Search

Soft Computing methods have been widely used in recent years to address the challenges posed by disturbances handling and uncertainty management in Manufacturing Execution Systems (MES). The focus of this research paper is on the application of Soft Computing methods for classification problems in Behaviour Based Control. The paper proposes the use of classification techniques to determine the behavior of a production system. This is an important task as it enables the detection of anomalous behavior and allows for the implementation of appropriate corrective measures. The proposed classification method is based on the use of Neural Networks and Fuzzy logic. Neural Networks are a powerful tool for classification tasks due to their ability to learn from data and make predictions based on patterns. The proposed method uses a feedforward neural network with a single hidden layer to classify the behavior of the production system. The inputs to the network are features extracted from the production system, while the output is the classification result. Fuzzy logic is also used in the proposed classification method to handle uncertainty in the input data. In conclusion, this research paper presents a novel approach to classification problems in Behaviour Based Control using Soft Computing methods. The proposed method shows promising results in handling disturbances and uncertainty in manufacturing systems. Further research in this area could lead to the development of more advanced Soft Computing methods for manufacturing systems, enabling more efficient and effective control and management of production processes.

This paper overviews the most common malicious software types. The motivationof writing this paper was a real wordcase studythat had to be investigated. Acomputer was suspected to have had an unwanted remote desktop connection attack. This paper presents how to investigatethe event log artifacts. For the unfortunate case when such an attack is proved to have happened, the second part of the article describes a method that allows the system administrator to detect brute force attacks through the remote desktop connection. When such an attack was revealed, the attacker’s IP address can be blacklisted.

This paper presents a calculator that can determine the distance by road types and calculate the toll fee for trucks. An overview is given on the toll fees of some neighbor countries. The algorithm uses reverse geocoding and a local toll fee database.

A cikkünkben arra keressük a választ, hogy létezik-e standardizált ERP integrációs modell törzsadatok szinkronizálására, másrészt a nyers számlaadatok standardizált, újra felhasználható struktúrára való átalakítására és egy idegen ERP-rendszer felé eljuttatására. A bemutatásra kerülő ERP-integrátor-komponens újszerű, mert nem tisztán adatbázis-replikáción alapul, nem is tisztán az üzleti logikát integrálja, hanem új megközelítésként az SQL-adatbázisoknál használatos eseménykezelő eljárásokat hív meg. A módszer fontos jellemzője, hogy az egységtesztek (unit tesztek) létrehozását természetes módon támogatja, így a működést leíró specifikációs feladatokat a tesztlépések sorozatának definiálásával is meg tudjuk határozni. A módszer kellőképpen általánosítható, azaz ilyen módon, tetszőleges (SQL-alapú) ERP-rendszer integrálható. Az integrátor alkalmazásával, az integrációs eljárás kidolgozásakor nem kell ismerni az idegen (third party) ERP belső adatbázis struktúráját, valamint az idegen ERP fejlesztőjének sem kell ismerni a komponens működését, hanem csak az integrátor által elvárt előre definiált nézeteket (DB view) és eseménykezelési mechanizmusokat kell alkalmazniuk.

A tanulmány egy konkrét példán keresztül mutatja be, hogyan lehet szoftverek hasonlóságát elemezni. Kiindulásként az elvégzendő feladathoz két szoftverfejlesztési projekt teljes forráskódja állt rendelkezésre. A fejlesztői dokumentáció hiányos volt, a vizsgálathoz nem nyújtott támpontot. A vizsgálat célja az volt, hogy megtalálja azokat a mérőszámokat, amelyek objektív módon írják le a hasonlóságot, illetve különbséget. Az adott feladat ismertetése mellett a tanulmány áttekinti azokat a módszereket és algoritmusokat, amelyek a szakirodalomban ismertek. A tipikus felhasználási területek a plágiumdetektálás, a rosszindulatú kódrészletek felismerése és a dekompiláció.

A kísérleti gyártáshoz kapcsolódó kutatás-fejlesztési feladatokban nagy mennyiségű adat keletkezik. Ennek a valós idejű megjelenítése fontos követelmény a gyártástámogató informatikai rendszerekben. A nagy adathalmazokkal a felhasználók önmagában nem tudnak mit kezdeni. Azokból ki kell nyerni az információt, szűrni és aggregálni kell. Jelen cikk áttekinti az elérhető dashboardokat abból a célból, hogy a Miskolci Egyetem FIEK szervezeti egységében található PUR LABOR szoftverfejlesztési feladataihoz támpontot adjon. Bemutatjuk, hogyan lehet a kísérleti poliuretán hab gyártórendszer robotvezérlőjének naplófájljából kinyerni a receptúrára és kísérleti anyagok készletszintjét, és azok vizualizációját.

Web applications displayed on heterogeneous platforms and have a frequently changing life cycle. There is a high demand for their visual testing. This paper presents an approach for automated visual regression testing. The basic concepts of visual testing are discussed. The traditional, manual testing checks the displayed content of various browsers and platforms. This is a huge amount of monotonous work, which often results in human mistakes thus reducing reliability and consistency. Another disadvantage of the manual testing is that it is slow. Certain web applications have weekly updates, manual testing is not a feasible option for them. Automation would gain us consistency and be free from human errors. Obviously, the configuration of the test environment takes some effort but that could return on long term. The paper investigates testing time and reliability of automated visual regression testing in a selected environment.

