Algorithms

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2 pages, 150 KiB

Open AccessEditorial

2020 Selected Papers from Algorithms’ Editorial Board Members

by Frank Werner

Algorithms 2021, 14(2), 32; https://doi.org/10.3390/a14020032 - 21 Jan 2021

Cited by 1 | Viewed by 2347

Abstract

This Special Issue of Algorithms is of a different nature than other Special Issue in the journal, which are usually dedicated to a particular subjects in the area of algorithms [...] Full article

(This article belongs to the Special Issue 2020 Selected Papers from Algorithms Editorial Board Members)

Research

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17 pages, 2800 KiB

Open AccessArticle

Sliding Mode Control Algorithms for Anti-Lock Braking Systems with Performance Comparisons

by Emanuel Chereji, Mircea-Bogdan Radac and Alexandra-Iulia Szedlak-Stinean

Algorithms 2021, 14(1), 2; https://doi.org/10.3390/a14010002 - 23 Dec 2020

Cited by 15 | Viewed by 3852

Abstract

This paper presents the performance of two sliding mode control algorithms, based on the Lyapunov-based sliding mode controller (LSMC) and reaching-law-based sliding mode controller (RSMC), with their novel variants designed and applied to the anti-lock braking system (ABS), which is known to be [...] Read more.

This paper presents the performance of two sliding mode control algorithms, based on the Lyapunov-based sliding mode controller (LSMC) and reaching-law-based sliding mode controller (RSMC), with their novel variants designed and applied to the anti-lock braking system (ABS), which is known to be a strongly nonlinear system. The goal is to prove their superior performance over existing control approaches, in the sense that the LSMC and RSMC do not bring additional computational complexity, as they rely on a reduced number of tuning parameters. The performance of LSMC and RSMC solves the uncertainty in the process model which comes from unmodeled dynamics and a simplification of the actuator dynamics, leading to a reduced second order process. The contribution adds complete design details and stability analysis is provided. Additionally, performance comparisons with several adaptive, neural networks-based and model-free sliding mode control algorithms reveal the robustness of the proposed LSMC and RSMC controllers, in spite of the reduced number of tuning parameters. The robustness and reduced computational burden of the controllers validated on the real-world complex ABS make it an attractive solution for practical industrial implementations. Full article

(This article belongs to the Special Issue 2020 Selected Papers from Algorithms Editorial Board Members)

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27 pages, 476 KiB

Open AccessArticle

Finding the Best 3-OPT Move in Subcubic Time

by Giuseppe Lancia and Marcello Dalpasso

Algorithms 2020, 13(11), 306; https://doi.org/10.3390/a13110306 - 23 Nov 2020

Cited by 3 | Viewed by 3356

Abstract

Given a Traveling Salesman Problem solution, the best 3-OPT move requires us to remove three edges and replace them with three new ones so as to shorten the tour as much as possible. No worst-case algorithm better than the [...] Read more.

Given a Traveling Salesman Problem solution, the best 3-OPT move requires us to remove three edges and replace them with three new ones so as to shorten the tour as much as possible. No worst-case algorithm better than the

Θ (n^{3})

enumeration of all triples is likely to exist for this problem, but algorithms with average case

O (n^{3 - ϵ})

are not ruled out. In this paper we describe a strategy for 3-OPT optimization which can find the best move by looking only at a fraction of all possible moves. We extend our approach also to some other types of cubic moves, such as some special 6-OPT and 5-OPT moves. Empirical evidence shows that our algorithm runs in average subcubic time (upper bounded by

O (n^{2.5})

) on a wide class of random graphs as well as Traveling Salesman Problem Library (TSPLIB) instances. Full article

(This article belongs to the Special Issue 2020 Selected Papers from Algorithms Editorial Board Members)

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18 pages, 6404 KiB

Open AccessArticle

Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy

by Tom Burr, Andrea Favalli, Marcie Lombardi and Jacob Stinnett

Algorithms 2020, 13(10), 265; https://doi.org/10.3390/a13100265 - 19 Oct 2020

Cited by 7 | Viewed by 3497

Abstract

Radioisotope identification (RIID) algorithms for gamma-ray spectroscopy aim to infer what isotopes are present and in what amounts in test items. RIID algorithms either use all energy channels in the analysis region or only energy channels in and near identified peaks. Because many [...] Read more.

