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Algorithms, Volume 11, Issue 7 (July 2018)

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Open AccessArticle Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions
Algorithms 2018, 11(7), 108; https://doi.org/10.3390/a11070108
Received: 8 June 2018 / Revised: 5 July 2018 / Accepted: 19 July 2018 / Published: 21 July 2018
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Abstract
The most important characteristics of autonomous vehicles are their safety and their ability to adapt to various traffic situations and road conditions. In our research, we focused on the development of controllers for automated steering of a realistically simulated car in slippery road
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The most important characteristics of autonomous vehicles are their safety and their ability to adapt to various traffic situations and road conditions. In our research, we focused on the development of controllers for automated steering of a realistically simulated car in slippery road conditions. We comparatively investigated three implementations of such controllers: a proportional-derivative (PD) controller built in accordance with the canonical servo-control model of steering, a PID controller as an extension of the servo-control, and a controller designed heuristically via the most versatile evolutionary computing paradigm: genetic programming (GP). The experimental results suggest that the controller evolved via GP offers the best quality of control of the car in all of the tested slippery (rainy, snowy, and icy) road conditions. Full article
(This article belongs to the Special Issue Algorithms for PID Controller)
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Open AccessArticle An Ensemble Extreme Learning Machine for Data Stream Classification
Algorithms 2018, 11(7), 107; https://doi.org/10.3390/a11070107
Received: 14 June 2018 / Revised: 29 June 2018 / Accepted: 11 July 2018 / Published: 17 July 2018
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Abstract
Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFN). Because ELM has a fast speed for classification, it is widely applied in data stream classification tasks. In this paper, a new ensemble extreme learning machine is presented. Different from
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Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFN). Because ELM has a fast speed for classification, it is widely applied in data stream classification tasks. In this paper, a new ensemble extreme learning machine is presented. Different from traditional ELM methods, a concept drift detection method is embedded; it uses online sequence learning strategy to handle gradual concept drift and uses updating classifier to deal with abrupt concept drift, so both gradual concept drift and abrupt concept drift can be detected in this paper. The experimental results showed the new ELM algorithm not only can improve the accuracy of classification result, but also can adapt to new concept in a short time. Full article
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Open AccessArticle Fractional-Order Closed-Loop Model Reference Adaptive Control for Anesthesia
Algorithms 2018, 11(7), 106; https://doi.org/10.3390/a11070106
Received: 15 May 2018 / Revised: 7 July 2018 / Accepted: 12 July 2018 / Published: 14 July 2018
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Abstract
The design of a fractional-order closed-loop model reference adaptive control (FOCMRAC) for anesthesia based on a fractional-order model (FOM) is proposed in the paper. This proposed model gets around many difficulties, namely, unknown parameters, lack of state measurement, inter and intra-patient variability, and
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The design of a fractional-order closed-loop model reference adaptive control (FOCMRAC) for anesthesia based on a fractional-order model (FOM) is proposed in the paper. This proposed model gets around many difficulties, namely, unknown parameters, lack of state measurement, inter and intra-patient variability, and variable time-delay, encountered in controller designs based on the PK/PD model commonly used for control of anesthesia, and allows to design a simple adaptive controller based on the Lyapunov analysis. Simulations illustrate the effectiveness and robustness of the proposed control. Full article
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Open AccessArticle Distributed Combinatorial Maps for Parallel Mesh Processing
Algorithms 2018, 11(7), 105; https://doi.org/10.3390/a11070105
Received: 31 May 2018 / Revised: 9 July 2018 / Accepted: 9 July 2018 / Published: 13 July 2018
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Abstract
We propose a new strategy for the parallelization of mesh processing algorithms. Our main contribution is the definition of distributed combinatorial maps (called n-dmaps), which allow us to represent the topology of big meshes by splitting them into independent parts. Our mathematical
