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Wyświetlanie 31-45 z 174
Tytuł:
Evolutionary method of robust controller computation
Techniki ewolucyjne doboru regulatorów odpornych
Autorzy:
Królikowski, T.
Nikończuk, P.
Powiązania:
https://bibliotekanauki.pl/articles/277210.pdf
Data publikacji:
2013
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
algorytmy genetyczne
sterowanie odporne
genetic algorithms
robust control
Opis:
Matematyczne metody doboru współczynników regulatora odpornego w przestrzeniach H∞ są bardzo skomplikowane. Projektant układu regulacji musi wykazywać się znajomością technik analizy funkcjonalnej. Do rozwiązywania problemów optymalizacji tego rodzaju doskonale nadają się algorytmy ewolucyjne. W artykule przedstawiono metodę oraz wyniki symulacji podczas doboru współczynników równania regulatora odpornego. Do doboru użyte są tylko dwa kryteria: sprawdzenie stabilności i zależność geometryczna - minimalizacja największej odległości między krzywymi Nyquista operacji G(jω) i 1/F(jω), gdzie G(jω) i F(jω) są transmitancjami regulatora oraz obiektu regulacji w układzie sprzężenia zwrotnego.
Mathematical methods of robust controller coefficients selection in H∞ spaces are very complicated. A control system integrator has to know functional analysis methods. To solve this kind of problem, evolutionary algorithms can be used. The paper presents both the method and simulation results of evolutionary algorithms application for a robust controller coefficients selection. To select robust controller, only two requirements are used: stability check and geometric dependency - minimizing the maximum distance between Nyquist diagrams of operations - G(jω) and 1/F(jω). Where G(jω) and F(jω) are controller and plant transfer functions in a feedback control system.
Źródło:
Pomiary Automatyka Robotyka; 2013, 17, 1; 80-82
1427-9126
Pojawia się w:
Pomiary Automatyka Robotyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Grammars in genetic programming
Autorzy:
Wieczorek, W.
Czech, Z.
Powiązania:
https://bibliotekanauki.pl/articles/205856.pdf
Data publikacji:
2000
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
algorytm genetyczny
gramatyka
genetic algorithms
grammars
strongly typed genetic programming
Opis:
The work consists of two parts. In the first part the idea of genetic programming is presented and the basic elements of a genetic programming system are described. In the second part, considering a selected example, we describe the results of investigations of the influence of program grammars on the efficiency of genetic programming.
Źródło:
Control and Cybernetics; 2000, 29, 4; 1019-1030
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparative Study of Particle Swarm Optimization and Genetic Algorithms for Complex Mathematical Functions
Autorzy:
Valdez, F.
Melin, P.
Powiązania:
https://bibliotekanauki.pl/articles/384575.pdf
Data publikacji:
2008
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
genetic algorithms
particle swarm optimization (PSO)
hybrid systems
optimization
Opis:
The Particle Swarm Optimization (PSO) and the Genetic Algorithms (GA) have been used successfully in solving problems of optimization with continuous and combinatorial search spaces. In this paper the results of the application of PSO and GAs for the optimization of mathematical functions are presented. These two methodologies have been implemented with the goal of making a comparison of their performance in solving complex optimization problems. This paper describes a comparison between a GA and PSO for the optimization of complex mathematical functions.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2008, 2, 1; 43-51
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evolutionary Learning of Goal-Oriented Communication Strategies in Multi-Agent Systems
Autorzy:
Althnian, A.
Agah, A.
