Algorithms, Volume 12, Issue 6 (June 2019) – 13 articles

We present two modifications of Duval’s algorithm for computing the Lyndon factorization of a string. One of the algorithms has been designed for strings containing runs of the smallest character. It works best for small alphabets and it is able to skip a significant number of characters of the string. Moreover, it can be engineered to have linear time complexity in the worst case. When there is a run-length encoded string R of length $\rho$ , the other algorithm computes the Lyndon factorization of R in $O\left(\rho \right)$ time and in constant space. It is shown by experimental results that the new variations are faster than Duval’s original algorithm in many scenarios. Full article

As of today, bioinformatics is one of the most exciting fields of scientific research. There is a wide-ranging list of challenging problems to face, i.e., pairwise and multiple alignments, motif detection/discrimination/classification, phylogenetic tree reconstruction, protein secondary and tertiary structure prediction, protein function prediction, DNA microarray analysis, gene regulation/regulatory networks, just to mention a few, and an army of researchers, coming from several scientific backgrounds, focus their efforts on developing models to properly address these problems. In this paper, we aim to briefly review some of the huge amount of machine learning methods, developed in the last two decades, suited for the analysis of gene microarray data that have a strong impact on molecular biology. In particular, we focus on the wide-ranging list of data clustering and visualization techniques able to find homogeneous data groupings, and also provide the possibility to discover its connections in terms of structure, function and evolution. Full article

Image clustering involves the process of mapping an archive image into a cluster such that the set of clusters has the same information. It is an important field of machine learning and computer vision. While traditional clustering methods, such as k-means or the agglomerative clustering method, have been widely used for the task of clustering, it is difficult for them to handle image data due to having no predefined distance metrics and high dimensionality. Recently, deep unsupervised feature learning methods, such as the autoencoder (AE), have been employed for image clustering with great success. However, each model has its specialty and advantages for image clustering. Hence, we combine three AE-based models—the convolutional autoencoder (CAE), adversarial autoencoder (AAE), and stacked autoencoder (SAE)—to form a hybrid autoencoder (BAE) model for image clustering. The MNIST and CIFAR-10 datasets are used to test the result of the proposed models and compare the results with others. The results of the clustering criteria indicate that the proposed models outperform others in the numerical experiment. Full article

This work suggests a solution for the output reference model (ORM) tracking control problem, based on approximate dynamic programming. General nonlinear systems are included in a control system (CS) and subjected to state feedback. By linear ORM selection, indirect CS feedback linearization is obtained, leading to favorable linear behavior of the CS. The Value Iteration (VI) algorithm ensures model-free nonlinear state feedback controller learning, without relying on the process dynamics. From linear to nonlinear parameterizations, a reliable approximate VI implementation in continuous state-action spaces depends on several key parameters such as problem dimension, exploration of the state-action space, the state-transitions dataset size, and a suitable selection of the function approximators. Herein, we find that, given a transition sample dataset and a general linear parameterization of the Q-function, the ORM tracking performance obtained with an approximate VI scheme can reach the performance level of a more general implementation using neural networks (NNs). Although the NN-based implementation takes more time to learn due to its higher complexity (more parameters), it is less sensitive to exploration settings, number of transition samples, and to the selected hyper-parameters, hence it is recommending as the de facto practical implementation. Contributions of this work include the following: VI convergence is guaranteed under general function approximators; a case study for a low-order linear system in order to generalize the more complex ORM tracking validation on a real-world nonlinear multivariable aerodynamic process; comparisons with an offline deep deterministic policy gradient solution; implementation details and further discussions on the obtained results. Full article

Production planning and scheduling are important bases for production decisions. Concerning the traditional modeling of production planning and scheduling based on Resource-Task Network (RTN) representation, uncertain factors such as utilities are rarely considered as constraints. For the production planning and scheduling problem based on RTN representation in an uncertain environment, this paper formulates the multi-period bi-level integrated model of planning and scheduling, and introduces the uncertainties of demand and utility in planning and scheduling layers respectively. Rolling horizon optimization strategy is utilized to solve the bi-level integrated model iteratively. The simulation results show that the proposed model and algorithm are feasible and effective, can calculate the consumption of utility in every period, decrease the effects of uncertain factors on optimization results, more accurately describe the uncertain factors, and reflect the actual production process. Full article

Blast-resistant buildings are mainly used to protect main instruments, controllers, expensive equipment, and people from explosion waves. Oil and gas industry projects almost always include blast-resistant buildings. For instance, based on a hazard identification (HAZID) and hazard and operability (HAZOP) analysis of a plant, control rooms and substations are sometimes designed to withstand an external free air explosion that generates blast over pressure. In this regard, a building façade is considered to be the first barrier of resistance against explosion waves, and therefore a building façade has an important role in reducing a building’s vulnerability and human casualties. In case of a lack of enough resistance, explosion waves enter a building and bring about irreparable damage to the building. Consequently, it seems important to study and evaluate various materials used in a façade against the consequences of an explosion. This study tried to make a comparison between different types of building facades against explosion waves. The materials used in a building play a key role in the vulnerability of a building. In this research, a literature review and the fuzzy Delphi method were applied to find the most critical criteria, and then a fuzzy evaluation based on the distance from the average solution (EDAS) was applied in order to assess various materials used in building facades from the perspective of resiliency. A questionnaire was presented to measure effective indices in order to receive experts’ ideas. Finally, by implementing this methodology in a case study, it was concluded that a stone façade performs much better against explosions. Full article