This article describes the structure of an implementation of a license plate identification system capable of reading the characters of a license plate from a digital image. It also collects the license plate detecting and character recognizing methods mentioned in the article, and we can read about rules regarding hungary's registered license plates as well. We can gain insight into the world of computer vision, as well as understand what image manipulation procedures a digital image must go through in order to extract characters from it. The system was written in Python and also used methods provided by OpenCV, as well as the Python-Tesseract optical character recognition tool to identify license plate characters.

An integrated clustering approach is suggested for the interpretation of petrophysical properties measured on core samples. Porosity, carbonate content, bulk and grain density, permeability, irreducible water saturation and capillary pressure curves measured from different sources are processed simultaneously for a more reliable evaluation of hydrocarbon reservoirs. The specialty of the problem is that the input dataset is composed of observations at different boreholes and depth intervals, where the number of rock specimens and measurement types strongly vary. Several statistical methods such as cluster and factor analyses are traditionally used for rock typing, which are either highly limited or not feasible at all for the processing of such incomplete datasets. To overcome this difficulty, the sparse matrix of multivariate observations is fully filled with reliable estimates of petrophysical parameters before cluster analysis. In the first phase, a correlation-based interpolation method is proposed for replacing the missing data with synthetic ones estimated from the available petrophysical information. Then, principal component analysis of the filled data matrix is performed to investigate the relative contribution of different variables to the solution and reduce the large number of measured parameters into fewer variables. In the last step, non-hierarchical cluster analysis of the principal components is made to separate the lithological units and reservoir zones. The suggested statistical workflow is tested in a Hungarian oilfield, where Miocene reservoirs of different lithologies are evaluated using a large amount of laboratory data collected for three decades. When clustering all petrophysical variables in a joint procedure, not just the lithological properties but also the fluid and other reservoir characteristics are taken into account to differentiate the pay zones from unproductive intervals. In addition to the current application, the statistical method may serve as useful tool for improved well log analysis, well-to-well correlation and reservoir modeling on larger scales.

The Internet of Things (IoT) is one of the most trending scientific topics. Over the past few years, it has been revolutionizing processes in the private and public sectors. Smart systems that communicate with each other, intelligent machines and various sensors are increasingly affecting our daily lives. Although IoT is still in its infancy on a scientific scale, companies, are investing a lot of capital in integrating IoT into their planning, manufacturing, etc. processes. This article provides an overview on the main mathematical methods for IoT data analysis, on the conditions to operate such a system, the key learning algorithms to process the resulting data set, and how to integrate such a complex infrastructure into management systems.

The automotive industry has undoubtedly significantly changed our societies and daily life. It is among the most advanced, complicated, and innovative industries. The automotive industry is an essential driving factor of many other advanced industries, it requires the contribution of many other technologies like advanced manufacturing systems, cyber-physical systems, and robotics. Blockchain technology can be highly beneficial for the automotive industry, to enhance its data security, integrity and reliability, tracking and location management, enhanced connectivity, mobility-as-a-service, tamper prevention, and fraud detection. One of the emerging blockchain capabilities is smart contract enforcement and autonomy. In this article, a basic overview of smart contracts is given, early history, definition and concepts, relation with the blockchain, listing its benefits, data sources, design, and describing its importance, finally we highlighted some serious, outstanding and worth to mention steps toward activation and enabling the smart contracts and blockchain roles in the automotive industry specifically and the industry in general.

This paper deals with the flow shop scheduling problem. To find the optimal solution is an NP-hard problem. The paper reviews some algorithms from the literature and applies a benchmark dataset to evaluate their efficiency. In this research work, the discrete bacterial memetic evolutionary algorithm (DBMEA) as a global searcher was investigated. The proposed algorithm improves the local search by applying the simulated annealing algorithm (SA). This paper presents the experimental results of solving the no-idle flow shop scheduling problem. To compare the proposed algorithm with other researchers’ work, a benchmark problem set was used. The calculated makespan times were compared against the best-known solutions in the literature. The proposed hybrid algorithm has provided better results than methods using genetic algorithm variants, thus it is a major improvement for the memetic algorithm family solving production scheduling problems.

Recently, massive open online courses (MOOCs) have been offering a new online approach in the field of distance learning and online education. A typical MOOC course consists of video lectures, reading material and easily accessible tests for students. For a computer programming course, it is important to provide interactive, dynamic, online coding exercises and more complex programming assignments for learners. It is expedient for the students to receive prompt feedback on their coding submissions. Although MOOC automated programme evaluation subsystem is capable of assessing source programme files that are in learning management systems, in MOOC systems there is a grader that is responsible for evaluating students’ assignments with the result that course staff would be required to assess thousands of programmes submitted by the participants of the course without the benefit of an automatic grader. This paper presents a new concept for grading programming submissions of students and improved techniques based on the Java unit testing framework that enables automatic grading of code chunks. Some examples are also given such as the creation of unique exercises by dynamically generating the parameters of the assignment in a MOOC programming course combined with the kind of coding style recognition to teach coding standards. (Published: 11 July 2017) Citation: Research in Learning Technology 2017, 25: 1945 - http://dx.doi.org/10.25304/rlt.v25.1945

This paper focuses on the application of the Group Technology (GT) concept in the case of producing controller software for manufacturing machines. An overview on the modular machines is given. The paper presents a new concept for the development of the controllers' software. The advantages of GT are presented. Two grouping approaches are detailed: the bottom-up approach and the top-down approach. A Genetic Algorithm Clustering is presented for the automatic grouping.

This research effort focuses on improving the flexibility of production engineering for manufacturing industry by means of providing technological alternatives. A new generation of NC simulators required supporting the decision making of production engineering. In this paper a conception of NC simulator trying to fill this gap is given. This is a step forward realising Virtual Manufacturing.