Showing papers in "The Journal of Information and Computational Science in 2013"

Triangular Hesitant Fuzzy Set and Its Application to Teaching Quality Evaluation

Abstract: As a generalization of fuzzy set, hesitant fuzzy set is a very useful technique to express people’s hesitancy in daily life. But there are weak points in traditional hesitant fuzzy set, which expressed the membership degrees of an element to a given set only by several crisp numbers. In this paper, we extend the traditional hesitant fuzzy set to Triangular Fuzzy Hesitant Fuzzy Set (TFHFS), in which the membership degree of an element to a given set is expressed by several possible triangular fuzzy numbers. Then a series of aggregation operators for TFHFEs are proposed. Furthermore, we apply them to multiple attribute decision making with triangular fuzzy hesitant fuzzy information.

Sustainable Supplier Evaluation Based on Intuitionistic Fuzzy Sets Group Decision Methods

Abstract: More and more practitioners and scholars have paid much attention in sustainable supply chain management in recent years, the most important operational tasks for the sustainable supply chain management is supplier evaluation process. This paper firstly introduced the study situation of sustainability and supplier evaluation, The criteria has been determined for the sustainable supplier evaluation. Based on the previous studies, a new method for supplier selection based on intuitionistic fuzzy sets is proposed. Finally, an empirical study is researched to test the validity and efficiency of the indicators and the method for sustainable supplier evaluation.

Intuitionistic Trapezoidal Fuzzy Information Aggregation Methods and Their Applications to Teaching Quality Evaluation

Abstract: As an extension of the intuitionistic fuzzy set, intuitionistic trapezoidal fuzzy set is an invaluable mathematical theory to express uncertain phenomenon in our real word. In this article, the aggregation methods of the intuitionistic trapezoidal fuzzy information are chief researched. Firstly, the generalized aggregation operator is introduced for aggregating intuitionistic trapezoidal fuzzy information, such as the Generalized Intuitionistic Trapezoidal Fuzzy Weighted Averaging operator (GITFWA). Then, various properties of the proposed aggregation operator are established. Finally, I investigate the evaluation of the teaching quality based on the proposed operator under intuitionistic trapezoidal fuzzy environment.

Discontinuous Galerkin Method for Time Fractional Diffusion Equation

Abstract: In this paper, we consider the discontinuous Galerkin method for time fractional diffusion equation. Firstly, GMMP scheme is introduced for time discretization. Then fully discrete scheme is obtained by using local discontinuous Galerkin method for space discretization. Optimal convergence rate is proved for space discretization and stability analysis is given. Theoretical analysis show that, the locality and high accuracy properties of discontinuous Galerkin method still hold for time fractional differential equation. Finally, theoretical results are confirmed through numerical examples.

An Improved Shuffled Frog Leaping Algorithm

Abstract: Shuffled Frog Leaping Algorithm (SFLA) is a new heuristic algorithm for global optimization. By analyzing the optimization mechanism of SFLA, an improved shuffled frog leaping algorithm is proposed. This new algorithm constructs the initial population using the principle of orthogonal design, which can make the population distribute more evenly in the feasible region and make the algorithm search more evenly in the feasible solution space. The sub-division method of the population is improved in order to narrow the individual difference and improve population diversity. In the solution update formula, an adaptive factor is designed to adjust the moving step, which speeds up the convergence process. Experimental results show that the improved SFLA avoids premature convergence effectively, improves the efficiency of search for complex functions and has a better convergence result and higher accuracy.

A Calibration Method of Three-axis Magnetic Sensor Based on Ellipsoid Fitting

Abstract: It is very essential for a magnetic sensor to be calibrated before use because of various disturbances. However, Constrained by maneuverability of carriers, sample data distribute in a quite limited area, traditional calibration approach using ordinary least-square ellipsoid fitting algorithm makes large error or even cannot compensate properly. In this paper, a new onboard calibration method based on ellipsoid fitting is presented, without requiring external reference. Sample data are obtained in the restricted area by rotating carrier in three simple traces, ellipsoid coefficient are calculated using constrained least squares algorithm. The proposed method is contrasted with the traditional method via software simulation and experimental test; the results indicate that the new calibration method is stable and reliable, and superior to the traditional approach.

