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Algorithms, Volume 10, Issue 4 (December 2017)

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Open AccessArticle Control-Oriented Models for SO Fuel Cells from the Angle of V&V: Analysis, Simplification Possibilities, Performance
Algorithms 2017, 10(4), 140; https://doi.org/10.3390/a10040140
Received: 23 October 2017 / Revised: 8 December 2017 / Accepted: 11 December 2017 / Published: 18 December 2017
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Abstract
In this paper, we take a look at the analysis and parameter identification for control-oriented, dynamic models for the thermal subsystem of solid oxide fuel cells (SOFC) from the systematized point of view of verification and validation (V&V). First, we give a possible
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In this paper, we take a look at the analysis and parameter identification for control-oriented, dynamic models for the thermal subsystem of solid oxide fuel cells (SOFC) from the systematized point of view of verification and validation (V&V). First, we give a possible classification of models according to their verification degree which depends, for example, on the kind of arithmetic used for both formulation and simulation. Typical SOFC models, consisting of several coupled differential equations for gas preheaters and the temperature distribution in the stack module, do not have analytical solutions because of spatial nonlinearity. Therefore, in the next part of the paper, we describe in detail two possible ways to simplify such models so that the underlying differential equations can be solved analytically while still being sufficiently accurate to serve as the basis for control synthesis. The simplifying assumption is to approximate the heat capacities of the gases by zero-order polynomials (or first-oder polynomials, respectively) in the temperature. In the last, application-oriented part of the paper, we identify the parameters of these models as well as compare their performance and their ability to reflect the reality with the corresponding characteristics of models in which the heat capacities are represented by quadratic polynomials (the usual case). For this purpose, the framework UniVerMeC (Unified Framework for Verified GeoMetric Computations) is used, which allows us to employ different kinds of arithmetics including the interval one. This latter possibility ensures a high level of reliability of simulations and of the subsequent validation. Besides, it helps to take into account bounded uncertainty in measurements. Full article
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Open AccessArticle An EMD–SARIMA-Based Modeling Approach for Air Traffic Forecasting
Algorithms 2017, 10(4), 139; https://doi.org/10.3390/a10040139
Received: 21 September 2017 / Revised: 9 December 2017 / Accepted: 12 December 2017 / Published: 14 December 2017
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Abstract
The ever-increasing air traffic demand in China has brought huge pressure on the planning and management of, and investment in, air terminals as well as airline companies. In this context, accurate and adequate short-term air traffic forecasting is essential for the operations of
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The ever-increasing air traffic demand in China has brought huge pressure on the planning and management of, and investment in, air terminals as well as airline companies. In this context, accurate and adequate short-term air traffic forecasting is essential for the operations of those entities. In consideration of such a problem, a hybrid air traffic forecasting model based on empirical mode decomposition (EMD) and seasonal auto regressive integrated moving average (SARIMA) has been proposed in this paper. The model proposed decomposes the original time series into components at first, and models each component with the SARIMA forecasting model, then integrates all the models together to form the final combined forecast result. By using the monthly air cargo and passenger flow data from the years 2006 to 2014 available at the official website of the Civil Aviation Administration of China (CAAC), the effectiveness in forecasting of the model proposed has been demonstrated, and by a horizontal performance comparison between several other widely used forecasting models, the advantage of the proposed model has also been proved. Full article
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Open AccessArticle A Hierarchical Multi-Label Classification Algorithm for Gene Function Prediction
Algorithms 2017, 10(4), 138; https://doi.org/10.3390/a10040138
Received: 28 September 2017 / Revised: 20 October 2017 / Accepted: 28 November 2017 / Published: 8 December 2017
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Abstract
Gene function prediction is a complicated and challenging hierarchical multi-label classification (HMC) task, in which genes may have many functions at the same time and these functions are organized in a hierarchy. This paper proposed a novel HMC algorithm for solving this problem