Powiązania:
https://bibliotekanauki.pl/articles/384735.pdf
Data publikacji:
2015
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
multi-agent system
communication strategy
evolutionary communication
genetic algorithms
Opis:
Previous studies in multi-agent systems have observed that varying the type of information that agents communicate, such as goals and beliefs, has a significant impact on the performance of the system with respect to different, usually conflicting, performance metrics, such as speed of solution, communication efficiency, and travel distance/cost. Therefore, when designing a communication strategy for a multi-agent system, it is unlikely that one strategy can perform well with respect to all of performance metrics. Yet, it is not clear in advance, which strategy will be the best with respect to each metric. With multi-agent systems being a common paradigm for building distributed systems in different domains, performance goals can vary from one application to the other according to the domain’s specifications and requirements. To address this issue, this work proposes a genetic algorithm-based approach for learning a goal- oriented communication strategy. The approach enables learning an effective communication strategy with respect to flexible, user-defined measurable performance goals. The learned strategy will determine what, when, and to whom information should be communicated during the course of task execution in order to improve the performance of the system with respect to the stated goal. Our preliminary evaluation shows that the proposed approach has promising results and the learned strategies have significant usefulness in improving the performance of the system with respect to the goals.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2015, 9, 3; 52-64
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Exchange Rates: Predictable but not Explainable? Data Mining with Leading Indicators and Technical Trading Rules
Możliwości modelowania i prognozowania kursów walutowych: wskaźniki wyprzedzające i analiza techniczna
Autorzy:
Brandl, Bernd
Powiązania:
https://bibliotekanauki.pl/articles/907593.pdf
Data publikacji:
2005
Wydawca:
Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:
exchange rates
data mining
artificial neural networks
genetic algorithms
Opis:
This paper presents a data mining approach to forecasting exchange rates. It is assumed that exchange rates are determined by both fundamental and technical factors. The balance of fundamental and technical factors varies for each exchange rate and frequency. It is difficult for forecasters to establish the relative relevance of different kinds of factors given this mixture; therefore the utilization of data mining algorithms is advantageous. The approach applied uses a genetic algorithm and neural networks. Out-of-sample forecasting results are illustrated for five exchange rates on different frequencies and it is shown that data mining is able to produce forecasts that perform well.
W artykule przedstawiono proces eksploracji danych statystycznych w prognozowaniu kursów walutowych. Zakładamy, że kursy walutowe pozostają pod wpływem zarówno czynników o charakterze fundamentalnym, jak i czynników pozaekonomicznych. Równowaga pomiędzy tymi czynnikami różni się w zależności od rodzaju kursu walutowego i częstotliwości jego pomiaru. Prognostykom trudno jest ustalić względną siłę wpływu różnych czynników, stąd analiza polegająca na eksploracji danych ma określone zalety. W proponowanym podejściu wykorzystano algorytmy genetyczne i sztuczne sieci neuronowe. Przedstawiliśmy wyniki eksperymentów prognostycznych poza próbą statystyczną w odniesieniu do pięciu kursów walutowych, obserwowanych z różną częstotliwością. Pokazaliśmy, że metoda eksploracji danych może stanowić skuteczne narzędzie prognostyczne.
Źródło:
Acta Universitatis Lodziensis. Folia Oeconomica; 2005, 192
0208-6018
2353-7663
Pojawia się w:
Acta Universitatis Lodziensis. Folia Oeconomica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An efficient genetic algorithm for the uncapacitated multiple allocation p-hub median problem
Autorzy:
Stanimirovic, Z.
Powiązania:
https://bibliotekanauki.pl/articles/970612.pdf
Data publikacji:
2008
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
p-hub problem
genetic algorithms
discrete location and assignment
Opis:
In this paper the Uncapacitated Multiple Allocation p-hub Median Problem (the UMApHMP) is considered. A new heuristic method based on a genetic algorithm approach (GA) for solving UMApHMP is proposed. The described GA uses binary representation of the solutions. Genetic operators which keep the feasibility of individuals in the population are designed and implemented. The mutation operator with frozen bits is used to increase the diversibility of the genetic material. The running time of the GA is improved by caching technique. Proposed GA approach is bench-marked on the well known CAB and AP data sets and compared with the existing methods for solving the UMApHMP. Computational results show that the GA quickly reaches all previously known optimal solutions, and also gives results on large scale AP instances (up to n=200, p=20) that were not considered in the literature so far.
Źródło:
Control and Cybernetics; 2008, 37, 3; 669-692
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
AH Method: a Novel Routine for Vicinity Examination of the Optimum Found with a Genetic Algorithm
Autorzy:
Piętak, Daniel Andrzej
Bilski, Piotr
Napiorkowski, Paweł Jan
Powiązania:
https://bibliotekanauki.pl/articles/2200688.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
heuristics
evolutionary computations
genetic algorithms
uncertainty estimation
parameter study
Opis:
The paper presents a novel heuristic procedure (further called the AH Method) to investigate function shape in the direct vicinity of the found optimum solution. The survey is conducted using only the space sampling collected during the optimization process with an evolutionary algorithm. For this purpose the finite model of point-set is considered. The statistical analysis of the sampling quality based upon the coverage of the points in question over the entire attraction region is exploited. The tolerance boundaries of the parameters are determined for the user-specified increase of the objective function value above the found minimum. The presented test-case data prove that the proposed approach is comparable to other optimum neighborhood examination algorithms. Also, the AH Method requires noticeably shorter computational time than its counterparts. This is achieved by a repeated, second use of points from optimization without additional objective function calls, as well as significant repository size reduction during preprocessing.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 4; 695--708
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Biologically inspired methods for control of evolutionary algorithms
Autorzy:
Stańczak, J.
