Description Of Algorithm Research Articles

A Study in Reproducibility: The Congruent Matching Cells Algorithm and cmcR Package

Scientific research is driven by our ability to use methods, procedures, and materials from previous studies and further research by adding to it. As the need for computationally-intensive methods to analyze large amounts of data grows, the criteria needed to achieve reproducibility, specifically computational reproducibility, have become more sophisticated. In general, prosaic descriptions of algorithms are not detailed or precise enough to ensure complete reproducibility of a method. Results may be sensitive to conditions not commonly specified in written-word descriptions such as implicit parameter settings or the programming language used. To achieve true computational reproducibility, it is necessary to provide all intermediate data and code used to produce published results. In this paper, we consider a class of algorithms developed to perform firearm evidence identification on cartridge case evidence known as the *Congruent Matching Cells* (CMC) methods. To date, these algorithms have been published as textual descriptions only. We introduce the first open-source implementation of the Congruent Matching Cells methods in the R package cmcR. We have structured the cmcR package as a set of sequential, modularized functions intended to ease the process of parameter experimentation. We use cmcR and a novel variance ratio statistic to explore the CMC methodology and demonstrate how to fill in the gaps when provided with computationally ambiguous descriptions of algorithms.

Development of an algorithm for identifying single-phase ground fault conditions in cable and overhead lines in the networks with isolated neutral

The paper addresses the issue of identification of the type of single-phase ground fault in medium voltage networks operating mainly with an isolated neutral. By analyzing the published research, the most promising methods for detection of a single-phase ground fault were selected. Further, using the selected parameters, algorithms were designed to determine the parameters, process them, and use them to identify the type of a single-phase ground fault. The algorithm developed by the authors was formalized for the application on the basis of microprocessor-based protection systems using digital signal processors. The operation process was modeled using real oscillograms of single-phase ground fault. The use of high-frequency zero-sequence current and voltage components, as well as the root-mean-square value of the zero-sequence voltage, made it possible to design an algorithm that identifies any of the possible single-phase ground fault types (bolted, arc, or contact resistance ones). The first part of the article gives an overview of the state-of-the-art of research in the field of detection and identification of single-phase-to-earth faults. In the second part, the parameters of emergency operating conditions are analyzed and the key parameters are determined based on them. The third part contains a description of the single-phase fault identification algorithm developed by the authors and the output oscillograms. The fourth part makes conclusions on the applicability of the algorithm.

Программирование массивно-параллельных архитектур без API на примере параллельного алгоритма построения дерева

The work proposes an approach to programming parallel architectures without using parallel programming interfaces (APIs) and parallel directives for various numerical simulation and computer graphics applications. The primary goal of this approach is to resolve the fundamental conflict between cross-platform compatibility and hardware acceleration when developing high-performance programs. This conflict is resolved through the automation of the development process: algorithmic descriptions in C++, free from any specific parallel constructs, are automatically translated into the implementation of the same algorithm, and a realization is generated on an existing parallel architecture programming API (C++ translates to SPIR-V for GPUs, and vector instructions for CPUs). Furthermore, if specific hardware acceleration is required, the programmer can replace individual parts of the generated code by substituting kernels and virtual functions in the generated code. Such substitution allows for code regeneration without losing usermade changes tailored to a particular hardware platform. The developed system operates as a white box, allowing the programmer to read and debug the generated code as if it were written manually. This enables us to easily distinguish translator errors from user errors and, moreover, does not create dependencies on the developed system in projects. The generated code can always be manually rewritten when necessary. The application of the developed technology is considered using the example of one of the most complex and inconvenient problems for GPUs — parallel algorithms for constructing trees.

Применение методов идемпотентной алгебры в генетическом алгоритме для решения задачи календарного планирования

The resource-constrained project scheduling problem in monetary form is considered. The criterion for the optimal start schedule for each project activity is the maximum net present value, which fulfills the constraints on sufficiency of funds and takes into account the technological relationship between the activities. This problem is NP-hard in a strong sense. It is proved that the project schedule can be represented as a solution of a linear equation over an idempotent semiring. A sufficient condition has been established for the admissibility of the schedule in terms of the partial order of work and the duration of the project. It is proved that each of the project schedules can be represented as a product of a matrix of a special form, calculated on the basis of the partial order matrix of the project, and a vector from an idempotent semimodule. For the coordinates of the vector, upper and lower limits have been determined, taking into account the timing of the activity. A description of the genetic algorithm for solving the problem is given. The algorithm is based on the evolution of a population whose individuals represent solutions of an idempotent equation for a partial order matrix of the project. The computational experiments demonstrate the effectiveness of the algorithm.