Fault Diagnosis of Rolling Bearings Based on IMF Envelope Sample Entropy and Support Vector Machine

Abstract: In this paper, a new fault feature extraction method based on Intrinsic Mode Function (IMF) envelope sample entropy (SampEn) is proposed for rolling bearings fault diagnosis. First, the Empirical Mode Decomposition (EMD) method is utilized to decompose the vibration signals self-adaptively into a number of IMFs which represent different frequency bands from high to low. Second, the IMF envelope signals are used to highlight the fault-induced information in a structurally simpler and physically more meaningful way than the original signals. Thus, the shortcoming of SampEn assigning high values to uncorrelated random signals can be overcome. Finally, the IMF envelope SampEn serve as a fault feature vector to be input into multi-class classifier of Support Vector Machine (SVM) for identification of different bearing conditions. The experimental results indicate that the proposed approach based on IMF envelope SampEn can identify different fault types as well as levels of severity effectively and is superior to that based on IMF SampEn.

Normalized Geometric Bonferroni Operators of Hesitant Fuzzy Sets and Their Application in Multiple Attribute Decision Making

Abstract: The Bonferroni Mean (BM) is a very useful aggregation technique, because it can capture the interrelation-ship between input arguments. Many BM type operators have been proposed, such as the weighted BM, the generalized BM, the intuitionistic fuzzy BM, and so on. However, the geometric BM for Hesitant Fuzzy Sets (HFSs) hasn’t been studied. In this paper, based on the geometric mean and the BM, we will propose two normalized weighted geometric BMs, and they can reect the interrelationship between the individual criterion and other criterion, which is the main advantage of the BM. Then, we will study some properties of the proposed operators, and apply them to deal with Multiple Attribute Decision Making (MADM) problems under the hesitant fuzzy environments. Finally, an example is presented to verify the developed approach.

The Probabilistic Hesitant Fuzzy Weighted Average Operators and Their Application in Strategic Decision Making

Abstract: For the decision making problems with probability, the immediate probability and information which are represented with hesitant fuzzy values, some new aggregation operators are developed, including the Probabilistic Hesitant Fuzzy Weighted Average (P-HFWA) operator, the Immediate Probability Hesitant Fuzzy Ordered Weighted Average (IP-HFOWA) operator, and the Probability Hesitant Fuzzy Ordered Weighted Average (P-HFOWA) operator. We also investigate the properties of the developed operators, such as idempotency, permutation, monotonicity and boundary. Finally, an example about the selection of strategies is given to verify the developed operators.

An Approach of Time Series Piecewise Linear Representation Based on Local Maximum Minimum and Extremum

Abstract: Time series is a kind of important complex data. It is a non-trivial problem to store data, analyze data and mine knowledge in its original data directly because of the inherent high dimensionality and complexity of the data. It is the most promising solution to achieve dimensionality reduction on the data. There are five major techniques in dimensionality reduction such as Discrete Fourier Transform, Discrete Wavelets Transform, Singular Value Decomposition, Symbolic Representation and Piecewise Linear Representation. Integrating the idea of important point and extreme point, a new approach of time series piecewise linear representation are proposed based on local maximum minimum and extremum in this paper. The results of experiments by using the public datasets from several different fields are shown that the proposed technique appears better fitting effect on the adjacent data value less volatile datasets and it has nice fitting effects in the volatile datasets under the low compression ratio condition compared with two other piecewise liner representation techniques.

An Energy Saving Routing Algorithm Based on Dijkstra in Wireless Sensor Networks

Abstract: This paper presents an Energy Saving-oriented Routing Algorithm Based on Dijkstra (ESRAD) in Wireless Sensor Networks (WSN). The ESRAD is designed to be economic to energy consumption in information transmission between any of two sensor nodes in a WSN. By modeling energy consumption of nodes in a WSN as evaluation index of hop count judgment in least-hop routing algorithm, this study first presents an improved Energy Saving-oriented Least-hop Routing Algorithm (ESLHA) to search the shortest path of energy consumption with least nodes between origin node and object node. Then, in the light that the ESLHA does not take the energy consumption in transmission process into account, the ESRAD models the evaluation index of energy consumption at each node, which is the weight of each edge in a WSN, by introducing energy consumption at both node and transmission process, and employs a Dijkstra-based routing algorithm to efficiently search the reliable shortest path with least energy consumption. The performance of the ESRAD is compared to those of the ESLHA and the Dijkstra for different WSNs. Extensive simulation tests on randomly simulated WSNs have demonstrated the potential of the proposed ESRAD for searching the shortest path with least energy consumption to transmit information between any of two nodes in WSNs.