Powiązania:
https://bibliotekanauki.pl/articles/206262.pdf
Data publikacji:
2003
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
algorytm genetyczny
adaptacja
adaptacyjny algorytm ewolucyjny
genetic algorithms
adaptation
adaptive ewolutionary algorithms
Opis:
In this paper two methods for evolutionary algorithm control are proposed. The first one is a new method of tuning tlie probabilities of genetic operators. It is assumed in the presented approach that every member of the optimized population conducts his own ranking of genetic operators' qualities. This ranking enables computing the probabilities of execution of genetic operators. This set of probabilities is a basis of experience of every individual and according to this basis the individual chooses the operator in every iteration of the algorithm. Due to this experience one can maximize the chances of his offspring to survive. The second part of the paper deals with a self-adapting method of selection of individuals to a subsequent generation. Methods of selection applied in the evolutionary algorithms are usually inspired by nature and prefer solutions where the main role is played by randomness, competition and struggle among individuals. In the case of evolutionary algorithms, where populations of individuals are usually small, this causes a premature convergence to local minima. In order to avoid this drawback I propose to apply an approach based rather on an agricultural technique. Two new methods of object selection are proposed: a histogram selection and a mixed selection. The methods described were tested using examples based on scheduling and TSP.
Źródło:
Control and Cybernetics; 2003, 32, 2; 411-433
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A genetic algorithm for the maximum 2-packing set problem
Autorzy:
Trejo-Sánchez, Joel Antonio
Fajardo-Delgado, Daniel
Gutierrez-Garcia, J. Octavio
Powiązania:
https://bibliotekanauki.pl/articles/330154.pdf
Data publikacji:
2020
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
maximum 2-packing set
genetic algorithms
graph algorithms
algorytm genetyczny
algorytm grafowy
Opis:
Given an undirected connected graph G = (V, E), a subset of vertices S is a maximum 2-packing set if the number of edges in the shortest path between any pair of vertices in S is at least 3 and S has the maximum cardinality. In this paper, we present a genetic algorithm for the maximum 2-packing set problem on arbitrary graphs, which is an NP-hard problem. To the best of our knowledge, this work is a pioneering effort to tackle this problem for arbitrary graphs. For comparison, we extended and outperformed a well-known genetic algorithm originally designed for the maximum independent set problem. We also compared our genetic algorithm with a polynomial-time one for the maximum 2-packing set problem on cactus graphs. Empirical results show that our genetic algorithm is capable of finding 2-packing sets with a cardinality relatively close (or equal) to that of the maximum 2-packing sets. Moreover, the cardinality of the 2-packing sets found by our genetic algorithm increases linearly with the number of vertices and with a larger population and a larger number of generations. Furthermore, we provide a theoretical proof demonstrating that our genetic algorithm increases the fitness for each candidate solution when certain conditions are met.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2020, 30, 1; 173-184
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Toward the best combination of optimization with fuzzy systems to obtain the best solution for the GA and PSO algorithms using parallel processing
Autorzy:
Valdez, Fevrier
Kawano, Yunkio
Melin, Patricia
Powiązania:
https://bibliotekanauki.pl/articles/384329.pdf
Data publikacji:
2020
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
genetic algorithms
particle swarm optimization (PSO)
fuzzy logic
parallel processing
Opis:
In general, this paper focuses on finding the best configuration for PSO and GA, using the different migration blocks, as well as the different sets of the fuzzy systems rules. To achieve this goal, two optimization algorithms were configured in parallel to be able to integrate a migration block that allow us to generate diversity within the subpopulations used in each algorithm, which are: the particle swarm optimization (PSO) and the genetic algorithm (GA). Dynamic parameter adjustment was also performed with a fuzzy system for the parameters within the PSO algorithm, which are the following: cognitive, social and inertial weight parameter. In the GA case, only the crossover parameter was modified.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 1; 55-64
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zastosowanie algorytmów genetycznych do optymalizacji modeli HMM
The use of genetic algorithms for optimalization of the models HMM
Autorzy:
Szostek, K.