Radioisotope identification (RIID) algorithms for gamma-ray spectroscopy aim to infer what isotopes are present and in what amounts in test items. RIID algorithms either use all energy channels in the analysis region or only energy channels in and near identified peaks. Because many RIID algorithms rely on locating peaks and estimating each peak’s net area, peak location and peak area estimation algorithms continue to be developed for gamma-ray spectroscopy. This paper shows that approximate Bayesian computation (ABC) can be effective for peak location and area estimation. Algorithms to locate peaks can be applied to raw or smoothed data, and among several smoothing options, the iterative bias reduction algorithm (IBR) is recommended; the use of IBR with ABC is shown to potentially reduce uncertainty in peak location estimation. Extracted peak locations and areas can then be used as summary statistics in a new ABC-based RIID. ABC allows for easy experimentation with candidate summary statistics such as goodness-of-fit scores and peak areas that are extracted from relatively high dimensional gamma spectra with photopeaks (1024 or more energy channels) consisting of count rates versus energy for a large number of gamma energies. Full article

(This article belongs to the Special Issue 2020 Selected Papers from Algorithms Editorial Board Members)

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17 pages, 374 KiB

Open AccessArticle

CYK Parsing over Distributed Representations

by Fabio Massimo Zanzotto, Giorgio Satta and Giordano Cristini

Algorithms 2020, 13(10), 262; https://doi.org/10.3390/a13100262 - 15 Oct 2020

Cited by 2 | Viewed by 3923

Abstract

Parsing is a key task in computer science, with applications in compilers, natural language processing, syntactic pattern matching, and formal language theory. With the recent development of deep learning techniques, several artificial intelligence applications, especially in natural language processing, have combined traditional parsing [...] Read more.

Parsing is a key task in computer science, with applications in compilers, natural language processing, syntactic pattern matching, and formal language theory. With the recent development of deep learning techniques, several artificial intelligence applications, especially in natural language processing, have combined traditional parsing methods with neural networks to drive the search in the parsing space, resulting in hybrid architectures using both symbolic and distributed representations. In this article, we show that existing symbolic parsing algorithms for context-free languages can cross the border and be entirely formulated over distributed representations. To this end, we introduce a version of the traditional Cocke–Younger–Kasami (CYK) algorithm, called distributed (D)-CYK, which is entirely defined over distributed representations. D-CYK uses matrix multiplication on real number matrices of a size independent of the length of the input string. These operations are compatible with recurrent neural networks. Preliminary experiments show that D-CYK approximates the original CYK algorithm. By showing that CYK can be entirely performed on distributed representations, we open the way to the definition of recurrent layer neural networks that can process general context-free languages. Full article

(This article belongs to the Special Issue 2020 Selected Papers from Algorithms Editorial Board Members)

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14 pages, 1064 KiB

Open AccessArticle

Spatially Adaptive Regularization in Image Segmentation

by Laura Antonelli, Valentina De Simone and Daniela di Serafino

Algorithms 2020, 13(9), 226; https://doi.org/10.3390/a13090226 - 8 Sep 2020

Cited by 11 | Viewed by 2847

Abstract

We present a total-variation-regularized image segmentation model that uses local regularization parameters to take into account spatial image information. We propose some techniques for defining those parameters, based on the cartoon-texture decomposition of the given image, on the mean and median filters, and [...] Read more.

We present a total-variation-regularized image segmentation model that uses local regularization parameters to take into account spatial image information. We propose some techniques for defining those parameters, based on the cartoon-texture decomposition of the given image, on the mean and median filters, and on a thresholding technique, with the aim of preventing excessive regularization in piecewise-constant or smooth regions and preserving spatial features in nonsmooth regions. Our model is obtained by modifying a well-known image segmentation model that was developed by T. Chan, S. Esedoḡlu, and M. Nikolova. We solve the modified model by an alternating minimization method using split Bregman iterations. Numerical experiments show the effectiveness of our approach. Full article

(This article belongs to the Special Issue 2020 Selected Papers from Algorithms Editorial Board Members)

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16 pages, 297 KiB

Open AccessArticle

A Brief Survey of Fixed-Parameter Parallelism

by Faisal N. Abu-Khzam and Karam Al Kontar

Algorithms 2020, 13(8), 197; https://doi.org/10.3390/a13080197 - 14 Aug 2020

Cited by 3 | Viewed by 2811

Abstract

This paper provides an overview of the field of parameterized parallel complexity by surveying previous work in addition to presenting a few new observations and exploring potential new directions. In particular, we present a general view of how known

F P T

techniques, [...] Read more.

