Open AccessArticle
Linear Algorithms for Radioelectric Spectrum Forecast
Algorithms 2016, 9(4), 82; doi:10.3390/a9040082 (registering DOI) -
Abstract
This paper presents the development and evaluation of two linear algorithms for forecasting reception power for different channels at an assigned spectrum band of global systems for mobile communications (GSM), in order to analyze the spatial opportunity for reuse of frequencies by secondary
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This paper presents the development and evaluation of two linear algorithms for forecasting reception power for different channels at an assigned spectrum band of global systems for mobile communications (GSM), in order to analyze the spatial opportunity for reuse of frequencies by secondary users (SUs) in a cognitive radio (CR) network. The algorithms employed correspond to seasonal autoregressive integrated moving average (SARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH), which allow for a forecast of channel occupancy status. Results are evaluated using the following criteria: availability and occupancy time for channels, different types of mean absolute error, and observation time. The contributions of this work include a more integral forecast as the algorithm not only forecasts reception power but also the occupancy and availability time of a channel to determine its precision percentage during the use by primary users (PUs) and SUs within a CR system. Algorithm analyses demonstrate a better performance for SARIMA over GARCH algorithm in most of the evaluated variables. Full article
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Open AccessArticle
Cross-Coupled Contouring Control of Multi-DOF Robotic Manipulator
Algorithms 2016, 9(4), 81; doi:10.3390/a9040081 -
Abstract
Reduction of contour error is a very important issue for high precise contour tracking applications, and many control systems were proposed to deal with contour tracking problems for two/three axial translational motion systems. However, there is no research on cross-coupled contour tracking control
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Reduction of contour error is a very important issue for high precise contour tracking applications, and many control systems were proposed to deal with contour tracking problems for two/three axial translational motion systems. However, there is no research on cross-coupled contour tracking control for serial multi-DOF robot manipulators. In this paper, the contouring control of multi-DOF serial manipulators is developed for the first time and a new cross-coupled PD (CC-PD) control law is proposed, based on contour errors of the end-effector and tracking errors of the joints. It is a combination of PD control for trajectory tracking at joint level and PD control for contour tracking at the end-effector level. The contour error of the end-effector is transformed to the equivalent tracking errors of the joints using the Jacobian regulation, and the CC-PD control law is implemented in the joint level. Stability analysis of the proposed CC-PD control system is conducted using the Lyapunov method, followed by some simulation studies for linear and nonlinear contour tracking to verify the effectiveness of the proposed CC-PD control system. Full article
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Open AccessArticle
Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior
Algorithms 2016, 9(4), 80; doi:10.3390/a9040080 -
Abstract
The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day
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The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day loads of users are calculated separately according to different date types (ordinary workdays, day before holidays, holidays). Second, the similarity between user electricity behaviors is mined and the user electricity loads are clustered to classify the users with similar behaviors into the same cluster. Finally, the load forecasting model based on the Online Sequential Extreme Learning Machine (OS-ELM) is applied to different clusters to conduct load forecasting and the load forecast is summed to obtain the system load. In order to prove the validity of the proposed method, we performed simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation. The experimental results show that the proposed method is able to mine the user electricity behaviors deeply, improve the accuracy of load forecasting by the reasonable clustering of users, and reveal the relationship between forecasting accuracy and cluster numbers. Full article
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Open AccessArticle
A Two-Stage Method to Test the Robustness of the Generalized Approximate Message Passing Algorithm
Algorithms 2016, 9(4), 79; doi:10.3390/a9040079 -
Abstract
We propose a two-stage method to test the robustness of the generalized approximate message passing algorithm (GAMP). A pursuit process based on the marginal posterior probability is inserted in the standard GAMP algorithm to find the support of a sparse vector, and a