A Region-based Image Segmentation Method Under P Systems

Abstract: Due to the reason of iterative calculation, there are some drawbacks for existing image segmentation methods based on region growing, such as large computation cost and low real-timing. Membrane computing is a novel class of computing models inspired by the structure and function of living cells, also known as P systems, which have the advantage of distributed and parallel computing. In order to overcome the drawbacks of existing image segmentation methods based on region growing, a regionbased image segmentation method based on P systems is proposed in this paper. A tissue-like P system designed specially applies evolution rules and communication rules to achieve an adaptive region growing for image segmentation. The proposed image segmentation method based on tissue-like P systems is evaluated in several standard gray-scale images and compared with the traditional image segmentation method based on region growing.

A Novel Thresholding Method in Removing Noises of Electrocardiogram Based on Wavelet Transform

Abstract: Electrocardiogram (ECG) signal is the most direct means of detecting human health condition. In the process of acquisition, ECG will be contaminated by all kinds of noises and interferences, while the effects of traditional wavelet denoising algorithms are not very satisfying. This paper presented a novel thresholding method based on Discrete Wavelet Transform (DWT). Compared with the traditional hard thresholding and soft thresholding, it can effectively suppress all kinds of high-frequency and lowfrequency noises. To a certain extent, the novel thresholding method protects the characteristics and amplitude of ECG signal perfectly. Finally, MIT-BIH arrhythmia database was used to verify this novel thresholding method; the experimental results showed that the performance of the proposed algorithm is better than the traditional wavelet thresholding algorithms. This proposed new thresholding algorithm can maintain the geometrical characteristics of ECG signal much better, and it also has higher Signal Noise Ratio (SNR).

Weighted Rational Quartic Spline Interpolation

Abstract: The weighted rational quartic spline interpolation has been constructed using two kinds of rational quartic spline with linear denominator in this paper. And the conditions of monotonicity preserving and C 2 continuity have been discussed. In addition to, the problem that constrains the weighted interpolating curves to lie above or below a given straight line or quadratic curve has been solved.

Noise Reduction of Accelerometer Signal with Singular Value Decomposition and Savitzky-Golay Filter

Abstract: The quartz flexure accelerometer has been applied in many inertial systems, but the accelerometer signal may be infected by various noise components. In order to be sufficient for the precision requirement, a noise reduction method is designed and explored to meliorate the measurement signal. By constructing a Hankel matrix with the single channel collected signal, the singular value decomposition technology is utilized to determine a threshold that can distinguish the clean signal and noise signal. When the singular values are lower than this threshold, they are set to be zeros, and the Hankel matrix can be reconstructed. Preliminarily, the enhancement of collected signal is achieved. Then, the Savitzky-Golay filter is employed to smooth the above-described enhanced signal. Finally, the denoised signal is acquired, and its result is evaluated by Allan variance and statistical parameters. With the static and dynamic accelerometer signals, the proposed method was validated by denoising experiments. The test results showed that the satisfying performance of noise reduction was implemented. It is demonstrated that the proposed method is adaptable, and the measurement accuracy of accelerometer signal can be improved.

A Backtracking Approach to Minimal Cost Feature Selection of Numerical Data

Abstract: In data mining applications, one of the most fundamental problems is feature selection, which can improve the generalization capability of patterns. Recent developments in this field have led to a renewed interest in cost-sensitive feature selection. Minimal cost feature selection is devoted to obtain a trade-off between test costs and misclassification costs. However, this problem has been addressed on only nominal data. In this paper, we consider numerical data with measurement errors and propose a backtracking approach for this problem. First, we build a data model with normal distribution measurement errors. In order to deal with this model, the neighborhood of each data item is constructed through the confidence interval. Compared with discretized intervals, neighborhoods are more reasonable to maintain the information of data. And then, we redefine the minimal total cost feature selection problem on the neighborhood model. Finally, a backtracking algorithm with three effective pruning techniques is proposed to deal with this problem. Experimental results indicate that the pruning techniques are effective, and the algorithm is efficient for datasets with nearly one thousand objects.

Research of FOD Recognition Based on Gabor Wavelets and SVM Classification

Abstract: Foreign Object Debris (FOD) on civilian and military runways threatens lives, disrupts service and causes billions of dollars in aircraft engine damage annually. Currently, most of the FOD monitoring is still done by man, which is inefficient and unreliable. This paper proposed a novel framework for Foreign Object Debris (FOD) detection on the runway surface based on Gabor wavelets and Support Vector Machine (SVM). The proposed system is carried by a vehicle. The framework has three steps. In the first step, the FOD is detected from the runway. In the second step, Gabor wavelets are used to extract features. In the third step, when the Gabor features were obtained, classifications were done by Support Vector Machine (SVM). Experiment results showed that the proposed framework is effective and accurately.

