optimization precision, convergence rate and robustness than GSA. Thereafter, the IGSA is applied to the nonlinear autoregressive exogenous (NARX) recurrent neural network identification for a magnetic levitation system. Compared with the system identification based on gravitational search algorithm neural network (GSANN) and other conventional methods like BPNN and GANN, the proposed algorithm shows the best performance.

Top-k Outlier Detection from Uncertain Data

Abstract: Uncertain data are common due to the increasing usage of sensors, radio frequency identification (RFID), GPS and similar devices for data collection. The causes of uncertainty include limitations of measurements, inclusion of noise, inconsistent supply voltage and delay or loss of data in transfer. In order to manage, query or mine such data, data uncertainty needs to be considered. Hence, this paper studies the problem of top-k distance-based outlier detection from uncertain data objects. In this work, an uncertain object is modelled by a probability density function of a Gaussian distribution. The naive approach of distance-based outlier detection makes use of nested loop. This approach is very costly due to the expensive distance function between two uncertain objects. Therefore, a populated-cells list (PC-list) approach of outlier detection is proposed. Using the PC-list, the proposed top-k outlier detection algorithm needs to consider only a fraction of dataset objects and hence quickly identifies candidate objects for top-k outliers. Two approximate top-k outlier detection algorithms are presented to further increase the efficiency of the top-k outlier detection algorithm. An extensive empirical study on synthetic and real datasets is also presented to prove the accuracy, efficiency and scalability of the proposed algorithms.

Unified MPPT Controller for Partially Shaded Panels in a Photovoltaic Array

Abstract: The power output of the photovoltaic (PV) system having multiple arrays gets reduced to a great extent when it is partially shaded due to environmental hindrances. The maximum power trackers which are conventionally used may not be competent enough to find the maximum power point (MPP) during partially shaded conditions. The sensible reason for the failure of conventional trackers is during partial shaded conditions the PV arrays exhibit multi peak power curves, thereby making simple maximum power point tracking (MPPT) algorithms like perturb and observe (P&O) to get stuck with local maxima instead of capturing global maxima. Therefore, global search MPPT aided by evolutionary and swarm intelligence algorithms will be conducive to find global power point during partially shaded conditions. This work suggests a unified controller which feeds control signal to its power electronic conditioner placed at each module. The evolutionary algorithm which is taken into consideration in this work is differential evolution (DE). The performance of the proposed method is compared to the classical un-dimensional search controller and it is evident from the Matlab/Simulink results that the unified controller prevails over the distributed counterpart.

Semantic Rule Based Image Visual Feature Ontology Creation

Abstract: Multimedia is one of the important communication channels for mankind. Due to the advancement in technology and enormous growth of mankind, a vast array of multimedia data is available today. This has resulted in the obvious need for some techniques for retrieving these data. This paper will give an overview of ontology-based image retrieval system for asteroideae flower family domain. In order to reduce the semantic gap between the low-level visual features of an image and the high-level domain knowledge, we have incorporated a concept of multi-modal image ontology. So, the created asteroideae flower domain specific ontology would have the knowledge about the domain and the visual features. The visual features used to define the ontology are prevalent color, basic intrinsic pattern and contour gradient. In prevalent color extraction, the most dominant color from the images was identified and indexed. In order to determine the texture pattern for a particular flower, basic intrinsic patterns were used. The contour gradients provide the information on the image edges with respect to the image base. These feature values are embedded in the ontology at appropriate slots with respect to the domain knowledge. This paper also defines some of the query axioms which are used to retrieve appropriate information from the created ontology. This ontology can be used for image retrieval system in semantic web.

Stabilizing Sets of PI/PID Controllers for Unstable Second Order Delay System

Abstract: In this paper, the problem of stabilizing an unstable second order delay system using classical proportional-integral-derivative (PID) controller is considered. An extension of the Hermite-Biehler theorem, which is applicable to quasi-polynomials, is used to seek the set of complete stabilizing proportional-integral/proportional-integral-derivative (PI/PID) parameters. The range of admissible proportional gains is determined in closed form. For each proportional gain, the stabilizing set in the space of the integral and derivative gains is shown to be a triangle.

Big Data Modeling and Analysis of Microblog Ecosystem

Abstract: Recent progress of Web 2.0 applications has witnessed the rapid development of microblog in China, which has already been one of the most important ways for online communications, especially on sharing information. This paper tries to make an in-depth investigation on the big data modeling and analysis of microblog ecosystem in China by using a real dataset containing over 17 million records of SinaWeibo users. First, we present the detailed geography, gender, authentication, education and age analysis of microblog users in this dataset. Then we conduct the numerical features distribution analysis, propose the user influence formula and calculate the influences for different kinds of microblog users. Finally, user content intention analysis is performed to reveal users' most concerns in their daily life.