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We propose a two-stage method to test the robustness of the generalized approximate message passing algorithm (GAMP). A pursuit process based on the marginal posterior probability is inserted in the standard GAMP algorithm to find the support of a sparse vector, and a revised GAMP process is used to estimate the amplitudes of the support. The numerical experiments with simulation and real world data confirm the robustness and performance of our proposed algorithm. Full article
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Open AccessArticle
A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter Optimization
Algorithms 2016, 9(4), 78; doi:10.3390/a9040078 -
Abstract
The issue of exploration-exploitation remains one of the most challenging tasks within the framework of evolutionary algorithms. To effectively balance the exploration and exploitation in the search space, this paper proposes a modified cloud particles differential evolution algorithm (MCPDE) for real-parameter optimization. In
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The issue of exploration-exploitation remains one of the most challenging tasks within the framework of evolutionary algorithms. To effectively balance the exploration and exploitation in the search space, this paper proposes a modified cloud particles differential evolution algorithm (MCPDE) for real-parameter optimization. In contrast to the original Cloud Particles Differential Evolution (CPDE) algorithm, firstly, control parameters adaptation strategies are designed according to the quality of the control parameters. Secondly, the inertia factor is introduced to effectively keep a better balance between exploration and exploitation. Accordingly, this is helpful for maintaining the diversity of the population and discouraging premature convergence. In addition, the opposition mechanism and the orthogonal crossover are used to increase the search ability during the evolutionary process. Finally, CEC2013 contest benchmark functions are selected to verify the feasibility and effectiveness of the proposed algorithm. The experimental results show that the proposed MCPDE is an effective method for global optimization problems. Full article
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Open AccessReview
Algorithms for Drug Sensitivity Prediction
Algorithms 2016, 9(4), 77; doi:10.3390/a9040077 -
Abstract
Precision medicine entails the design of therapies that are matched for each individual patient. Thus, predictive modeling of drug responses for specific patients constitutes a significant challenge for personalized therapy. In this article, we consider a review of approaches that have been proposed
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Precision medicine entails the design of therapies that are matched for each individual patient. Thus, predictive modeling of drug responses for specific patients constitutes a significant challenge for personalized therapy. In this article, we consider a review of approaches that have been proposed to tackle the drug sensitivity prediction problem especially with respect to personalized cancer therapy. We first discuss modeling approaches that are based on genomic characterizations alone and further the discussion by including modeling techniques that integrate both genomic and functional information. A comparative analysis of the prediction performance of four representative algorithms, elastic net, random forest, kernelized Bayesian multi-task learning and deep learning, reflecting the broad classes of regularized linear, ensemble, kernelized and neural network-based models, respectively, has been included in the paper. The review also considers the challenges that need to be addressed for successful implementation of the algorithms in clinical practice. Full article
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Open AccessArticle
A Modified Iterative Algorithm for Split Feasibility Problems of Right Bregman Strongly Quasi-Nonexpansive Mappings in Banach Spaces with Applications
Algorithms 2016, 9(4), 75; doi:10.3390/a9040075 -
Abstract
In this paper, we present a new iterative scheme for finding a common element of the solution set F of the split feasibility problem and the fixed point set F(T) of a right Bregman strongly quasi-nonexpansive mapping T in p
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In this paper, we present a new iterative scheme for finding a common element of the solution set F of the split feasibility problem and the fixed point set F(T) of a right Bregman strongly quasi-nonexpansive mapping T in p-uniformly convex Banach spaces which are also uniformly smooth. We prove strong convergence theorem of the sequences generated by our scheme under some appropriate conditions in real p-uniformly convex and uniformly smooth Banach spaces. Furthermore, we give some examples and applications to illustrate our main results in this paper. Our results extend and improve the recent ones of some others in the literature. Full article
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Open AccessArticle
A Procedure for Identification of Appropriate State Space and ARIMA Models Based on Time-Series Cross-Validation
Algorithms 2016, 9(4), 76; doi:10.3390/a9040076 -
Abstract