СОВЕРШЕНСТВОВАНИЕ МЕТОДИКИ ОПТИМИЗАЦИИ РАССТАНОВКИ РЕЗЦОВ НА ИСПОЛНИТЕЛЬНЫХ ОРГАНАХ ПРОХОДЧЕСКИХ КОМБАЙНОВ

The technique of optimizing the placement of incisors on the executive body of a roadheaders is considered. A detailed description of the calculation algorithm is given. The limits of variation of the main parameters and quality criteria are determined. A mathematical description of the condition of the intersection of the tool holders is presented, which is an important stage of optimization.

COMPUTATIONAL FEATURES OF CALCULAT-ING EXTENDED CONDUCTOR ANTENNAS BASED ON THE INTEGRAL EQUATIONS METHOD

The numerical solution of integral equations or a system of integral equations (for a system of conductors) consists in their discretization and reduction to a system of linear algebraic equations for the desired function. For discretization, it is possible to use projection methods, for example, the Galerkin method or the collocation method. A system of linear algebraic equations for the class of problems under consideration is characterized by a complete (filled) complex matrix. For conductor antenna structures that are quite arbitrary in geometry and size, systems of linear algebraic equations turn out to be of a high order, and special computational algorithms are required to solve them. The purpose of the work is to study adequate mathematical models for wire antennas with a subsequent description of uniform numerical algorithms based on methods of sampling and approximation of antenna current. With this method, the error in solving integral equations is determined by the error in calculating the elements of the matrix of a system of linear algebraic equations for a given piecewise polynomial approximation of the solution at step h. If the quadrature formula for numerical integration is chosen, then there are two ways to reduce the solution error. An increase in the number of collocation points leads to a rapid increase in the volume of calculations and, consequently, to a rapid increase in the amount of occupied computer memory. Therefore, the question arises of choosing the most profitable method of piecewise polynomial interpolation and discretization step h from the point of view of using computer resources, ensuring an acceptable number of solutions.

Crosstalk Performance Analysis: ENRZ, NRZ, PAM3, and PAM4

The performances of Ensemble Non-Return-to-Zero (ENRZ) under the interferences of crosstalk, in conjunction with Non-Return-to-Zero (NRZ), Pulse Amplitude Modulation of 3-level (PAM3), and Pulse Amplitude Modulation of 4-level (PAM4) are investigated. Two scenarios, 0 dB and high-loss, with varying levels of dual-side Far-End Cross-Talk (FEXT) and Near-End Cross-Talk (NEXT), are studied. Following a detailed description of the ENRZ algorithm, the reasons that lead to the performance edge of ENRZ in contrast to the rest three modulations are analyzed. The proper methodology of injecting the crosstalk interferences in the differential and multi-wire-based modulation systems is discussed. A widely existing issue of computing FEXT is pointed out along with the correct technique. A holistic channel simulation method called Frequency Domain Matrix Multiplication (FDMM) is employed to better handle multi-wire-based channels. The eye diagrams obtained with the four modulation techniques are compared. The study shows that overall, ENRZ has a remarkable advantage over the other three modulation techniques, which become more prominent as the crosstalk level increases.

Thermogenomic Analysis of Left-Handed Z-DNA Propensities in Genomes.

The initial discovery of left-handed Z-DNA was met with great excitement as a dramatic alternative to the right-handed double-helical conformation of canonical B-DNA. In this chapter, we describe the workings of the program ZHUNT as a computational approach to mapping Z-DNA in genomic sequences using a rigorous thermodynamic model for the transition between the two conformations (the B-Z transition). The discussion starts with a brief summary of the structural properties that differentiate Z- from B-DNA, focusing on those properties that are particularly relevant to the B-Z transition and the junction that splices a left- to right-handed DNA duplex. We then derive the statistical mechanics (SM) analysis of the zipper model that describes the cooperative B-Z transition and show that this analysis very accurately simulates this behavior of naturally occurring sequences that are induced to undergo the B-Z transition through negative supercoiling. A description of the ZHUNT algorithm and its validation are presented, followed by how the program had been applied for genomic and phylogenomic analyses in the past and how a user can access the online version of the program. Finally, we present a new version of ZHUNT (called mZHUNT) that has been parameterized to analyze sequences that contain 5-methylcytosine bases and compare the results of the ZHUNT and mZHUNT analyses on native and methylated yeast chromosome 1.