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Gene function prediction is a complicated and challenging hierarchical multi-label classification (HMC) task, in which genes may have many functions at the same time and these functions are organized in a hierarchy. This paper proposed a novel HMC algorithm for solving this problem based on the Gene Ontology (GO), the hierarchy of which is a directed acyclic graph (DAG) and is more difficult to tackle. In the proposed algorithm, the HMC task is firstly changed into a set of binary classification tasks. Then, two measures are implemented in the algorithm to enhance the HMC performance by considering the hierarchy structure during the learning procedures. Firstly, negative instances selecting policy associated with the SMOTE approach are proposed to alleviate the imbalanced data set problem. Secondly, a nodes interaction method is introduced to combine the results of binary classifiers. It can guarantee that the predictions are consistent with the hierarchy constraint. The experiments on eight benchmark yeast data sets annotated by the Gene Ontology show the promising performance of the proposed algorithm compared with other state-of-the-art algorithms. Full article
(This article belongs to the Special Issue Bioinformatics Algorithms and Applications)
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Open AccessArticle Weakly Coupled Distributed Calculation of Lyapunov Exponents for Non-Linear Dynamical Systems
Algorithms 2017, 10(4), 137; https://doi.org/10.3390/a10040137
Received: 19 October 2017 / Revised: 10 November 2017 / Accepted: 13 November 2017 / Published: 7 December 2017
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Abstract
Numerical estimation of Lyapunov exponents in non-linear dynamical systems results in a very high computational cost. This is due to the large-scale computational cost of several Runge–Kutta problems that need to be calculated. In this work we introduce a parallel implementation based on
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Numerical estimation of Lyapunov exponents in non-linear dynamical systems results in a very high computational cost. This is due to the large-scale computational cost of several Runge–Kutta problems that need to be calculated. In this work we introduce a parallel implementation based on MPI (Message Passing Interface) for the calculation of the Lyapunov exponents for a multidimensional dynamical system, considering a weakly coupled algorithm. Since we work on an academic high-latency cluster interconnected with a gigabit switch, the design has to be oriented to reduce the number of messages required. With the design introduced in this work, the computing time is drastically reduced, and the obtained performance leads to close to optimal speed-up ratios. The implemented parallelisation allows us to carry out many experiments for the calculation of several Lyapunov exponents with a low-cost cluster. The numerical experiments showed a high scalability, which we showed with up to 68 cores. Full article
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Open AccessArticle Algebraic Dynamic Programming on Trees
Algorithms 2017, 10(4), 135; https://doi.org/10.3390/a10040135
Received: 13 October 2017 / Revised: 1 December 2017 / Accepted: 2 December 2017 / Published: 6 December 2017
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Abstract
Where string grammars describe how to generate and parse strings, tree grammars describe how to generate and parse trees. We show how to extend generalized algebraic dynamic programming to tree grammars. The resulting dynamic programming algorithms are efficient and provide the complete feature
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Where string grammars describe how to generate and parse strings, tree grammars describe how to generate and parse trees. We show how to extend generalized algebraic dynamic programming to tree grammars. The resulting dynamic programming algorithms are efficient and provide the complete feature set available to string grammars, including automatic generation of outside parsers and algebra products for efficient backtracking. The complete parsing infrastructure is available as an embedded domain-specific language in Haskell. In addition to the formal framework, we provide implementations for both tree alignment and tree editing. Both algorithms are in active use in, among others, the area of bioinformatics, where optimization problems on trees are of considerable practical importance. This framework and the accompanying algorithms provide a beneficial starting point for developing complex grammars with tree- and forest-based inputs. Full article
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Open AccessArticle Detecting Composite Functional Module in miRNA Regulation and mRNA Interaction Network
Algorithms 2017, 10(4), 136; https://doi.org/10.3390/a10040136
Received: 17 September 2017 / Revised: 1 December 2017 / Accepted: 2 December 2017 / Published: 5 December 2017
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Abstract
The detection of composite miRNA functional module (CMFM) is of tremendous significance and helps in understanding the organization, regulation and execution of cell processes in cancer, but how to identify functional CMFMs is still a computational challenge. In this paper we propose a