In this work, a cross-validation procedure is used to identify an appropriate Autoregressive Integrated Moving Average model and an appropriate state space model for a time series. A minimum size for the training set is specified. The procedure is based on one-step forecasts
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In this work, a cross-validation procedure is used to identify an appropriate Autoregressive Integrated Moving Average model and an appropriate state space model for a time series. A minimum size for the training set is specified. The procedure is based on one-step forecasts and uses different training sets, each containing one more observation than the previous one. All possible state space models and all ARIMA models where the orders are allowed to range reasonably are fitted considering raw data and log-transformed data with regular differencing (up to second order differences) and, if the time series is seasonal, seasonal differencing (up to first order differences). The value of root mean squared error for each model is calculated averaging the one-step forecasts obtained. The model which has the lowest root mean squared error value and passes the Ljung–Box test using all of the available data with a reasonable significance level is selected among all the ARIMA and state space models considered. The procedure is exemplified in this paper with a case study of retail sales of different categories of women’s footwear from a Portuguese retailer, and its accuracy is compared with three reliable forecasting approaches. The results show that our procedure consistently forecasts more accurately than the other approaches and the improvements in the accuracy are significant. Full article
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Open AccessArticle
An Optimization Algorithm for the Design of an Irregularly-Shaped Bridge Based on the Orthogonal Test and Analytic Hierarchy Process
Algorithms 2016, 9(4), 74; doi:10.3390/a9040074 -
Abstract
Irregularly-shaped bridges are usually adopted to connect the main bridge and ramps in urban overpasses, which are under significant flexion-torsion coupling effects and in complicated stress states. In irregular-shaped bridge design, the parameters such as ramp radius, bifurcation diaphragm stiffness, box girder height,
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Irregularly-shaped bridges are usually adopted to connect the main bridge and ramps in urban overpasses, which are under significant flexion-torsion coupling effects and in complicated stress states. In irregular-shaped bridge design, the parameters such as ramp radius, bifurcation diaphragm stiffness, box girder height, and supporting condition could affect structural performance in different manners. In this paper, the influence of various parameters on three indices, including maximum stress, the stress variation coefficient, and the fundamental frequency of torsional vibration, is investigated and analyzed based on orthogonal test method. Through orthogonal analysis, the major influence parameters and corresponding optimal values for these indices are achieved. Combining with the analytic hierarchy process (AHP), the hierarchical structure model of the multi-indices orthogonal test is established and a comprehensive weight analysis method is proposed to reflect the parameter influence on overall mechanical properties of an irregularly-shaped bridge. Influence order and optimal values of parameters for overall mechanical properties are determined based on the weight of factors and levels calculated by the comprehensive weight analysis method. The results indicate that the comprehensive weight analysis method is superior to the overall balance method, which verifies the effectiveness and accuracy of the comprehensive weight analysis in the parameter optimization of the multi-indices orthogonal test for an irregularly-shaped bridge. Optimal parameters obtained in this paper can provide reference and guidance for parameter control in irregularly-shaped bridge design. Full article
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Open AccessArticle
Community Structure Detection for Directed Networks through Modularity Optimisation
Algorithms 2016, 9(4), 73; doi:10.3390/a9040073 -
Abstract
Networks constitute powerful means of representing various types of complex systems, where nodes denote the system entities and edges express the interactions between the entities. An important topological property in complex networks is community structure, where the density of edges within subgraphs is
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Networks constitute powerful means of representing various types of complex systems, where nodes denote the system entities and edges express the interactions between the entities. An important topological property in complex networks is community structure, where the density of edges within subgraphs is much higher than across different subgraphs. Each of these subgraphs forms a community (or module). In literature, a metric called modularity is defined that measures the quality of a partition of nodes into different mutually exclusive communities. One means of deriving community structure is modularity maximisation. In this paper, a novel mathematical programming-based model, DiMod, is proposed that tackles the problem of maximising modularity for directed networks. Full article