ПРИМЕНЕНИЕ АЛГОРИТМОВ ФОРМИРОВАНИЯ ВОПРОСОВ ПО ТЕКСТУ ДЛЯ АВТОМАТИЧЕСКОЙ ГЕНЕРАЦИИ ТЕСТОВ

The article presents findings of manual, semi-automatic, and automatic approaches to gen-erate test questions based on such methods as annotation, keyword extraction, and learning datasets for com-piling tests for studying material, along with a description of each method algorithm, examples of generated questions, and their quality assessment. These examples demonstrate the advantages of an algorithm for gen-erating a method using a dataset and a combination of methods, as well as their possible practical application.

The Concept of an Intelligent Decision Support System for Ore Transportation in Underground Mine

The management of in-mine ore transportation systems is a crucial challenge due to the constant development of underground mines. The transport network's stability and efficiency directly affect enterprise profits and growth. Managing key areas such as transportation asset maintenance, object control, and ore extraction planning is essential to ensuring high transportation standards. For larger, multi-site enterprises, transportation supervision is divided among many managers. To address short-term production planning and management support during unplanned events, a decision support system is proposed in this article to meet the expectations of both lower and higher-level managers. The proposed concept is based on the simulation deployed from real-time operational data from mine, in which an agent is placed and taught through reinforcement learning. The simulation as well as agent possible actions have a block structure, where each means of ore transport is a separate add-on. In addition, it is assumed that the solution will be further improved by the implementation of various algorithms found in the literature (e.g. early fault detection algorithms). When created and implemented, such a system will bring improvements in multiple levels of operation, such as the safety of employees, overall machine health, cost reduction, energy efficiency, etc. As a result of this work, a conception of an intelligent decision-support system is presented, along with the specification of areas of its influence and a description of tasks and algorithms proposed to be incorporated within the system itself.

ARTIFICIAL INTELLIGENCE AND STOCHASTIC OPTIMIZATION ALGORITHMS FOR THE CHAOTIC DATASETS

Almost every natural process is stochastic due to the basic consequences of nature’s existence and the dynamical behavior of each process that is not stationary but evolves with the passage of time. These stochastic processes not only exist and appear in the fields of biological sciences but are also evident in industrial, agricultural and economical research datasets. Stochastic processes are challenging to model and to solve as well. The stochastic patterns when repeated result into random fractals and are very common in natural processes. These processes are usually simulated with the aid of smart computational and optimization tools. With the progress in the field of artificial intelligence, smart tools are developed that can model the stochastic processes by generalization and genetic optimization. Based on the basic theoretical description of the stochastic optimization algorithms, the stochastic learning tools, stochastic modeling, stochastic approximation and stochastic fractals, a comparative analysis is presented with the aid of the stochastic fractal search, multi-objective stochastic fractal search and pattern search algorithms.

Iterative re‐weighted covariates selection for robust feature selection modelling in the presence of outliers (irCovSel)

AbstractA new method for feature selective modelling in the presence of outliers is presented. The method is a combination of iterative re‐weighted partial least squares and the covariates selection approach. The method relies on iterative down‐weighting of the outlying samples prior to estimating the squared covariance for covariates selection. In this way, the outlying samples carrying low weights have minimal influence on the squared covariance estimation, while the inliers have the maximum influence on the squared covariance estimation. The method allows selecting robust features, and models based on such features in general perform better in terms of prediction accuracy (lower error) than selecting features using equal sample weights for all samples. The algorithm description and tests of the method in single and multiple response scenarios are presented. Method performance is also demonstrated on a real spectral data set.