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We propose a new strategy for the parallelization of mesh processing algorithms. Our main contribution is the definition of distributed combinatorial maps (called n-dmaps), which allow us to represent the topology of big meshes by splitting them into independent parts. Our mathematical definition ensures the global consistency of the meshes at their interfaces. Thus, an n-dmap can be used to represent a mesh, to traverse it, or to modify it by using different mesh processing algorithms. Moreover, an nD mesh with a huge number of elements can be considered, which is not possible with a sequential approach and a regular data structure. We illustrate the interest of our solution by presenting a parallel adaptive subdivision method of a 3D hexahedral mesh, implemented in a distributed version. We report space and time performance results that show the interest of our approach for parallel processing of huge meshes. Full article
(This article belongs to the Special Issue Efficient Data Structures)
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Open AccessArticle The Gradient and the Hessian of the Distance between Point and Triangle in 3D
Algorithms 2018, 11(7), 104; https://doi.org/10.3390/a11070104
Received: 5 June 2018 / Revised: 2 July 2018 / Accepted: 10 July 2018 / Published: 12 July 2018
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Abstract
Computation of the distance between point and triangle in 3D is a common task in numerical analysis. The input values of the algorithm are coordinates of three points of the triangle and one point from which the distance is determined. An existing algorithm
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Computation of the distance between point and triangle in 3D is a common task in numerical analysis. The input values of the algorithm are coordinates of three points of the triangle and one point from which the distance is determined. An existing algorithm is extended to compute the gradient and the Hessian of that distance with respect to coordinates of involved points. Derivation of exact expressions for gradient and Hessian is presented, and numerical accuracy is evaluated for various cases. The algorithm has O(1) time and space complexity. The included open-source code may be used in applications where derivatives of point-triangle distance are required. Full article
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Open AccessArticle Solutions to the Sub-Optimality and Stability Issues of Recursive Pole and Zero Distribution Algorithms for the Approximation of Fractional Order Models
Algorithms 2018, 11(7), 103; https://doi.org/10.3390/a11070103
Received: 31 May 2018 / Revised: 26 June 2018 / Accepted: 28 June 2018 / Published: 12 July 2018
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Abstract
This paper analyses algorithms currently found in the literature for the approximation of fractional order models and based on recursive pole and zero distributions. The analysis focuses on the sub-optimality of the approximations obtained and stability issues that may appear after approximation depending
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This paper analyses algorithms currently found in the literature for the approximation of fractional order models and based on recursive pole and zero distributions. The analysis focuses on the sub-optimality of the approximations obtained and stability issues that may appear after approximation depending on the pole location of the initial fractional order model. Solutions are proposed to reduce this sub-optimality and to avoid stability issues. Full article
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Open AccessEditorial Advanced Artificial Neural Networks
Algorithms 2018, 11(7), 102; https://doi.org/10.3390/a11070102
Received: 4 July 2018 / Accepted: 7 July 2018 / Published: 10 July 2018
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Abstract
Artificial neural networks (ANNs) have been extensively applied to a wide range of disciplines, such as system identification and control, decision making, pattern recognition, medical diagnosis, finance, data mining, visualization, and others. With advances in computing and networking technologies, more complicated forms of
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Artificial neural networks (ANNs) have been extensively applied to a wide range of disciplines, such as system identification and control, decision making, pattern recognition, medical diagnosis, finance, data mining, visualization, and others. With advances in computing and networking technologies, more complicated forms of ANNs are expected to emerge, requiring the design of advanced learning algorithms. This Special Issue is intended to provide technical details of the construction and training of advanced ANNs. Full article
(This article belongs to the Special Issue Advanced Artificial Neural Networks)