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The detection of composite miRNA functional module (CMFM) is of tremendous significance and helps in understanding the organization, regulation and execution of cell processes in cancer, but how to identify functional CMFMs is still a computational challenge. In this paper we propose a novel module detection method called MBCFM (detecting Composite Function Modules based on Maximal Biclique enumeration), specifically designed to bicluster miRNAs and target messenger RNAs (mRNAs) on the basis of multiple biological interaction information and topical network features. In this method, we employ algorithm MICA to enumerate all maximal bicliques and further extract R-pairs from the miRNA-mRNA regulatory network. Compared with two existing methods, Mirsynergy and SNMNMF on ovarian cancer dataset, the proposed method of MBCFM is not only able to extract cohesiveness-preserved CMFMs but also has high efficiency in running time. More importantly, MBCFM can be applied to detect other cancer-associated miRNA functional modules. Full article
(This article belongs to the Special Issue Bioinformatics Algorithms and Applications)
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Open AccessArticle Improved Integral Inequalities for Stability Analysis of Interval Time-Delay Systems
Algorithms 2017, 10(4), 134; https://doi.org/10.3390/a10040134
Received: 28 October 2017 / Revised: 26 November 2017 / Accepted: 30 November 2017 / Published: 3 December 2017
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Abstract
A novel stability analysis for the interval time-delay systems is proposed by employing a new series of integral inequalities for single and double integrals. Different from the recently introduced Wirtinger-based inequalities, refined Jensen inequalities and auxiliary function-based inequalities, the proposed ones can provide
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A novel stability analysis for the interval time-delay systems is proposed by employing a new series of integral inequalities for single and double integrals. Different from the recently introduced Wirtinger-based inequalities, refined Jensen inequalities and auxiliary function-based inequalities, the proposed ones can provide more accurate bounds for the cross terms in derivatives of the Lyapunov–Krasovskii functional (LKF) without involving additional slack variables. Based on the augmented LKF with triple-integral terms, their applications to stability analysis for interval time-delay systems are provided. By virtue of the newly derived inequalities, the resulting criteria are less conservative than some existing literature. Finally, numerical examples are provided to verify the effectiveness and improvement of the proposed approaches. Full article
Open AccessArticle Neutrosophic Linear Equations and Application in Traffic Flow Problems
Algorithms 2017, 10(4), 133; https://doi.org/10.3390/a10040133
Received: 2 October 2017 / Revised: 28 November 2017 / Accepted: 30 November 2017 / Published: 1 December 2017
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Abstract
A neutrosophic number (NN) presented by Smarandache can express determinate and/or indeterminate information in real life. NN (z = a + uI) consists of the determinate part a and the indeterminate part uI for a, uR (R
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A neutrosophic number (NN) presented by Smarandache can express determinate and/or indeterminate information in real life. NN (z = a + uI) consists of the determinate part a and the indeterminate part uI for a, uR (R is all real numbers) and indeterminacy I, and is very suitable for representing and handling problems with both determinate and indeterminate information. Based on the concept of NNs, this paper presents for first time the concepts of neutrosophic linear equations and the neutrosophic matrix, and introduces the neutrosophic matrix operations. Then, we propose some solving methods, including the substitution method, the addition method, and the inverse matrix method, for the system of neutrosophic linear equations or the neutrosophic matrix equation. Finally, an applied example about a traffic flow problem is provided to illustrate the application and effectiveness of handling the indeterminate traffic flow problem by using the system of neutrosophic linear equations. Full article
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Open AccessArticle Hyperspectral Data: Efficient and Secure Transmission
Algorithms 2017, 10(4), 132; https://doi.org/10.3390/a10040132
Received: 23 October 2017 / Revised: 25 November 2017 / Accepted: 28 November 2017 / Published: 30 November 2017
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Abstract
Airborne and spaceborne hyperspectral sensors collect information which is derived from the electromagnetic spectrum of an observed area. Hyperspectral data are used in several studies and they are an important aid in different real-life applications (e.g., mining and geology applications, ecology, surveillance, etc.).