LIF: A Dynamic Scheduling Algorithm for Cloud Data Centers Considering Multi-dimensional Resources ⋆

Abstract: Essential requirements of a dynamic resource scheduler is to have low computational complexity, require little information about the system state, and be robust to changes in the traffic parameters. To meet these requirements, this paper introduces a dynamic scheduling algorithm, called Lowest Integratedload First (LIF), for Cloud datacenters in a highly changing environment. One of the challenging scheduling problems in Cloud data centers is to consider allocation and migration of multi-type virtual machines on hosting physical machines with multi-dimensional resources such as CPU, memory and network bandwidth etc. In general, load-balance scheduling is NP-hard problem as proved in many open literatures. Unlike traditional load-balance scheduling algorithms considering only one factor such as CPU, which can cause hotspots or bottlenecks in many cases, LIF treats multi-dimensional resource such as CPU, memory and network bandwidth integrated for both physical machines and virtual machines in real time scheduling to minimize total imbalance level of Cloud data centers. There still lack of related metrics for scheduling algorithms considering multi-dimensional resource. In this paper, multidimensional integrated measurement for total imbalance level of Cloud data centers as well as average imbalance level of each server is developed. Both theoretical proofs and simulation results show that LIF algorithm has good performance regarding total imbalance value, average imbalance value as well as meeting essential requirements of a resource scheduler.

Cognitive Radio Decision Engine Based on Binary Chaotic Particle Swarm Optimization

Abstract: A novel Cognitive Radio (CR) decision engine based on Binary Chaotic Particle Swarm Optimization (BCPSO) is presented to enable robust and spectrally-efficient transmission in this paper. The BCPSO algorithm is used to adjust transmitter parameters so that the proposed CR decision engine can adjust the transmission scheme to adapt to the instantaneous channel condition. The obtained results in the Orthogonal Frequency Division Multiplexing (OFDM) system are compared with those of Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO) algorithm and the superiority of the proposed algorithm in convergence and accuracy over other algorithms is demonstrated. The proposed CR decision engine can select the optimal transmission scheme in real time and have the versatility for multiple applications with different Quality of Service (QoS) requirements.

Combined ANN Prediction Model for Rock Fragmentation Distribution due to Blasting

Abstract: It is feasible using Artificial Neural Network (ANN) to predict the distribution of particle size in bench blasting. It explored the minimum error based on the steepest descent method and plunged into local minima easily. A Back-propagation Neural Network (BPNN) improved by genetic algorithm (GA-BP) to predict mean particle size (K50) was proposed. The input variables used for the proposed method were rock joints (J), Protogyakonov’s coefficient (f ), burden (W ), time interval of millisecond (T ), detonation velocity (V ), explosive specific charge (G), stemming length (L) and hole-space (S). After using the practical data for training and testing, the forecasted mean Relative Errors (RE) of Multivariate Regression Analysis (MVRA), BPNN were 11:15% and 7:38% respectively while the RE achieved by GA-BP was 3:09%. Results indicate that GA-BP model can efficiently reach the expected target, and the problem of incidental trap in local minimum is solved. The improved model is more suitable for prediction of blasting fragmentation.

Estimating the Number of Components in Gaussian Mixture Models Adaptively

Abstract: An important but difficult problem of mixture model is estimating the number of components, k, by model selection criterion. We investigate the sum of weighted real and imaginary parts of all LogCharacteristic Functions (LCF) for Gaussian Mixture Model (GMM) and propose a new method to estimate k, adaptively. Our method defines the Sum of Weighted Real parts of all LCFs (SWRLCF) as a new convergent function and propose a new model selection criterion based on it. Our new model criterion makes use of the stability of the SWRLCF when k is larger than the true number of components. The univariate acidity and simulated 2D datasets are used to test. Experiment results suggest that our method without any priori is more suited for large sample applications than Akaike’s Information Criterion (AIC), AIC3, Bayesian Information Criterion (BIC) and the Stepwise Split and-merge EM (SSMEM) methods.

An Improved Mass-spring Model for Simulation of Soft Tissue Deformation ⋆

Abstract: In this paper, the simulation of soft tissue deformation in virtual surgery is studied. Because linear elastic model and explicit Euler integration are implemented in most of mass spring model, its realism and realtime is too low. To overcome these disadvantages, an improved mass spring model and its simulation algorithm for soft tissue deformation are proposed. Tetrahedral mass spring model is constructed firstly, in which an improved mechanical model is defined to reduce the probability of super elasticity and improve the realism of simulation. Then, a dynamic local simulation solution algorithm is presented to solve the deformation equations, based on an improved explicit Euler integration. Experiment results show that these proposed methods have better realism, real-time and stability than the traditional methods, so they can be implemented to real time simulation of soft tissue deformation.