Open AccessArticle Robust Fuzzy Adaptive Sliding Mode Stabilization for Fractional-Order Chaos
Algorithms 2018, 11(7), 101; https://doi.org/10.3390/a11070101
Received: 1 June 2018 / Revised: 3 July 2018 / Accepted: 5 July 2018 / Published: 7 July 2018
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Abstract
In this paper, a new adaptive fuzzy sliding mode control (AFSMC) design strategy is proposed for the control of a special class of three-dimensional fractional order chaotic systems with uncertainties and external disturbance. The design methodology is developed in two stages: first, an
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In this paper, a new adaptive fuzzy sliding mode control (AFSMC) design strategy is proposed for the control of a special class of three-dimensional fractional order chaotic systems with uncertainties and external disturbance. The design methodology is developed in two stages: first, an adaptive sliding mode control law is proposed for the class of fractional order chaotic systems without uncertainties, and then a fuzzy logic system is used to estimate the control compensation effort to be added in the case of uncertainties on the system’s model. Based on the Lyapunov theory, the stability analysis of both control laws is provided with elimination of the chattering action in the control signal. The developed control scheme is simple to implement and the overall control scheme guarantees the global asymptotic stability in the Lyapunov sense if all the involved signals are uniformly bounded. In the present work, simulation studies on fractional-order Chen chaotic systems are carried out to show the efficiency of the proposed fractional adaptive controllers. Full article
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Open AccessArticle A Low Complexity Reactive Tabu Search Based Constellation Constraints in Signal Detection
Algorithms 2018, 11(7), 99; https://doi.org/10.3390/a11070099
Received: 12 May 2018 / Revised: 13 June 2018 / Accepted: 28 June 2018 / Published: 3 July 2018
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Abstract
For Massive multiple-input multiple output (MIMO) systems, many algorithms have been proposed for detecting spatially multiplexed signals, such as reactive tabu search (RTS), minimum mean square error (MMSE), etc. As a heuristic neighborhood search algorithm, RTS is particularly suitable for signal detection in
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For Massive multiple-input multiple output (MIMO) systems, many algorithms have been proposed for detecting spatially multiplexed signals, such as reactive tabu search (RTS), minimum mean square error (MMSE), etc. As a heuristic neighborhood search algorithm, RTS is particularly suitable for signal detection in systems with large number of antennas. In this paper, we propose a strategy to reduce the neighborhood searching space of the traditional RTS algorithms. For this, we introduce a constellation constraints (CC) structure to determine whether including a candidate vector into the RTS searching neighborhood. By setting a pre-defined threshold on the symbol constellation, the Euclidean distance between the estimated signal and its nearest constellation points are calculated, and the threshold and distance are compared to separate the reliable estimated signal from unreliable ones. With this structure, the proposed CC-RTS algorithm may ignore a significant number of unnecessary candidates in the RTS neighborhood searching space and greatly reduce the computational complexity of the traditional RTS algorithm. Simulation results show that the BER performance of the proposed CC-RTS algorithm is very close to that of the traditional RTS algorithm, and with about 50% complexity reduction with the same signal-to-noise (SNR) ratio. Full article
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Open AccessArticle A Self-Adaptive Evolutionary Algorithm for the Berth Scheduling Problem: Towards Efficient Parameter Control
Algorithms 2018, 11(7), 100; https://doi.org/10.3390/a11070100
Received: 5 May 2018 / Revised: 25 June 2018 / Accepted: 28 June 2018 / Published: 3 July 2018
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Abstract
Since ancient times, maritime transportation has played a very important role for the global trade and economy of many countries. The volumes of all major types of cargo, which are transported by vessels, has substantially increased in recent years. Considering a rapid growth
