Approximation of Ausferrite Content in the Compacted Graphite Iron with the Use of Combined Techniques of Data Mining

Regulski, K.; Wilk-Kołodziejczyk, D.; Kacprzyk, B.; Gumienny, G.; Rojek, Gabriel; Mrzygłód, B.

doi:10.1515/afe-2017-0102

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…In the PSO model, the solution of each optimization problem is the state of a ''particle'' in the search space. Each particle has a fitness value (fitness value), determined by the optimized function and a velocity that determines the direction and distance of their flight [24]. Particles can be dynamically adjusted according to their own flight experience and that of their peers, which can be said to update themselves by tracking two positions [25].…”

Section: Improved Pso Algorithm a Particle Swarm Optimizationmentioning

confidence: 99%

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Research on Anomaly Detection in Massive Multimedia Data Transmission Network Based on Improved PSO Algorithm

Guo

2020

IEEE Access

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With the development of computer network technology and the expansion of network system, sensitive data is facing the threat of hacker attack. Intrusion detection is an active network security defense measure, which is an attempt to invade, an ongoing intrusion or an intrusion that has occurred to identify the process. At present, the detection rate of intrusion detection method is low, the false alarm rate and false alarm rate is high, and the real-time performance is poor. It needs a large number of or complete data to achieve better detection performance. In this paper, the concept, characteristics, classification, research contents and difficulties of traditional intrusion detection for mass multimedia data transmission network are described. Then, the basic principle of neural network and particle swarm optimization (PSO) algorithm and the basic idea of particle swarm optimization algorithm with quantum (QPSO) behaviour are introduced. It is emphasized that QPSO has better convergence performance than PSO algorithm in global optimization problems. In this paper, the concept, characteristics and structure of neural network are described, and the algorithm and classification of wavelet neural network are introduced. Then taking wavelet neural network (WNN) as the object, using the QPSO algorithm as the training algorithm, the concrete operation process is given. The research work in this paper shows that the performance of neural network trained by QPSO algorithm and improved QPSO algorithm is better than that of other intelligent algorithms such as PSO algorithm and genetic algorithm, and the convergence speed is faster than that of PSO algorithm or GA algorithm. QPSO is a high performance neural network training algorithm, which can play a good role in neural network anomaly detection. INDEX TERMS Anomaly detection, particle swarm optimization, data transmission, wavelet neural network.

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Section: Improved Pso Algorithm a Particle Swarm Optimizationmentioning

confidence: 99%

“…If the maximum and minimum values of variables are X max and X min , respectively. And the actual limits of the network are A max and A min , the variables X can be transformed by the following formula: (24) In which,…”

Section: Experimental Data Preprocessingmentioning

confidence: 99%

Research on Anomaly Detection in Massive Multimedia Data Transmission Network Based on Improved PSO Algorithm

Guo

2020

IEEE Access

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“…Nkonyana et al [14] evaluated the predictive methods based on data ming compare to classical predictive methods. The data mining technique is also used for effects of different parameters in a different process, Wilk-Kołodziejczyk et al [15] used the data mining technique to investigate the process effect on the austempered ductile iron, and Regulski [16] used the data mining technique to explore the formation of ausferrite in compacted graphite iron. Effective parameters in the bondvalence model are also studied by Zheng et al [17] With the help of the data mining technique.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the factors affecting the iron ore pellet FEO index using data mining technique

Nakhaeinejad¹,

Zarei²

2020

j. sist. manaj. ind.

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One of the most critical factors affecting iron pellet quality is the reduction in FeO (Iron Oxide) index in the final product. This study aims to predict factors affecting the FeO (Iron Oxide) of iron pellets and find out the contribution of each factor to reduce the pellets FeO (the ideal amount is between 0.4 to 0.6) using data mining techniques. When the FeO index's value is in the optimal range, the quality and price of pellets are higher. The data used in this study was collected from the pelletizing plant of Gol-E-Gohar in Sirjan, Iran, and the decision tree and regression algorithms are used in this analysis. Forty-five factors that can affect the FeO (Iron Oxide) index of the final product were studied, showing that the Magnesium Oxide and Airflow of the inlet fan of the indurating machine had the greatest impact on the FeO (Iron Oxide) of iron pellets.

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Data Mining Methods for Prediction of Multi-Component Al-Si Alloy Properties Based on Cooling Curves

Regulski

Wilk-Kołodziejczyk

Szymczak

et al. 2019

J. of Materi Eng and Perform

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The paper concerns the mechanical properties of hypoeutectic Al-Si alloy (silumin) with the addition of Cr, Mo, V and W. Changes in microstructure under the impact of these elements result in a change in the mechanical properties. Crystallization of Al-Si alloys determines grain size reduction, which causes a significant increase in their strength properties. Crystallization subjected to modifications through the influence of alloying additives can be described by the cooling curve run. Statistical relationships between the characteristic values of cooling curves and mechanical properties are investigated with data mining techniques of regression, especially regression trees. Such knowledge could provide an ability of a property prediction on the basis of cooling curves in terms of the benefits of a short time of the curve registration. Keywords Al-Si alloy, cooling curves, data mining, decision trees, mechanical properties, regression This article is an invited submission to JMEP selected from presentations at the 73rd World Foundry Congress and has been expanded from the original presentation. 73WFC was held in Krakow, Poland, September 23-27, 2018, and was organized by the World Foundry Organization and Polish FoundrymenÕs Association.

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Approximation of Ausferrite Content in the Compacted Graphite Iron with the Use of Combined Techniques of Data Mining

Cited by 6 publications

References 27 publications

Research on Anomaly Detection in Massive Multimedia Data Transmission Network Based on Improved PSO Algorithm

Research on Anomaly Detection in Massive Multimedia Data Transmission Network Based on Improved PSO Algorithm

Analysis of the factors affecting the iron ore pellet FEO index using data mining technique

Data Mining Methods for Prediction of Multi-Component Al-Si Alloy Properties Based on Cooling Curves

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