Computer Science
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ISSN 1508-2806
e-ISSN: 2300-7036
Issue Date
2021
Volume
Vol. 22
Number
No. 4
Description
Reviewed by: Szymon Lukasik, Raghad Yousif, Amin Salih, Karol Opara, Marcin Plucinski, Mikolaj Kromka, Piotr Kalita, Aleksander Wawer, Pouria Behnoudfar, Robert Schaefer, Piotr Nowak
Journal Volume
Computer Science
Vol. 22 (2021)
Projects
Pages
Articles
Overview of adaptive and low-rank approximation algorithms for modeling influence of electromagnetic waves generated by cellphone antenna on human head
(Wydawnictwa AGH, 2021) Głut, Barbara; Paszyński, Maciej
This paper presents an overview of formulations and algorithms that are dedicated to modeling the influence of electromagnetic waves on the human head. We start from h adaptive approximation of a three-dimensional MRI scan of the human head. Next, we solve the time-harmonic Maxwell equations with a 1.8 GHz cellphone antenna. We compute the specific absorption rate used as the heat source for the Pennes bioheat equation modeling the heat generated by EM waves inside the head. We propose an adaptive algorithm mixed with time-stepping iterations where we simultaneously refine the computational mesh, solve the Maxwell and Pennes equations, and iterate the time steps. We employ the sparse Gaussian elimination algorithm with the low-rank compres-sion of the off-diagonal matrix blocks for the factorization of the matrices. We conclude with the statement that 15 minutes of talking with a 1.8 GHz antenna with one watt of power results in increased brain tissue temperatures (up to 38.4$^{\circ}$C).
Meta-heuristic approach based on genetic and greedy algorithms to solve flexible job-shop scheduling problem
(Wydawnictwa AGH, 2021) Rezaeipanah, Amin; Sarhangnia, Fariba; Abdollahi, Mohammad Javad
Job-shop scheduling systems are one of the applications of group technology in industry, the purpose of which is to take advantage of the physical or operational similarities of products in their various aspects of construction and design. Additionally, these systems are identified as cellular manufacturing systems (CMS). In this paper, a meta-heuristic method that is based on combining genetic and greedy algorithms has been used in order to optimize and evaluate the performance criteria of the flexible job-shop scheduling problem. In order to improve the efficiency of the genetic algorithm, the initial population is generated by the greedy algorithm, and several elitist operators are used to improve the solutions. The greedy algorithm that is used to improve the generation of the initial population prioritizes the cells and the job in each cell and, thus, offers quality solutions. The proposed algorithm is tested over the P-FJSP dataset and compared with the state-of-the-art techniques of this literature. To evaluate the performance of the diversity, spacing, quality, and run-time criteria were used in a multi-objective function. The results of the simulation indicate the better performance of the proposed method as compared to the NRGA and NSGA-II methods.
Novel framework for aspect knowledge base generated automatically from social media using pattern rules
(Wydawnictwa AGH, 2021) Trần, Tuấn Anh; Duangsuwan, Jarunee; Wettayaprasit, Wiphada
One of the factors that improve businesses in business intelligence is summarization systems that can generate summaries based on sentiment from social media. However, these systems cannot produce such summaries automatically, they use annotated datasets. To support these systems with annotated datasets, we propose a novel framework that uses pattern rules. The framework has two procedures: 1) pre-processing, and 2) aspect knowledge-base generation. The first procedure is to check and correct any misspelled words (bigram and unigram) by a proposed method and tag the parts-of-speech of all of the words. The second procedure is to automatically generate an aspect knowledge base that is to be used to produce sentiment summaries by sentiment-summarization systems. Pattern rules and semantic similarity-based pruning are used to automatically generate an aspect knowledge base from social media. In the experiments, eight domains from benchmark datasets of reviews are used. The performance evaluation of our proposed approach shows the highest performance when compared to other unsupervised approaches.
Efficient simulations of large-scale convective heat transfer problems
(Wydawnictwa AGH, 2021) Goik, Damian; Banaś, Krzysztof; Bielański, Jan Gustaw; Chłoń, Kazimierz
We describe an approach for efficient solution of large-scale convective heat transfer problems that are formulated as coupled unsteady heat conduction and incompressible fluid-flow equations. The original problem is discretized over time using classical implicit methods, while stabilized finite elements are used for space discretization. The algorithm employed for the discretization of the fluid-flow problem uses Picard’s iterations to solve the arising nonlinear equations. Both problems (the heat transfer and Navier–Stokes equations) give rise to large sparse systems of linear equations. The systems are solved by using an iterative GMRES solver with suitable preconditioning. For the incompressible flow equations, we employ a special preconditioner that is based on an algebraic multigrid (AMG) technique. This paper presents algorithmic and implementation details of the solution procedure, which is suitably tuned – especially for ill-conditioned systems that arise from discretizations of incompressible Navier–Stokes equations. We describe a parallel implementation of the solver using MPI and elements from the PETSC library. The scalability of the solver is favorably compared with other methods, such as direct solvers and the standard GMRES method with ILU preconditioning.
Optimized jk-nearest neighbor based online signature verification and evaluation of main parameters
(Wydawnictwa AGH, 2021) Saleem, Muhammad; Kovari, Bence
In this paper, we propose an enhanced $jk$-nearest neighbor ($jk$-NN) algorithm for online signature verification. The effect of its main parameters is evaluated and used to build an optimized system. The results show that the $jk$-NN classifier improves the verification accuracy by 0.73–10% as compared to a traditional one-class $k$-NN classifier. The algorithm achieved reasonable accuracy for different databases: a 3.93% average error rate when using the SVC2004, 2.6% for the MCYT-100, 1.75% for the SigComp'11, and 6% for the SigComp'15 databases. These results followed a state-of-the-art accuracy evaluation where both forged and genuine signatures were used in the training phase. Another scenario is also presented in this paper by using an optimized $jk$-NN algorithm that uses specifically chosen parameters and a procedure to pick the optimal value for $k$ using only the signer’s reference signatures to build a practical verification system for real-life scenarios where only these signatures are available. By applying the proposed algorithm, the average error rates that were achieved were 8% for SVC2004, 3.26% for MCYT-100, 13% for SigComp'15, and 2.22% for SigComp'11.