Nonlinear Model Order Selection: A GMM Clustering Approach Based on a Genetic Version of EM Algorithm

Choosing an accurate model order is one of the important sections in system identification. Traditionally, the model order selection of a nonlinear system depends on a predetermined model. However, it requires excess calculation and is impossible to get rid of the trouble of the structural design of the model, once the specific model was determined. A false nearest neighbor (FNN) algorithm that only relies on input-output data to estimate the model order is proposed here. Due to the FNN algorithm is sensitive to its own threshold which is a crucial constant for evaluating the model structure, Gaussian mixture model (GMM) clustering based on a genetic version of the expectation-maximization (EM) algorithm and minimum description length (MDL) criterion is developed in this paper, where the order can be determined without relying on a specific model. The GMM clustering is proposed to calculate the threshold of the FNN. Then, the genetic algorithm and MDL criteria are embedded to optimize the calculation of the EM algorithm as reduce the influence of initial values and not prone to fall into local extreme values as well. Three examples are given here to indicate the superiority of this technique: simulation of a strongly nonlinear system, isothermal polymerization process, and Van der Vusse reaction in relevant reference. Finally, some typical modeling methods are conducted to confirm the validity of this approach.

Learning-based underwater image enhancement: An efficient two-stream approach

Underwater image enhancement (UIE) attracts extensive attention due to its crucial importance in marine missions. Built upon the powerful representation capacity of deep neural networks (DNNs), recent years have witnessed the exponential growth of learning-based UIE methods. Thus, this work first thoroughly reviews numerous learning-based UIE methods including the algorithm description, dataset generation, and evaluation methodology. Although these learned UIE methods demonstrate remarkable efficiency for improving the reconstruction quality, they still present noticeable limitations, e.g., model generalization and unexpected quality impairments. To tackle these issues, we develop an embarrassingly-efficient two-stream UIE approach to improve both the enhancement quality of underwater images. One stream is the Significant Region Refinement (SRR) that stacks channel attention optimized residual groups to improve the image illumination and resolve the color casting problem, and the other is the Global Appearance Adjustment (GAA) that relies on several dense blocks to enhance the global image sharpness. The final output is derived by intelligently weighing contributions from the SRR and GAA branches. We verify our method using a variety of UIE datasets. Simulations show that our model outperforms state-of-the-art works in both quantitative and qualitative measurements with affordable complexity consumption.

A new catalogue of solar flare events from soft X-ray GOES signal in the period 1986–2020

Solar flares, along with other sun-originated events such as Coronal Mass Ejections, fast solar wind streams, and solar energetic particles are among the most relevant events in Space Weather. Moreover, solar flares are the most energetic processes that occur in our solar system. The in-depth study of their occurrence statistics, both over extended periods or during individual solar cycles, allows us to improve and constrain the basic physical models of their origin. Increasing the number of detected events, especially those of lower intensity, and the number of physical parameters that describe the detected flares is, therefore, a mandatory goal.In this paper, we present a computationally efficient algorithm for the detection of solar flares in the soft-X solar flux provided by the GOES (NASA/NOAA) satellite constellation. Our code produces a new flare catalogue increasing the number of events with respect to the official GOES list. In addition to increasing the number of identified events, the catalogue contains information such as: an estimate of the total energy released, start and end time of the event, possible overlap with other events, background level of the GOES X-ray emission close to the revealed event. After a detailed description of the detection algorithm, we carry out a preliminary analysis of the flares reported in our catalogue and compare our results with the official list of GOES for the period from 1998 to 2020.

Stochastic process and tutorial of the African buffalo optimization

This paper presents the data description of the African buffalo optimization algorithm (ABO). ABO is a recently-designed optimization algorithm that is inspired by the migrant behaviour of African buffalos in the vast African landscape. Organizing their large herds that could be over a thousand buffalos using just two principal sounds, the /maaa/ and the /waaa/ calls present a good foundation for the development of an optimization algorithm. Since elaborate descriptions of the manual workings of optimization algorithms are rare in literature, this paper aims at solving this problem, hence it is our main contribution. It is our belief that elaborate manual description of the workings of optimization algorithms make it user-friendly and encourage reproducibility of the experimental procedures performed using this algorithm. Again, our ability to describe the algorithm’s basic flow, stochastic and data generation processes in a language so simple that any non-expert can appreciate and use as well as the practical implementation of the popular benchmark Rosenbrock and Shekel Foxhole functions with the novel algorithm will assist the research community in benefiting maximally from the contributions of this novel algorithm. Finally, benchmarking the good experimental output of the ABO with those of the popular, highly effective and efficient Cuckoo Search and Flower Pollination Algorithm underscores the ABO as a worthy contribution to the existing body of population-based optimization algorithms

Numerical Simulation and Active Protection of Lightning Discharge Based on Quantum Heuristic Evolutionary Algorithm