An Integrated Approach to Hypersonic Entry Attitude Control

Abstract: This paper presents an integrated approach based on dynamic inversion (DI) and active disturbance rejection control (ADRC) to the entry attitude control of a generic hypersonic vehicle (GHV). DI is firstly used to cancel the nonlinearities of the GHV entry model to construct a basic attitude controller. To enhance the control performance and system robustness to inevitable disturbances, ADRC techniques, including the arranged transient process (ATP), nonlinear feedback (NF), and most importantly the extended state observer (ESO), are integrated with the basic DI controller. As one primary task, the stability and estimation error of the second-order nonlinear ESO are analyzed from a brand new perspective: the nonlinear ESO is treated as a specific form of forced Lienard system. Abundant qualitative properties of the Lienard system are utilized to yield comprehensive theorems on nonlinear ESO solution behaviors, such as the boundedness, convergence, and existence of periodic solutions. Phase portraits of ESO estimation error dynamics are given to validate our analysis. At last, three groups of simulations, including comparative simulations with modeling errors, Monte Carlo runs with parametric uncertainties, and a six degrees-of-freedom reference entry trajectory tracking are executed, which demonstrate the superiority of the proposed integrated controller over the basic DI controller.

A Hybrid Time-delay Prediction Method for Networked Control System

Abstract: This paper presents an Ethernet based hybrid method for predicting random time-delay in the networked control system. First, db3 wavelet is used to decompose and reconstruct time-delay sequence, and the approximation component and detail components of time-delay sequences are figured out. Next, one step prediction of time-delay is obtained through echo state network (ESN) model and auto-regressive integrated moving average model (ARIMA) according to the different characteristics of approximate component and detail components. Then, the final predictive value of time-delay is obtained by summation. Meanwhile, the parameters of echo state network is optimized by genetic algorithm. The simulation results indicate that higher accuracy can be achieved through this prediction method.

Distributed H ∞ PID Feedback for Improving Consensus Performance of Arbitrary-delayed Multi-agent System

Abstract: The H ? proportional-integral-differential (PID) feedback for arbitrary-order delayed multi-agent system is investigated to improve the system performance. The closed-loop multi-input multi-output (MIMO) control framework with the distributed PID controller is firstly described for the multi-agent system in a unified way. Then, by using the matrix theory, the prescribed H ? performance criterion of the multi-agent system is shown to be equivalent to several independent H ? performance constraints of the single-input single-output (SISO) subsystem with respect to the eigenvalues of the Laplacian matrix. Subsequently, for each subsystem, the set of the PID controllers satisfying the required H ? performance constraint is analytically characterized based on the extended Hermite-Biehler theorem. Finally, the three-dimensional set of the decentralized H ? PID control parameters is derived by finding the intersection of the H ? PID regions for all the decomposed subsystems. The simulation results reveal the effectiveness of the proposed method.

New Results on PWARX Model Identification Based on Clustering Approach

Abstract: This paper deals with the problem of piecewise auto regressive systems with exogenous input (PWARX) model identification based on clustering solution. This problem involves both the estimation of the parameters of the affine sub-models and the hyper planes defining the partitions of the state-input regression. The existing identification methods present three main drawbacks which limit its effectiveness. First, most of them may converge to local minima in the case of poor initializations because they are based on the optimization using nonlinear criteria. Second, they use simple and ineffective techniques to remove outliers. Third, most of them assume that the number of sub-models is known a priori. To overcome these drawbacks, we suggest the use of the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The results presented in this paper illustrate the performance of our methods in comparison with the existing approach. An application of the developed approach to an olive oil esterification reactor is also proposed in order to validate the simulation results.

A Multi-dimensional Index Structure Based on Improved VA-file and CAN in the Cloud

Abstract: Currently, the cloud computing systems use simple key-value data processing, which cannot support similarity search effectively due to lack of efficient index structures, and with the increase of dimensionality, the existing tree-like index structures could lead to the problem of "the curse of dimensionality". In this paper, a novel VF-CAN indexing scheme is proposed. VF-CAN integrates content addressable network (CAN) based routing protocol and the improved vector approximation file (VA-file) index. There are two index levels in this scheme: global index and local index. The local index VAK-file is built for the data in each storage node. VAK-file is the k-means clustering result of VA-file approximation vectors according to their degree of proximity. Each cluster forms a separate local index file and each file stores the approximate vectors that are contained in the cluster. The vector of each cluster center is stored in the cluster center information file of corresponding storage node. In the global index, storage nodes are organized into an overlay network CAN, and in order to reduce the cost of calculation, only clustering information of local index is issued to the entire overlay network through the CAN interface. The experimental results show that VF-CAN reduces the index storage space and improves query performance effectively.

H.264/SVC Mode Decision Based on Mode Correlation and Desired Mode List

Abstract: Design of video encoders involves implementation of fast mode decision (FMD) algorithm to reduce computation complexity while maintaining the performance of the coding. Although H.264/scalable video coding (SVC) achieves high scalability and coding efficiency, it also has high complexity in implementing its exhaustive computation. In this paper, a novel algorithm is proposed to reduce the redundant candidate modes by making use of the correlation among layers. A desired mode list is created based on the probability to be the best mode for each block in base layer and a candidate mode selection in the enhancement layer by the correlations of modes among reference frame and current frame. Our algorithm is implemented in joint scalable video model (JSVM) 9.19.15 reference software and the performance is evaluated based on the average encoding time, peak signal to noise ration (PSNR) and bit rate. The experimental results show 41.89% improvement in encoding time with minimal loss of 0.02 dB in PSNR and 0.05% increase in bit rate.