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Open AccessArticle
Engineering a Combinatorial Laplacian Solver: Lessons Learned
Algorithms 2016, 9(4), 72; doi:10.3390/a9040072 -
Abstract
Linear system solving is a main workhorse in applied mathematics. Recently, theoretical computer scientists contributed sophisticated algorithms for solving linear systems with symmetric diagonally-dominant (SDD) matrices in provably nearly-linear time. These algorithms are very interesting from a theoretical perspective, but their practical performance
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Linear system solving is a main workhorse in applied mathematics. Recently, theoretical computer scientists contributed sophisticated algorithms for solving linear systems with symmetric diagonally-dominant (SDD) matrices in provably nearly-linear time. These algorithms are very interesting from a theoretical perspective, but their practical performance was unclear. Here, we address this gap. We provide the first implementation of the combinatorial solver by Kelner et al. (STOC 2013), which is appealing for implementation due to its conceptual simplicity. The algorithm exploits that a Laplacian matrix (which is SDD) corresponds to a graph; solving symmetric Laplacian linear systems amounts to finding an electrical flow in this graph with the help of cycles induced by a spanning tree with the low-stretch property. The results of our experiments are ambivalent. While they confirm the predicted nearly-linear running time, the constant factors make the solver much slower for reasonable inputs than basic methods with higher asymptotic complexity. We were also not able to use the solver effectively as a smoother or preconditioner. Moreover, while spanning trees with lower stretch indeed reduce the solver’s running time, we experience again a discrepancy in practice: in our experiments, simple spanning tree algorithms perform better than those with a guaranteed low stretch. We expect that our results provide insights for future improvements of combinatorial linear solvers. Full article
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Open AccessArticle
A Variable Block Insertion Heuristic for the Blocking Flowshop Scheduling Problem with Total Flowtime Criterion
Algorithms 2016, 9(4), 71; doi:10.3390/a9040071 -
Abstract
In this paper, we present a variable block insertion heuristic (VBIH) algorithm to solve the blocking flowshop scheduling problem with the total flowtime criterion. In the VBIH algorithm, we define a minimum and a maximum block size. After constructing the initial sequence, the
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In this paper, we present a variable block insertion heuristic (VBIH) algorithm to solve the blocking flowshop scheduling problem with the total flowtime criterion. In the VBIH algorithm, we define a minimum and a maximum block size. After constructing the initial sequence, the VBIH algorithm starts with a minimum block size being equal to one. It removes the block from the current sequence and inserts it into the partial sequence sequentially with a predetermined move size. The sequence, which is obtained after several block moves, goes under a variable local search (VLS), which is based on traditional insertion and swap neighborhood structures. If the new sequence obtained after the VLS local search is better than the current sequence, it replaces the current sequence. As long as it improves, it keeps the same block size. However, if it does not improve, the block size is incremented by one and a simulated annealing-type of acceptance criterion is used to accept the current sequence. This process is repeated until the block size reaches at the maximum block size. Furthermore, we present a novel constructive heuristic, which is based on the profile fitting heuristic from the literature. The proposed constructive heuristic is able to further improve the best known solutions for some larger instances in a few seconds. Parameters of the constructive heuristic and the VBIH algorithm are determined through a design of experiment approach. Extensive computational results on the Taillard’s well-known benchmark suite show that the proposed VBIH algorithm outperforms the discrete artificial bee colony algorithm, which is one of the most efficient algorithms recently in the literature. Ultimately, 52 out of the 150 best known solutions are further improved with substantial margins. Full article
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Open AccessArticle
Plant Electrical Signal Classification Based on Waveform Similarity
Algorithms 2016, 9(4), 70; doi:10.3390/a9040070 -
Abstract
(1) Background: Plant electrical signals are important physiological traits which reflect plant physiological state. As a kind of phenotypic data, plant action potential (AP) evoked by external stimuli—e.g., electrical stimulation, environmental stress—may be associated with inhibition of gene expression related to stress tolerance.