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Airborne and spaceborne hyperspectral sensors collect information which is derived from the electromagnetic spectrum of an observed area. Hyperspectral data are used in several studies and they are an important aid in different real-life applications (e.g., mining and geology applications, ecology, surveillance, etc.). A hyperspectral image has a three-dimensional structure (a sort of datacube): it can be considered as a sequence of narrow and contiguous spectral channels (bands). The objective of this paper is to present a framework permits the efficient storage/transmission of an input hyperspectral image, and its protection. The proposed framework relies on a reversible invisible watermarking scheme and an efficient lossless compression algorithm. The reversible watermarking scheme is used in conjunction with digital signature techniques in order to permit the verification of the integrity of a hyperspectral image by the receiver. Full article
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Open AccessArticle An Indoor Collaborative Coefficient-Triangle APIT Localization Algorithm
Algorithms 2017, 10(4), 131; https://doi.org/10.3390/a10040131
Received: 23 September 2017 / Revised: 20 November 2017 / Accepted: 22 November 2017 / Published: 28 November 2017
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Abstract
The Approximate Point-In-Triangulation (APIT) localization algorithm is a widely used indoor positioning technology due to its simplicity and low power consumption. However, in practice, In-to-Out misjudgments exist regularly in APIT, and a considerable amount of nodes cannot be positioned due to the low
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The Approximate Point-In-Triangulation (APIT) localization algorithm is a widely used indoor positioning technology due to its simplicity and low power consumption. However, in practice, In-to-Out misjudgments exist regularly in APIT, and a considerable amount of nodes cannot be positioned due to the low node density. To tackle this issue, a Collaborative Coefficient-triangle APIT Localization (CCAL) algorithm is proposed. Firstly, an effective triangle criterion is put forward to reduce the probability of In-to-Out misjudgment and reduce the computational complexity. Then, a further Received Signal Strength Indicator (RSSI) location and weighted triangle coordinate calculation method is adopted to reduce the positioning error. Meanwhile, the idea of iterative collaborative positioning of the positioned unknown nodes is introduced to remarkably expand the localization coverage rate. Simulation results show that the proposed algorithm outperforms APIT, RSSI, and other improved algorithms in terms of both node location error and localization coverage rate. Full article
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Open AccessArticle 2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization
Algorithms 2017, 10(4), 130; https://doi.org/10.3390/a10040130
Received: 17 October 2017 / Revised: 17 November 2017 / Accepted: 23 November 2017 / Published: 28 November 2017
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Abstract
Portfolio optimization is a serious challenge for financial engineering and has pulled down special attention among investors. It has two objectives: to maximize the reward that is calculated by expected return and to minimize the risk. Variance has been considered as a risk
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Portfolio optimization is a serious challenge for financial engineering and has pulled down special attention among investors. It has two objectives: to maximize the reward that is calculated by expected return and to minimize the risk. Variance has been considered as a risk measure. There are many constraints in the world that ultimately lead to a non–convex search space such as cardinality constraint. In conclusion, parametric quadratic programming could not be applied and it seems essential to apply multi-objective evolutionary algorithm (MOEA). In this paper, a new efficient multi-objective portfolio optimization algorithm called 2-phase NSGA II algorithm is developed and the results of this algorithm are compared with the NSGA II algorithm. It was found that 2-phase NSGA II significantly outperformed NSGA II algorithm. Full article
(This article belongs to the Special Issue Evolutionary Computation for Multiobjective Optimization)
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Open AccessArticle Comparative Analysis of Classifiers for Classification of Emergency Braking of Road Motor Vehicles
Algorithms 2017, 10(4), 129; https://doi.org/10.3390/a10040129
Received: 30 September 2017 / Revised: 12 November 2017 / Accepted: 17 November 2017 / Published: 22 November 2017
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Abstract
We investigate the feasibility of classifying (inferring) the emergency braking situations in road vehicles from the motion pattern of the accelerator pedal. We trained and compared several classifiers and employed genetic algorithms to tune their associated hyperparameters. Using offline time series data of
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We investigate the feasibility of classifying (inferring) the emergency braking situations in road vehicles from the motion pattern of the accelerator pedal. We trained and compared several classifiers and employed genetic algorithms to tune their associated hyperparameters. Using offline time series data of the dynamics of the accelerator pedal as the test set, the experimental results suggest that the evolved classifiers detect the emergency braking situation with at least 93% accuracy. The best performing classifier could be integrated into the agent that perceives the dynamics of the accelerator pedal in real time and—if emergency braking is detected—acts by applying full brakes well before the driver would have been able to apply them. Full article