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Since ancient times, maritime transportation has played a very important role for the global trade and economy of many countries. The volumes of all major types of cargo, which are transported by vessels, has substantially increased in recent years. Considering a rapid growth of waterborne trade, marine container terminal operators should focus on upgrading the existing terminal infrastructure and improving operations planning. This study aims to assist marine container terminal operators with improving the seaside operations and primarily focuses on the berth scheduling problem. The problem is formulated as a mixed integer linear programming model, minimizing the total weighted vessel turnaround time and the total weighted vessel late departures. A self-adaptive Evolutionary Algorithm is proposed to solve the problem, where the crossover and mutation probabilities are encoded in the chromosomes. Numerical experiments are conducted to evaluate performance of the developed solution algorithm against the alternative Evolutionary Algorithms, which rely on the deterministic parameter control, adaptive parameter control, and parameter tuning strategies, respectively. Results indicate that all the considered solution algorithms demonstrate a relatively low variability in terms of the objective function values at termination from one replication to another and can maintain the adequate population diversity. However, application of the self-adaptive parameter control strategy substantially improves the objective function values at termination without a significant impact on the computational time. Full article
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Open AccessFeature PaperArticle Width, Depth, and Space: Tradeoffs between Branching and Dynamic Programming
Algorithms 2018, 11(7), 98; https://doi.org/10.3390/a11070098
Received: 31 March 2018 / Revised: 27 June 2018 / Accepted: 28 June 2018 / Published: 1 July 2018
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Abstract
Treedepth is a well-established width measure which has recently seen a resurgence of interest. Since graphs of bounded treedepth are more restricted than graphs of bounded tree- or pathwidth, we are interested in the algorithmic utility of this additional structure. On the negative
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Treedepth is a well-established width measure which has recently seen a resurgence of interest. Since graphs of bounded treedepth are more restricted than graphs of bounded tree- or pathwidth, we are interested in the algorithmic utility of this additional structure. On the negative side, we show with a novel approach that the space consumption of any (single-pass) dynamic programming algorithm on treedepth decompositions of depth d cannot be bounded by (2ϵ)d·logO(1)n for Vertex Cover, (3ϵ)d·logO(1)n for 3-Coloring and (3ϵ)d·logO(1)n for Dominating Set for any ϵ>0. This formalizes the common intuition that dynamic programming algorithms on graph decompositions necessarily consume a lot of space and complements known results of the time-complexity of problems restricted to low-treewidth classes. We then show that treedepth lends itself to the design of branching algorithms. Specifically, we design two novel algorithms for Dominating Set on graphs of treedepth d: A pure branching algorithm that runs in time dO(d2)·n and uses space O(d3logd+dlogn) and a hybrid of branching and dynamic programming that achieves a running time of O(3dlogd·n) while using O(2ddlogd+dlogn) space. Full article
(This article belongs to the Special Issue Algorithms for Hard Problems: Approximation and Parameterization)
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Open AccessArticle A Regional Topic Model Using Hybrid Stochastic Variational Gibbs Sampling for Real-Time Video Mining
Algorithms 2018, 11(7), 97; https://doi.org/10.3390/a11070097
Received: 8 May 2018 / Revised: 14 June 2018 / Accepted: 21 June 2018 / Published: 1 July 2018
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Abstract
The events location and real-time computational performance of crowd scenes continuously challenge the field of video mining. In this paper, we address these two problems based on a regional topic model. In the process of video topic modeling, region topic model can simultaneously
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The events location and real-time computational performance of crowd scenes continuously challenge the field of video mining. In this paper, we address these two problems based on a regional topic model. In the process of video topic modeling, region topic model can simultaneously cluster motion words of video into motion topics, and the locations of motion into motion regions, where each motion topic associates with its region. Meanwhile, a hybrid stochastic variational Gibbs sampling algorithm is developed for inference of our region topic model, which has the ability of inferring in real time with massive video stream dataset. We evaluate our method on simulate and real datasets. The comparison with the Gibbs sampling algorithm shows the superiorities of proposed model and its online inference algorithm in terms of anomaly detection. Full article
(This article belongs to the Special Issue Discrete Algorithms and Discrete Problems in Machine Intelligence)
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Open AccessArticle Solving Multi-Document Summarization as an Orienteering Problem