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(1) Background: Plant electrical signals are important physiological traits which reflect plant physiological state. As a kind of phenotypic data, plant action potential (AP) evoked by external stimuli—e.g., electrical stimulation, environmental stress—may be associated with inhibition of gene expression related to stress tolerance. However, plant AP is a response to environment changes and full of variability. It is an aperiodic signal with refractory period, discontinuity, noise, and artifacts. In consequence, there are still challenges to automatically recognize and classify plant AP; (2) Methods: Therefore, we proposed an AP recognition algorithm based on dynamic difference threshold to extract all waveforms similar to AP. Next, an incremental template matching algorithm was used to classify the AP and non-AP waveforms; (3) Results: Experiment results indicated that the template matching algorithm achieved a classification rate of 96.0%, and it was superior to backpropagation artificial neural networks (BP-ANNs), supported vector machine (SVM) and deep learning method; (4) Conclusion: These findings imply that the proposed methods are likely to expand possibilities for rapidly recognizing and classifying plant action potentials in the database in the future. Full article
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Open AccessReview
An Overview on the Applications of Matrix Theory in Wireless Communications and Signal Processing
Algorithms 2016, 9(4), 68; doi:10.3390/a9040068 -
Abstract
This paper overviews the key applications enabled by matrix theory in two major fields of interest in electrical engineering, namely wireless communications and signal processing. The paper focuses on the fundamental role played by matrices in modeling and optimization of wireless communication systems,
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This paper overviews the key applications enabled by matrix theory in two major fields of interest in electrical engineering, namely wireless communications and signal processing. The paper focuses on the fundamental role played by matrices in modeling and optimization of wireless communication systems, and in detection, extraction and processing of the information embedded in signals. Among the major applications in wireless communications, the role of matrix representations and decompositions in characterizing multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) communication systems is described. In addition, this paper points out the important contribution made by matrices in solving signal estimation and detection problems. Special attention is given to the implementation of matrices in sensor array signal processing and the design of adaptive filters. Furthermore, the crucial role played by matrices in representing and processing digital images is depicted by several illustrative applications. This paper concludes with some applications of matrix theory in the area of compressive sensing of signals and by outlining a few open research problems for future study. Full article
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Open AccessArticle
A New Fuzzy Harmony Search Algorithm Using Fuzzy Logic for Dynamic Parameter Adaptation
Algorithms 2016, 9(4), 69; doi:10.3390/a9040069 -
Abstract
In this paper, a new fuzzy harmony search algorithm (FHS) for solving optimization problems is presented. FHS is based on a recent method using fuzzy logic for dynamic adaptation of the harmony memory accepting (HMR) and pitch adjustment (PArate)
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In this paper, a new fuzzy harmony search algorithm (FHS) for solving optimization problems is presented. FHS is based on a recent method using fuzzy logic for dynamic adaptation of the harmony memory accepting (HMR) and pitch adjustment (PArate) parameters that improve the convergence rate of traditional harmony search algorithm (HS). The objective of the method is to dynamically adjust the parameters in the range from 0.7 to 1. The impact of using fixed parameters in the harmony search algorithm is discussed and a strategy for efficiently tuning these parameters using fuzzy logic is presented. The FHS algorithm was successfully applied to different benchmarking optimization problems. The results of simulation and comparison studies demonstrate the effectiveness and efficiency of the proposed approach. Full article
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Open AccessArticle
Fault Sensing Using Fractal Dimension and Wavelet
Algorithms 2016, 9(4), 66; doi:10.3390/a9040066 -
Abstract
A new fusion sensing (FS) method was proposed by using the improved fractal box dimension (IFBD) and a developed maximum wavelet coefficient (DMWC) for fault sensing of an online power cable. There are four strategies that were used. Firstly, the traditional fractal box
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A new fusion sensing (FS) method was proposed by using the improved fractal box dimension (IFBD) and a developed maximum wavelet coefficient (DMWC) for fault sensing of an online power cable. There are four strategies that were used. Firstly, the traditional fractal box dimension was improved to enlarge the feature distances between the different fault classes. Secondly, the IFBD recognition algorithm was proposed by using the improved fractal dimension feature extracted from the three-phase currents for the first stage of fault recognition. Thirdly, the DMWC recognition algorithm was developed based on the K-transform and wavelet analysis to establish the relationship between the maximum wavelet coefficient and the fault class. Fourthly, the FS method was formed by combining the IFBD algorithm and the DMWC algorithm in order to recognize the 10 types of short circuit faults of online power. The designed test system proved that the FS method increased the fault recognition accuracy obviously. In addition, the parameters of the initial angle, transient resistance, and fault distance had no influence on the FS method. Full article