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Open AccessArticle Truss Structure Optimization with Subset Simulation and Augmented Lagrangian Multiplier Method
Algorithms 2017, 10(4), 128; https://doi.org/10.3390/a10040128
Received: 20 August 2017 / Revised: 10 November 2017 / Accepted: 12 November 2017 / Published: 21 November 2017
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Abstract
This paper presents a global optimization method for structural design optimization, which integrates subset simulation optimization (SSO) and the dynamic augmented Lagrangian multiplier method (DALMM). The proposed method formulates the structural design optimization as a series of unconstrained optimization sub-problems using DALMM and
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This paper presents a global optimization method for structural design optimization, which integrates subset simulation optimization (SSO) and the dynamic augmented Lagrangian multiplier method (DALMM). The proposed method formulates the structural design optimization as a series of unconstrained optimization sub-problems using DALMM and makes use of SSO to find the global optimum. The combined strategy guarantees that the proposed method can automatically detect active constraints and provide global optimal solutions with finite penalty parameters. The accuracy and robustness of the proposed method are demonstrated by four classical truss sizing problems. The results are compared with those reported in the literature, and show a remarkable statistical performance based on 30 independent runs. Full article
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Open AccessArticle A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2
Algorithms 2017, 10(4), 127; https://doi.org/10.3390/a10040127
Received: 30 September 2017 / Revised: 10 November 2017 / Accepted: 14 November 2017 / Published: 16 November 2017
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Abstract
Traffic sign detection is an important task in traffic sign recognition systems. Chinese traffic signs have their unique features compared with traffic signs of other countries. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in
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Traffic sign detection is an important task in traffic sign recognition systems. Chinese traffic signs have their unique features compared with traffic signs of other countries. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, we present a Chinese traffic sign detection algorithm based on a deep convolutional network. To achieve real-time Chinese traffic sign detection, we propose an end-to-end convolutional network inspired by YOLOv2. In view of the characteristics of traffic signs, we take the multiple 1 × 1 convolutional layers in intermediate layers of the network and decrease the convolutional layers in top layers to reduce the computational complexity. For effectively detecting small traffic signs, we divide the input images into dense grids to obtain finer feature maps. Moreover, we expand the Chinese traffic sign dataset (CTSD) and improve the marker information, which is available online. All experimental results evaluated according to our expanded CTSD and German Traffic Sign Detection Benchmark (GTSDB) indicate that the proposed method is the faster and more robust. The fastest detection speed achieved was 0.017 s per image. Full article
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Open AccessReview Linked Data for Life Sciences
Algorithms 2017, 10(4), 126; https://doi.org/10.3390/a10040126
Received: 27 September 2017 / Revised: 11 November 2017 / Accepted: 13 November 2017 / Published: 16 November 2017
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Abstract
Massive amounts of data are currently available and being produced at an unprecedented rate in all domains of life sciences worldwide. However, this data is disparately stored and is in different and unstructured formats making it very hard to integrate. In this review,
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Massive amounts of data are currently available and being produced at an unprecedented rate in all domains of life sciences worldwide. However, this data is disparately stored and is in different and unstructured formats making it very hard to integrate. In this review, we examine the state of the art and propose the use of the Linked Data (LD) paradigm, which is a set of best practices for publishing and connecting structured data on the Web in a semantically meaningful format. We argue that utilizing LD in the life sciences will make data sets better Findable, Accessible, Interoperable, and Reusable. We identify three tiers of the research cycle in life sciences, namely (i) systematic review of the existing body of knowledge, (ii) meta-analysis of data, and (iii) knowledge discovery of novel links across different evidence streams to primarily utilize the proposed LD paradigm. Finally, we demonstrate the use of LD in three use case scenarios along the same research question and discuss the future of data/knowledge integration in life sciences and the challenges ahead. Full article
(This article belongs to the Special Issue Algorithmic Methods for Computational Molecular Biology)
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