Image-based Method for Concrete Bridge Crack Detection ?

Abstract: This paper presents a modifled image processing method for detecting cracks in surface images of concrete bridges. Clip, fllling and rotation transformation are applied on concrete images for precise extracting cracks. Modifled C-V model, adaptive threshold, morphology, C-V model and Canny are compared on segmenting crack images which are gathered in difierent illumination. Analysis results indicated that the misclassiflcation rate of the modifled C-V model is 3.56% and the operation time is 96 ms. Therefore, the proposed method can be used for the accurate detection of cracks in surface images recorded under various conditions. Moreover, the estimated crack widths are in good agreement with those measured manually.

A New LEACH-Based Routing Clustering Protocol in WSN

Abstract: Aiming at the disadvantages of LEACH protocol, a new multi-hop WSN cluster routing protocol was proposed, realizing the purpose of saving energy. The election of each cluster heads is on the basis of the residual energy of the node and the distance between the node and the base station. The non-cluster head nodes has taken into account of factors, such as Received Signal Strength Indicator (RSSI) and the energy of cluster heads joining the cluster. A method of establishing a hierarchical routing tree with hop count is used for data transmission, the count which is obtained with division area by fixed energy-broadcast message. Results of simulation indicate that the improved protocol can balance the network load and enhance the network operation cycle.

A Class of Cubic Trigonometric B\'ezier Curve with a Shape Parameter

Abstract: A class of cubic trigonometric Bezier curve with a shape parameter analogous to the cubic polynomial Bezier curve is presented in this work. The cubic trigonometric Bezier curve inherits properties similar to those of cubic polynomial Bezier curve, and the shape of the curve can be adjusted by changing the value of the shape parameter. In proper conditions, the cubic trigonometric Bezier curve can be used to exactly represent ellipse and parabola.

Numerical Method in Solving Fredholm Integro-differential Equations by Using Hybrid Function Operational Matrix of Derivative ⋆

Abstract: A numerical method for solving Fredholm integro-differential equations is introduced. The method is based upon hybrid functions and tau method. A hybrid function operational matrix of derivative is presented. The properties of hybrid of block-pulse functions and Chebyshev polynomials are utilized to reduce the integro-differential equations to the solution of algebraic equations. The efficiency of and accuracy of the proposed method is illustrated by three examples.

An Evolutionary Neural Network Approach to Simple Prediction of Dam Deformation

Abstract: Due to the nonlinearity between deformation and its influencing factors, it is difficult to establish an effective and practical dam deformation prediction model. As one of the ideal tools to model a nonlinear relationship between inputs and outputs, Back Propagation Neural Network (BPNN) has recently been employed to predict dam deformation. Despite its extensive applications, BPNN has a slow convergence rate and easily falls into the local minimum, and its design and structural optimization are still done via a time-consuming reiterative trial-and-error approach. In this paper, Levenberg-Marquardt with Genetic Algorithm (GA-LM), an evolutionary neural network model combining the Levenberg-Marquardt (LM) algorithm and Genetic Algorithm (GA), has been developed for predicting dam deformation. LM is used to train NN, which shows faster convergence rate than BPNN. The network architecture is optimized by GA. The performance of GA-LM has been compared with that of conventional BPNN and LM algorithm with trial-and-error approach. The comparison indicates that the predicted dam deformation values using GA-LM model are in good agreement with the measured data, and it is proved that the GA-LM model can offer stronger and better performance than conventional neural networks and give superior predictions over the trial-and-error model.

A Simplified Calculation Model for Moment-rotation Curve Used in Semi-rigid End-plate Connections

Abstract: This paper presents a simplified calculation model for moment-rotation curve used in semi-rigid end-plate connections. The model includes three characteristic parameters: Ke, Me and My of certain kind of endplate connections. Classic trilinear M- curve model and yielding line theory were utilized to deduce the simplified model. In order to verify the calculation method, four semi-rigid end-plate joints were tested by applying monotonic load. Comparisons are made between the results obtained from the model and the experiment results, it has been proved that the M- relationship of semi-rigid end-plate connections, especially their stiffness and flexural capacity during elastic stage, can be accurately estimated by this simplified calculation method and satisfy the requirement of structural design.