Algorithms 2018, 11(7), 96; https://doi.org/10.3390/a11070096
Received: 4 June 2018 / Revised: 25 June 2018 / Accepted: 28 June 2018 / Published: 30 June 2018
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Abstract
With advances in information technology, people face the problem of dealing with tremendous amounts of information and need ways to save time and effort by summarizing the most important and relevant information. Thus, automatic text summarization has become necessary to reduce the information
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With advances in information technology, people face the problem of dealing with tremendous amounts of information and need ways to save time and effort by summarizing the most important and relevant information. Thus, automatic text summarization has become necessary to reduce the information overload. This article proposes a novel extractive graph-based approach to solve the multi-document summarization (MDS) problem. To optimize the coverage of information in the output summary, the problem is formulated as an orienteering problem and heuristically solved by an ant colony system algorithm. The performance of the implemented system (MDS-OP) was evaluated on DUC 2004 (Task 2) and MultiLing 2015 (MMS task) benchmark corpora using several ROUGE metrics, as well as other methods. Its comparison with the performances of 26 systems shows that MDS-OP achieved the best F-measure scores on both tasks in terms of ROUGE-1 and ROUGE-L (DUC 2004), ROUGE-SU4, and three other evaluation methods (MultiLing 2015). Overall, MDS-OP ranked among the best 3 systems. Full article
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Open AccessFeature PaperArticle Experimental Validation of a Novel Auto-Tuning Method for a Fractional Order PI Controller on an UR10 Robot
Algorithms 2018, 11(7), 95; https://doi.org/10.3390/a11070095
Received: 15 May 2018 / Revised: 6 June 2018 / Accepted: 28 June 2018 / Published: 30 June 2018
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Abstract
Classical fractional order controller tuning techniques usually consider the frequency domain specifications (phase margin, gain crossover frequency, iso-damping) and are based on knowledge of a process model, as well as solving a system of nonlinear equations to determine the controller parameters. In this
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Classical fractional order controller tuning techniques usually consider the frequency domain specifications (phase margin, gain crossover frequency, iso-damping) and are based on knowledge of a process model, as well as solving a system of nonlinear equations to determine the controller parameters. In this paper, a novel auto-tuning method is used to tune a fractional order PI controller. The advantages of the proposed auto-tuning method are two-fold: There is no need for a process model, neither to solve the system of nonlinear equations. The tuning is based on defining a forbidden region in the Nyquist plane using the phase margin requirement and determining the parameters of the fractional order controller such that the loop frequency response remains out of the forbidden region. Additionally, the final controller parameters are those that minimize the difference between the slope of the loop frequency response and the slope of the forbidden region border, to ensure the iso-damping property. To validate the proposed method, a case study has been used consisting of a pick and place movement of an UR10 robot. The experimental results, considering two different robot configurations, demonstrate that the designed fractional order PI controller is indeed robust. Full article
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Open AccessArticle Tensor Completion Based on Triple Tubal Nuclear Norm
Algorithms 2018, 11(7), 94; https://doi.org/10.3390/a11070094
Received: 21 May 2018 / Revised: 16 June 2018 / Accepted: 19 June 2018 / Published: 28 June 2018
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Abstract
Many tasks in computer vision suffer from missing values in tensor data, i.e., multi-way data array. The recently proposed tensor tubal nuclear norm (TNN) has shown superiority in imputing missing values in 3D visual data, like color images and videos. However, by interpreting
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Many tasks in computer vision suffer from missing values in tensor data, i.e., multi-way data array. The recently proposed tensor tubal nuclear norm (TNN) has shown superiority in imputing missing values in 3D visual data, like color images and videos. However, by interpreting in a circulant way, TNN only exploits tube (often carrying temporal/channel information) redundancy in a circulant way while preserving the row and column (often carrying spatial information) relationship. In this paper, a new tensor norm named the triple tubal nuclear norm (TriTNN) is proposed to simultaneously exploit tube, row and column redundancy in a circulant way by using a weighted sum of three TNNs. Thus, more spatial-temporal information can be mined. Further, a TriTNN-based tensor completion model with an ADMM solver is developed. Experiments on color images, videos and LiDAR datasets show the superiority of the proposed TriTNN against state-of-the-art nuclear norm-based tensor norms. Full article