The interpretation of Myocardial Infarction (MI) via electrocardiogram (ECG) signal is a challenging task. ECG signals’ morphological view show significant variation in different patients under different physical conditions. Several learning algorithms have been studied to interpret MI. However, the drawback of machine learning is the use of heuristic features with shallow feature learning architectures. To overcome this problem, a deep learning approach is used for learning features automatically, without conventional handcrafted features. This paper presents sequence modeling based on deep learning with recurrent network for ECG-rhythm signal classification. The recurrent network architecture such as a Recurrent Neural Network (RNN) is proposed to automatically interpret MI via ECG signal. The performance of the proposed method is compared to the other recurrent network classifiers such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The objective is to obtain the best sequence model for ECG signal processing. This paper also aims to study a proper data partitioning ratio for the training and testing sets of imbalanced data. The large imbalanced data are obtained from MI and healthy control of PhysioNet: The PTB Diagnostic ECG Database 15-lead ECG signals. According to the comparison result, the LSTM architecture shows better performance than standard RNN and GRU architecture with identical hyper-parameters. The LSTM architecture also shows better classification compared to standard recurrent networks and GRU with sensitivity, specificity, precision, F1-score, BACC, and MCC is 98.49%, 97.97%, 95.67%, 96.32%, 97.56%, and 95.32%, respectively. Apparently, deep learning with the LSTM technique is a potential method for classifying sequential data that implements time steps in the ECG signal. Full article

In the current paper, an iterative algorithm is developed to simulate the problem of two-phase flow with heat transfer in porous media. The convective body force caused by heat transfer is described by Boussinesq approximation throughout with the governing equations, namely, pressure, saturation, and energy. The two coupled equations of pressure and saturation are solved using the implicit pressure-explicit saturation (IMPES) scheme, while the energy equation is treated implicitly, and the scheme is called iterative implicit pressure, explicit saturation, implicit temperature (I-IMPES-IMT). In order to calculate the pressure implicitly, the equations of pressure and saturation are coupled by linearizing the capillary pressure which is a function of saturation. After that, the equation of saturation is solved explicitly. Then, the velocity is computed which is used in the energy equation to calculate the temperature implicitly. The cell-centered finite difference (CCFD) method is utilized for spatial discretization. Furthermore, a relaxation factor along is used with the Courant–Friedrichs–Lewy (CFL) condition. Finally, in order to illustrate the efficiency of the developed algorithm, error estimates for saturation and temperature for different values of time steps and number of iterations are presented. Moreover, numerical examples of different physical scenarios of heterogamous media are presented. Full article

Nowadays, a variety of data-compressors (or archivers) is available, each of which has its merits, and it is impossible to single out the best ones. Thus, one faces the problem of choosing the best method to compress a given file, and this problem is more important the larger is the file. It seems natural to try all the compressors and then choose the one that gives the shortest compressed file, then transfer (or store) the index number of the best compressor (it requires $logm$ bits, if m is the number of compressors available) and the compressed file. The only problem is the time, which essentially increases due to the need to compress the file m times (in order to find the best compressor). We suggest a method of data compression whose performance is close to optimal, but for which the extra time needed is relatively small: the ratio of this extra time and the total time of calculation can be limited, in an asymptotic manner, by an arbitrary positive constant. In short, the main idea of the suggested approach is as follows: in order to find the best, try all the data compressors, but, when doing so, use for compression only a small part of the file. Then apply the best data compressors to the whole file. Note that there are many situations where it may be necessary to find the best data compressor out of a given set. In such a case, it is often done by comparing compressors empirically. One of the goals of this work is to turn such a selection process into a part of the data compression method, automating and optimizing it. Full article

Background subtraction plays a fundamental role for anomaly detection in video surveillance, which is able to tell where moving objects are in the video scene. Regrettably, the regular rotating pumping unit is treated as an abnormal object by the background-subtraction method in pumping-unit surveillance. As an excellent classifier, a deep convolutional neural network is able to tell what those objects are. Therefore, we combined background subtraction and a convolutional neural network to perform anomaly detection for pumping-unit surveillance. In the proposed method, background subtraction was applied to first extract moving objects. Then, a clustering method was adopted for extracting different object types that had more movement-foreground objects but fewer typical targets. Finally, nonpumping unit objects were identified as abnormal objects by the trained classification network. The experimental results demonstrate that the proposed method can detect abnormal objects in a pumping-unit scene with high accuracy. Full article

The widely known generators of Poisson random variables are associated with different modifications of the algorithm based on the convergence in probability of a sequence of uniform random variables to the created stochastic number. However, in some situations, this approach yields different discrete Poisson probability distributions and skipping in the generated numbers. This article offers a new approach for creating Poisson random variables based on the complete twister generator of uniform random variables, using cumulative frequency technology. The simulation results confirm that probabilistic and frequency distributions of the obtained stochastic numbers completely coincide with the theoretical Poisson distribution. Moreover, combining this new approach with the tuning algorithm of basic twister generation allows for a significant increase in length of the created sequences without using additional RAM of the computer. Full article

In this paper, we develop and numerically illustrate a robust sensor network design to optimally detect a radiation source in an urban environment. This problem exhibits several challenges: penalty functionals are non-smooth due to the presence of buildings, radiation transport models are often computationally expensive, sensor locations are not limited to a discrete number of points, and source intensity and location responses, based on a fixed number of sensors, are not unique. We consider a radiation source located in a prototypical 250 m × 180 m urban setting. To address the non-smooth properties of the model and computationally expensive simulation codes, we employ a verified surrogate model based on radial basis functions. Using this surrogate, we formulate and solve a robust design problem that is optimal in an average sense for detecting source location and intensity with minimized uncertainty. Full article