Powiązania:
https://bibliotekanauki.pl/articles/320378.pdf
Data publikacji:
2005
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
optymalizacja
rozpoznawanie mowy
algorytmy genetyczne
optimization
speech recognition
genetic algorithms
Opis:
W artykule przedstawiono metodę optymalizacji modeli HMM z wykorzystaniem algorytmu genetycznego. W celu zbadania skuteczności przedstawionego algorytmu genetycznego zostały przeprowadzone badania optymalizacji modeli HMM za pomocą algorytmu Bauma-Welcha oraz zaproponowanego algorytmu genetycznego. Dodatkowo w artykule zostały umieszczone wyniki z badań modelowania sygnału mowy w postaci przebiegów czasowych przez modele HMM optymalizowane algorytmem Bauma-Welcha.
In the article there was presented the method of optimization of the models HMM with the use of the genetic algorithm. For the purpose of examining the effectiveness of the presented genetic algorithm there were carried out tests of optimization of the models HMM with the use of Baum-Welch's algorithm and the proposed genetic algorithm. In addition, in the article there were placed the results of tests of modeling the speech signal in the form of time runs by the models HMM optimized with the use of Baum-Welch 's algorithm.
Źródło:
Elektrotechnika i Elektronika; 2005, 24, 2; 183-193
1640-7202
Pojawia się w:
Elektrotechnika i Elektronika
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Voice command recognition using hybrid genetic algorithm
Autorzy:
Wroniszewska, M.
Dziedzic, J.
Powiązania:
https://bibliotekanauki.pl/articles/1955309.pdf
Data publikacji:
2010
Wydawca:
Politechnika Gdańska
Tematy:
voice command recognition
genetic algorithms
K-nearest neighbour
hybrid approach
Opis:
Speech recognition is a process of converting the acoustic signal into a set of words, whereas voice command recognition consists in the correct identification of voice commands, usually single words. Voice command recognition systems are widely used in the military, control systems, electronic devices, such as cellular phones, or by people with disabilities (e.g., for controlling a wheelchair or operating a computer system). This paper describes the construction of a model for a voice command recognition system based on the combination of genetic algorithms (GAs) and K-nearest neighbour classifier (KNN). The model consists of two parts. The first one concerns the creation of feature patterns from spoken words. This is done by means of the discrete Fourier transform and frequency analysis. The second part constitutes the essence of the model, namely the design of the supervised learning and classification system. The technique used for the classification task is based on the simplest classifier – K-nearest neighbour algorithm. GAs, which have been demonstrated as a good optimization and machine learning technique, are applied to the feature extraction process for the pattern vectors. The purpose and main interest of this work is to adapt such a hybrid approach to the task of voice command recognition, develop an implementation and to assess its performance. The complete model of the system was implemented in the C++ language, the implementation was subsequently used to determine the relevant parameters of the method and to improve the approach in order to obtain the desired accuracy. Different variants of GAs were surveyed in this project and the influence of particular operators was verified in terms of the classification success rate. The main finding from the performed numerical experiments indicates the necessity of using genetic algorithms for the learning process. In consequence, a highly accurate recognition system was obtained, providing 94.2% correctly classified patterns. The hybrid GA/KNN approach constituted a significant improvement over the simple KNN classifier. Moreover, the training time required for the GA to learn the given set of words was found to be on a level that is acceptable for the efficient functioning of the voice command recognition system.
Źródło:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2010, 14, 4; 377-396
1428-6394
Pojawia się w:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimizing the Use of Human Resources in Industrial-Service Enterprises
Autorzy:
Tychoniuk, A.
Wyczółkowski, R.
Stuchlý, V.