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Open AccessArticle Layered Graphs: Applications and Algorithms
Algorithms 2018, 11(7), 93; https://doi.org/10.3390/a11070093
Received: 4 June 2018 / Revised: 17 June 2018 / Accepted: 21 June 2018 / Published: 28 June 2018
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Abstract
The computation of distances between strings has applications in molecular biology, music theory and pattern recognition. One such measure, called short reversal distance, has applications in evolutionary distance computation. It has been shown that this problem can be reduced to the computation of
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The computation of distances between strings has applications in molecular biology, music theory and pattern recognition. One such measure, called short reversal distance, has applications in evolutionary distance computation. It has been shown that this problem can be reduced to the computation of a maximum independent set on the corresponding graph that is constructed from the given input strings. The constructed graphs primarily fall into a class that we call layered graphs. In a layered graph, each layer refers to a subgraph containing, at most, some k vertices. The inter-layer edges are restricted to the vertices in adjacent layers. We study the MIS, MVC, MDS, MCV and MCD problems on layered graphs where MIS computes the maximum independent set; MVC computes the minimum vertex cover; MDS computes the minimum dominating set; MCV computes the minimum connected vertex cover; and MCD computes the minimum connected dominating set. MIS, MVC and MDS run in polynomial time if k=Θ(log|V|). MCV and MCD run in polynomial time ifk=O((log|V|)α), where α<1. If k=Θ((log|V|)1+ϵ), for ϵ>0, then MIS, MVC and MDS run in quasi-polynomial time. If k=Θ(log|V|), then MCV and MCD run in quasi-polynomial time. Full article
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Open AccessArticle Predictive Current Control of Boost Three-Level and T-Type Inverters Cascaded in Wind Power Generation Systems
Algorithms 2018, 11(7), 92; https://doi.org/10.3390/a11070092
Received: 7 May 2018 / Revised: 14 June 2018 / Accepted: 25 June 2018 / Published: 27 June 2018
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Abstract
A topology structure based on boost three-level converters (BTL converters) and T-type three-level inverters for a direct-drive wind turbine in a wind power generation system is proposed. In this structure, the generator-side control can be realized by the boost-TL converter. Compared with the
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A topology structure based on boost three-level converters (BTL converters) and T-type three-level inverters for a direct-drive wind turbine in a wind power generation system is proposed. In this structure, the generator-side control can be realized by the boost-TL converter. Compared with the conventional boost converter, the boost-TL converter has a low inductor current ripple, which reduces the torque ripple of the generator, increases the converter’s capacity, and minimizes switching losses. The boost-TL converter can boost the DC output from the rectifier at low speeds. The principles of the boost-TL converter and the T-type three-level inverter are separately introduced. Based on the cascaded structure of the proposed BTL converter and three-level inverter, a model predictive current control (MPCC) method is adopted, and the optimization of the MPCC is presented. The prediction model is derived, and the simulation and experimental research are carried out. The results show that the algorithm based on the proposed cascaded structure is feasible and superior. Full article
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Open AccessArticle emgr—The Empirical Gramian Framework
Algorithms 2018, 11(7), 91; https://doi.org/10.3390/a11070091
Received: 28 May 2018 / Revised: 21 June 2018 / Accepted: 24 June 2018 / Published: 26 June 2018
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Abstract
System Gramian matrices are a well-known encoding for properties of input-output systems such as controllability, observability or minimality. These so-called system Gramians were developed in linear system theory for applications such as model order reduction of control systems. Empirical Gramians are an extension