This paper provides an overview of the field of parameterized parallel complexity by surveying previous work in addition to presenting a few new observations and exploring potential new directions. In particular, we present a general view of how known

F P T

techniques, such as bounded search trees, color coding, kernelization, and iterative compression, can be modified to produce fixed-parameter parallel algorithms. Full article

(This article belongs to the Special Issue 2020 Selected Papers from Algorithms Editorial Board Members)

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10 pages, 499 KiB

Open AccessArticle

Compression of Next-Generation Sequencing Data and of DNA Digital Files

by Bruno Carpentieri

Algorithms 2020, 13(6), 151; https://doi.org/10.3390/a13060151 - 24 Jun 2020

Cited by 4 | Viewed by 3371

Abstract

The increase in memory and in network traffic used and caused by new sequenced biological data has recently deeply grown. Genomic projects such as HapMap and 1000 Genomes have contributed to the very large rise of databases and network traffic related to genomic [...] Read more.

The increase in memory and in network traffic used and caused by new sequenced biological data has recently deeply grown. Genomic projects such as HapMap and 1000 Genomes have contributed to the very large rise of databases and network traffic related to genomic data and to the development of new efficient technologies. The large-scale sequencing of samples of DNA has brought new attention and produced new research, and thus the interest in the scientific community for genomic data has greatly increased. In a very short time, researchers have developed hardware tools, analysis software, algorithms, private databases, and infrastructures to support the research in genomics. In this paper, we analyze different approaches for compressing digital files generated by Next-Generation Sequencing tools containing nucleotide sequences, and we discuss and evaluate the compression performance of generic compression algorithms by confronting them with a specific system designed by Jones et al. specifically for genomic file compression: Quip. Moreover, we present a simple but effective technique for the compression of DNA sequences in which we only consider the relevant DNA data and experimentally evaluate its performances. Full article

(This article belongs to the Special Issue 2020 Selected Papers from Algorithms Editorial Board Members)

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30 pages, 853 KiB

Open AccessArticle

A Novel Method for Inference of Chemical Compounds of Cycle Index Two with Desired Properties Based on Artificial Neural Networks and Integer Programming

by Jianshen Zhu, Chenxi Wang, Aleksandar Shurbevski, Hiroshi Nagamochi and Tatsuya Akutsu

Algorithms 2020, 13(5), 124; https://doi.org/10.3390/a13050124 - 18 May 2020

Cited by 9 | Viewed by 4314

Abstract

Inference of chemical compounds with desired properties is important for drug design, chemo-informatics, and bioinformatics, to which various algorithmic and machine learning techniques have been applied. Recently, a novel method has been proposed for this inference problem using both artificial neural networks (ANN) [...] Read more.

Inference of chemical compounds with desired properties is important for drug design, chemo-informatics, and bioinformatics, to which various algorithmic and machine learning techniques have been applied. Recently, a novel method has been proposed for this inference problem using both artificial neural networks (ANN) and mixed integer linear programming (MILP). This method consists of the training phase and the inverse prediction phase. In the training phase, an ANN is trained so that the output of the ANN takes a value nearly equal to a given chemical property for each sample. In the inverse prediction phase, a chemical structure is inferred using MILP and enumeration so that the structure can have a desired output value for the trained ANN. However, the framework has been applied only to the case of acyclic and monocyclic chemical compounds so far. In this paper, we significantly extend the framework and present a new method for the inference problem for rank-2 chemical compounds (chemical graphs with cycle index 2). The results of computational experiments using such chemical properties as octanol/water partition coefficient, melting point, and boiling point suggest that the proposed method is much more useful than the previous method. Full article

(This article belongs to the Special Issue 2020 Selected Papers from Algorithms Editorial Board Members)

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