Powiązania:
https://bibliotekanauki.pl/articles/2064891.pdf
Data publikacji:
2018
Wydawca:
STE GROUP
Tematy:
optimization
human resource allocation
competency
selection of employees
genetic algorithms
Opis:
The optimal use of resources available in the enterprise is important regardless of the size of the company and the industry in which it operates. Enterprises are therefore forced constantly to make alternative choices related to the allocation of available resources and to optimize these choices. The article addresses the problem of the approach to optimizing the use of human resources particularly, the use of extra employee qualifications e.g., manual skills, pressure resistance, work precision, the ability to read schematic diagrams, etc. in the context of technical requirements for a given task. This is extremely important in the situation when subsequent works are individual and the conditions in which they will be performed, cannot be predicted in 100%, they may differ from those that have been implemented so far, and at the same time numerous orders of various nature are being implemented. In this situation, an accurate prediction of the requirements posed by new tasks and the appropriate selection of teams executing them can have an impact on the effectiveness of the task completion process. In the article, this problem is presented on the example of a medium-sized service enterprise operating in the industry-related sector operating basically on tender procedures and tender contests. The works are carried out on the customer's premises, often with new customers or in new field conditions. Thus, the success of the undertaking depends mainly on the optimal selection of employees with appropriate qualifications and competences. The example of an investment task is used to show a method of identifying characteristics relevant to the task as well as selection of employees in order to use the capabilities of human teams better. Technical aspects of task implementation and an employees team selection with regard to the absolutely required technical qualifications as well as the behavioral and physical skills necessary for its implementation are taken into account. The described method can be used for future tasks regardless of the changing conditions of their implementation. The intention of the authors is to develop a tool supporting the decision-making process in this area, so that it can also be used by managers with lower technical competences.
Źródło:
Multidisciplinary Aspects of Production Engineering; 2018, 1, 1; 857--865
2545-2827
Pojawia się w:
Multidisciplinary Aspects of Production Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Using a genetic algorithm for the design of an optimal transport network
Zastosowanie algorytmu genetycznego do optymalizacji sieci transportowej
Autorzy:
Król, A.
Pamuła, T.
Powiązania:
https://bibliotekanauki.pl/articles/375204.pdf
Data publikacji:
2009
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
algorytm genetyczny
sieć transportowa
optymalizacja
transport network
genetic algorithms
optimisation
Opis:
A transportation network serves the transport requirements of moving people and goods with different destination goals and relocation directions. The current network structure is usually a result of historically long adaptation process and the probability that it is not optimal is very high. Additionally it can be observed a growth of transportation needs. In these circumstances when a modernisation or expansion is required a number of competing designs must be evaluated. Combined total building expenses and maintenance costs are accepted among the evaluation criteria. Such a restriction does not guarantee an optimal solution as only a small fraction of the solution space is analysed. The input data for the optimisation problem cannot be entered in analytical form so it is natural to propose a genetic algorithm for performing the task.
Siec transportowa służy zaspokojeniu komunikacyjnych potrzeb ludności ukierunkowanych na różne punkty docelowe i różne kierunki. Ponieważ aktualna struktura sieci jest skutkiem długotrwałych procesów w przeszłosci prawdopodobienstwo, że nie jest ona optymalna dla obecnych potrzeb jest duże. Dodatkowo, przewiduje sie wzrost tych potrzeb. W takiej sytuacji, gdy wymagana jest modernizacja lub rozbudowa sieci transportowej z reguły rozpatruje sie kilka konkurencyjnych projektów i nastepnie wybiera jeden z nich. Jako kryterium rozpatruje sie łaczne koszty rozbudowy i koszty użytkowania sieci transportowej. Taka procedura nie gwarantuje znalezienia rozwiązania optymalnego, gdyż nawet niewielki ułamek przestrzeni wszystkich możliwości nie jest poddany analizie. Ze wzgledu na to, że dane wejściowe dla tego problemu nie mogą być zadane postaci analitycznej, naturalne jest zaproponowanie algorytmu genetycznego, jako narzędzia optymalizacyjnego
Źródło:
Transport Problems; 2009, 4, 4; 107-113
1896-0596
2300-861X
Pojawia się w:
Transport Problems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimisation of the machining process using genetic algorithm
Autorzy:
Čuboňová, Nadežda
Dodok, Tomáš
Ságová, Zuzana
Powiązania:
https://bibliotekanauki.pl/articles/196338.pdf
Data publikacji:
2019
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
optimisation
genetic algorithms
CAM system
optymalizacja
algorytmy genetyczne
system CAM
Opis:
This paper deals with genetic algorithms as an optimisation method and its use for optimisation of the machining process in the CAM system. Tool path verification and optimisation are two best ways of dramatically improving manufacturing operations while saving money with relatively little work. Genetic algorithms can be used for improvement of these operations and considerably reduce length of tool paths leading to the reduction of machine times and optimisation of cutting parameters. Provides the software application created to optimise processes of boring and local milling (Incomplete sentence; what or who provides).
Źródło:
Zeszyty Naukowe. Transport / Politechnika Śląska; 2019, 104; 15-25
0209-3324
2450-1549
Pojawia się w:
Zeszyty Naukowe. Transport / Politechnika Śląska
Dostawca treści:
Biblioteka Nauki
Artykuł
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