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Open AccessArticle
Comparison of Different Algorithms to Orthorectify WorldView-2 Satellite Imagery
Algorithms 2016, 9(4), 67; doi:10.3390/a9040067 -
Abstract
Due to their level of spatial detail (pixel dimensions equal to or less than 1 m), very high-resolution satellite images (VHRSIs) need particular georeferencing and geometric corrections which require careful orthorectification. Although there are several dedicated algorithms, mainly commercial and free software for
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Due to their level of spatial detail (pixel dimensions equal to or less than 1 m), very high-resolution satellite images (VHRSIs) need particular georeferencing and geometric corrections which require careful orthorectification. Although there are several dedicated algorithms, mainly commercial and free software for geographic information system (GIS) and remote sensing applications, the quality of the results may be inadequate in terms of the representation scale for which these images are intended. This paper compares the most common orthorectification algorithms in order to define the best approach for VHRSIs. Both empirical models (such as 2D polynomial functions, PFs; or 3D rational polynomial functions, RPFs) and rigorous physical and deterministic models (such as Toutin) are considered. Ground control points (GCPs) and check points (CPs)—whose positions in the image as, well as in the real world, are known—support algorithm applications. Tests were executed on a WorldView-2 (WV-2) panchromatic image of an area near the Gulf of Naples in Campania (Italy) to establish the best-performing algorithm. Combining 3D RPFs with 2D PFs produced the best results. Full article
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Open AccessArticle
Local Convergence Analysis of an Eighth Order Scheme Using Hypothesis Only on the First Derivative
Algorithms 2016, 9(4), 65; doi:10.3390/a9040065 -
Abstract
In this paper, we propose a local convergence analysis of an eighth order three-step method to approximate a locally unique solution of a nonlinear equation in a Banach space setting. Further, we also study the dynamic behaviour of that scheme. In an earlier
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In this paper, we propose a local convergence analysis of an eighth order three-step method to approximate a locally unique solution of a nonlinear equation in a Banach space setting. Further, we also study the dynamic behaviour of that scheme. In an earlier study, Sharma and Arora (2015) did not discuss these properties. Furthermore, the order of convergence was shown using Taylor series expansions and hypotheses up to the fourth order derivative or even higher of the function involved which restrict the applicability of the proposed scheme. However, only the first order derivatives appear in the proposed scheme. To overcome this problem, we present the hypotheses for the proposed scheme maximum up to first order derivative. In this way, we not only expand the applicability of the methods but also suggest convergence domain. Finally, a variety of concrete numerical examples are proposed where earlier studies can not be applied to obtain the solutions of nonlinear equations on the other hand our study does not exhibit this type of problem/restriction. Full article
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Open AccessArticle
Noise Reduction of Steel Cord Conveyor Belt Defect Electromagnetic Signal by Combined Use of Improved Wavelet and EMD
Algorithms 2016, 9(4), 62; doi:10.3390/a9040062 -
Abstract
In order to reduce the noise of a defect electromagnetic signal of the steel cord conveyor belt used in coal mines, a new signal noise reduction method by combined use of the improved threshold wavelet and Empirical Mode Decomposition (EMD) is proposed. Firstly,
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In order to reduce the noise of a defect electromagnetic signal of the steel cord conveyor belt used in coal mines, a new signal noise reduction method by combined use of the improved threshold wavelet and Empirical Mode Decomposition (EMD) is proposed. Firstly, the denoising method based on the improved threshold wavelet is applied to reduce the noise of a defect electromagnetic signal obtained by an electromagnetic testing system. Then, the EMD is used to decompose the denoised signal and then the effective Intrinsic Mode Function (IMF) is extracted by the dominant eigenvalue strategy. Finally, the signal reconstruction is carried out by utilizing the obtained IMF. In order to verify the proposed noise reduction method, the experiments are carried out in two cases including the defective joint and steel wire rope break. The experimental results show that the proposed method in this paper obtains the higher Signal to Noise Ratio (SNR) for the defect electromagnetic signal noise reduction of steel cord conveyor belts. Full article
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Open AccessMeeting Report
Theorietage der Gesellschaft für Informatik in Speyer 2015—Special Issue
Algorithms 2016, 9(4), 64; doi:10.3390/a9040064 -
Abstract We briefly report on the national workshops on Formal Languages and Automata Theory as well as on Algorithms and Complexity Theory held in early Autumn, 2015. Full article