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System Gramian matrices are a well-known encoding for properties of input-output systems such as controllability, observability or minimality. These so-called system Gramians were developed in linear system theory for applications such as model order reduction of control systems. Empirical Gramians are an extension to the system Gramians for parametric and nonlinear systems as well as a data-driven method of computation. The empirical Gramian framework - emgr - implements the empirical Gramians in a uniform and configurable manner, with applications such as Gramian-based (nonlinear) model reduction, decentralized control, sensitivity analysis, parameter identification and combined state and parameter reduction. Full article
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Open AccessFeature PaperArticle A Novel Method for Control Performance Assessment with Fractional Order Signal Processing and Its Application to Semiconductor Manufacturing
Algorithms 2018, 11(7), 90; https://doi.org/10.3390/a11070090
Received: 15 May 2018 / Revised: 21 June 2018 / Accepted: 24 June 2018 / Published: 26 June 2018
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Abstract
The significant task for control performance assessment (CPA) is to review and evaluate the performance of the control system. The control system in the semiconductor industry exhibits a complex dynamic behavior, which is hard to analyze. This paper investigates the interesting crossover properties
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The significant task for control performance assessment (CPA) is to review and evaluate the performance of the control system. The control system in the semiconductor industry exhibits a complex dynamic behavior, which is hard to analyze. This paper investigates the interesting crossover properties of Hurst exponent estimations and proposes a novel method for feature extraction of the nonlinear multi-input multi-output (MIMO) systems. At first, coupled data from real industry are analyzed by multifractal detrended fluctuation analysis (MFDFA) and the resultant multifractal spectrum is obtained. Secondly, the crossover points with spline fit in the scale-law curve are located and then employed to segment the entire scale-law curve into several different scaling regions, in which a single Hurst exponent can be estimated. Thirdly, to further ascertain the origin of the multifractality of control signals, the generalized Hurst exponents of the original series are compared with shuffled data. At last, non-Gaussian statistical properties, multifractal properties and Hurst exponents of the process control variables are derived and compared with different sets of tuning parameters. The results have shown that CPA of the MIMO system can be better employed with the help of fractional order signal processing (FOSP). Full article
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Open AccessArticle Degradation Trend Prediction for Rotating Machinery Using Long-Range Dependence and Particle Filter Approach
Algorithms 2018, 11(7), 89; https://doi.org/10.3390/a11070089
Received: 29 April 2018 / Revised: 6 June 2018 / Accepted: 14 June 2018 / Published: 26 June 2018
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Abstract
Timely maintenance and accurate fault prediction of rotating machinery are essential for ensuring system availability, minimizing downtime, and contributing to sustainable production. This paper proposes a novel approach based on long-range dependence (LRD) and particle filter (PF) for degradation trend prediction of rotating
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Timely maintenance and accurate fault prediction of rotating machinery are essential for ensuring system availability, minimizing downtime, and contributing to sustainable production. This paper proposes a novel approach based on long-range dependence (LRD) and particle filter (PF) for degradation trend prediction of rotating machinery, taking the rolling bearing as an example. In this work, the degradation prediction is evaluated based on two health indicators time series; i.e., equivalent vibration severity (EVI) time series and kurtosis time series. Specifically, the degradation trend prediction issues here addressed have the following two distinctive features: (i) EVI time series with weak LRD property and (ii) kurtosis time series with sharp transition points (STPs) in the forecasted region. The core idea is that the parameters distribution of the LRD model can be updated recursively by the particle filter algorithm; i.e., the parameters degradation of the LRD model are restrained, and thus the prognostic results could be generated real-time, wherein the initial LRD model is designed randomly. The prediction results demonstrate that the significant improvements in prediction accuracy are obtained with the proposed method compared to some state-of-the-art approaches such as the autoregressive–moving-average (ARMA) model and the fractional order characteristic (FOC) model, etc. Full article
(This article belongs to the Special Issue Stochastic Optimization: Algorithms and Applications)
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