Thunder is a discharge phenomenon that often occurs in nature. Due to its physical influences such as strong current, high temperature, strong shock waves, and strong electromagnetic radiation, it has a huge destructive effect instantly, which may bring serious threats to people’s lives and property safety. This study aimed to study the lightning discharge numerical simulation and active protection based on the quantum heuristic evolutionary algorithm and proposed to apply the lightning discharge numerical simulation to the prevention of lightning disasters. This article gives a detailed description of the quantum algorithm, the generation, and harm of lightning discharge. The genetic algorithm is used to optimize the lightning data simulation algorithm, and the optimization process is introduced in detail. In addition, this article conducts related experiments on lightning discharge numerical simulation and active protection. The experimental results show that targeted active protection and effective numerical simulation are important measures to prevent lightning disasters. Active lightning protection measures can reduce lightning by 30%. Losses are caused by disasters.

Fibonacci Sequences, Symmetry and Order in Biological Patterns, Their Sources, Information Origin and the Landauer Principle

Physical roots, exemplifications and consequences of periodic and aperiodic ordering (represented by Fibonacci series) in biological systems are discussed. The physical and biological roots and role of symmetry and asymmetry appearing in biological patterns are addressed. A generalization of the Curie–Neumann principle as applied to biological objects is presented, briefly summarized as: “asymmetry is what creates a biological phenomenon”. The “top-down” and “bottom-up” approaches to the explanation of symmetry in organisms are presented and discussed in detail. The “top-down” approach implies that the symmetry of the biological structure follows the symmetry of the media in which this structure is functioning; the “bottom-up” approach, in turn, accepts that the symmetry of biological structures emerges from the symmetry of molecules constituting the structure. A diversity of mathematical measures applicable for quantification of order in biological patterns is introduced. The continuous, Shannon and Voronoi measures of symmetry/ordering and their application to biological objects are addressed. The fine structure of the notion of “order” is discussed. Informational/algorithmic roots of order inherent in the biological systems are considered. Ordered/symmetrical patterns provide an economy of biological information, necessary for the algorithmic description of a biological entity. The application of the Landauer principle bridging physics and theory of information to the biological systems is discussed.

Semantic Network Model for Sign Language Comprehension

In this study, the authors propose a computational cognitive model for sign language (SL) perception and comprehension with detailed algorithmic descriptions based on cognitive functionalities in human language processing. The semantic network model (SNM) that represents semantic relations between concepts is used as a form of knowledge representation. The proposed model is applied in the comprehension of sign language for classifier predicates. The spreading activation search method is initiated by labeling a set of source nodes (e.g., concepts in the semantic network) with weights or “activation” and then iteratively propagating or “spreading” that activation out to other nodes linked to the source nodes. The results demonstrate that the proposed search method improves the performance of sign language comprehension in the SNM.

Automation algorithms for water bodies characteristics calculation on the example of the Volga-Akhtuba floodplain

Introduction. Organization and implementation of a special spring release, carried out annually in the interests of the agriculture and fisheries of the Lower Volga, is the most important and complex problem of the Volga-Kama cascade management. The way to increase the efficiency of solving this problem is to use of mathematical models for calculating the movement of water along the Volga-Akhtuba floodplain. However, the currently functioning mathematical hydrodynamic models of the Lower Volga do not provide enough accuracy and speed. It is planned to create an algorithm for modeling the movement of water masses, combining the use of nomograms of dynamic volumes and a tank model. The characteristics of water bodies and streams that form the water system of the Lower Volga should be obtained at the first stage.
 
 Materials and methods. The materials for the work were a raster digital elevation model with 30 m resolution, obtained from Earth remote sensing data. Algorithms for determining the position and boundaries of water bodies and calculating their characteristics are implemented in the Fortran programming language.
 
 Results. The description of algorithms of smoothing the digital elevation model, searching for local minima, and determining the boundaries of floodplain reservoirs developed to prepare information structures using in the created mathematical model of a water system with a developed network of floodplain streams and lakes is given on the example of the Volga-Akhtuba floodplain. The results of testing the developed algorithms are shown.
 
 Conclusions. The results of the algorithms for searching for local minima on the digital elevation model and determining the position and boundaries of floodplain reservoirs make it possible to calculate the characteristics of water bodies, building a model graph, and developing a data storage system at the next stages of work.