Wyniki wyszukiwania - BazTech

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Approach to predict product quality considering current customers’ expectations

Siwiec Dominika, Pacana Andrzej, Bednárová Lucia

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2022

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z. 155

461--472

EN

Purpose: The purpose was to develop an approach to predict product quality considering current customers' expectations. Design/methodology/approach: The approach includes integrated techniques, i.e.: SMART(-ER) method, a questionnaire with the Likert scale, brainstorming (B&M), WSM method, and Naïve Bayes Classifier. This approach refers to obtaining customers' expectations for satisfaction from the current quality of products and the importance of these criteria. Based on the satisfaction of customers, the quality of the product was estimated and classified. Then, the quality of the product was predicted for current customers. Findings: It was shown that it is possible to predict product quality based on current customer expectations, and so based on the current existing product. Research limitations/implications: The proposed approach does not include the possibilities of determining the expected quality of the product. The approach focuses on predicting customers' satisfaction with the current quality of the product. Therefore, if there is a need for improvement actions, further analyzes should be carried out to determine which criteria should be modified and how. Practical implications: The presented approach can be used for any product. Therefore, it is a useful tool for any kind of organization, which strives to meet customer satisfaction. Despite the possibility to predict the quality of the product, the proposed approach can indicate at an early stage to the organization that it is necessary to make improvement actions. Social implications: It is possible to reduce the waste of resources by predicting that improvement actions are necessary. Moreover, the approach supports an entity (e.g., expert, enterprise, interested parties) in predicting current customers' satisfaction. Originality/value: Originality is predicting product quality based on current customers' expectations. A new combination of quality management techniques, decision support, and machine learning was implemented.

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Application of Time-Series Analysis for Predicting Defects in Continuous Steel Casting Process

Rodziewicz A., Perzyk M.

Archives of Foundry Engineering

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2016

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Vol. 16, iss. 4

125--130

EN

The purpose of this paper was testing suitability of the time-series analysis for quality control of the continuous steel casting process in production conditions. The analysis was carried out on industrial data collected in one of Polish steel plants. The production data concerned defective fractions of billets obtained in the process. The procedure of the industrial data preparation is presented. The computations for the time-series analysis were carried out in two ways, both using the authors’ own software. The first one, applied to the real numbers type of the data has a wide range of capabilities, including not only prediction of the future values but also detection of important periodicity in data. In the second approach the data were assumed in a binary (categorical) form, i.e. the every heat(melt) was labeled as ‘Good’ or ‘Defective’. The naïve Bayesian classifier was used for predicting the successive values. The most interesting results of the analysis include good prediction accuracies obtained by both methodologies, the crucial influence of the last preceding point on the predicted result for the real data time-series analysis as well as obtaining an information about the type of misclassification for binary data. The possibility of prediction of the future values can be used by engineering or operational staff with an expert knowledge to decrease fraction of defective products by taking appropriate action when the forthcoming period is identified as critical.

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Classification algorithms to identify changes in resistance

Duraj A., Korzeniewska E., Krawczyk A.

Przegląd Elektrotechniczny

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2015

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R. 91, nr 12

80-82

EN

In the article the basic method for measuring the resistance of medical electrodes, made based on a thin conductive layer formed during the PVD process, is described. The authors also briefly characterized two algorithms for data classification: k-nearest neighbors and Bayes classifier, which were used as algorithms to detect changes in the electrode resistance.

PL

W artykule została opisana podstawowa metoda pomiaru rezystancji elektrod medycznych wykonanych w oparciu o cienkie warstwy przewodzące powstałe w procesie PVD. Scharakteryzowano również krótko dwa algorytmy klasyfikacji danych: algorytm k najbliższych sąsiadów oraz klasyfikator bayowski, które zostały wykorzystane jako algorytmy identyfikacji zmian rezystancji elektrod.

4

Learning the naive Bayes classifier with optimization models

Taheri S., Mammadov M.

International Journal of Applied Mathematics and Computer Science

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2013

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Vol. 23, no. 4

787--795

EN

Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are calculated using training data, and then values of these probabilities are used to classify new observations. In this paper, we introduce three novel optimization models for the naive Bayes classifier where both class probabilities and conditional probabilities are considered as variables. The values of these variables are found by solving the corresponding optimization problems. Numerical experiments are conducted on several real world binary classification data sets, where continuous features are discretized by applying three different methods. The performances of these models are compared with the naive Bayes classifier, tree augmented naive Bayes, the SVM, C4.5 and the nearest neighbor classifier. The obtained results demonstrate that the proposed models can significantly improve the performance of the naive Bayes classifier, yet at the same time maintain its simple structure.

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Propozycja mieszanego przetwarzania półstrukturalnego modelu opisu zdarzeń z akcji ratowniczo-gaśniczych Państwowej Straży Pożarnej PSP3

Mirończuk M., Maciak T.

Bezpieczeństwo i Technika Pożarnicza

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2013

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Nr 1

95-106

PL

W opracowaniu przedstawiono aktualnie rozwijane reprezentacje wiedzy i sposoby opisów zdarzeń, dla systemu wnioskowania na podstawie przypadków zdarzeń służb ratowniczych Państwowej Straży Pożarnej PSP. W artykule zaproponowano sposób ich przetwarzania. Przedstawiony sposób bazuje na klasyfikacji i wyszukiwaniu opisów zdarzeń.

EN

This paper describes a review of actual developed knowledge representation and case representation for fire services cases based reasoning system. The article also describes a method of processing the cases of events. This processing method based on classification and information retrieval.

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On Naive Bayes in Speech Recognition

Toth L., Kocsor A., Csirik J.

International Journal of Applied Mathematics and Computer Science

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2005

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Vol. 15, no 2

287-294

EN

The currently dominant speech recognition technology, hidden Markov modeling, has long been criticized for its simplistic assumptions about speech, and especially for the naive Bayes combination rule inherent in it. Many sophisticated alternative models have been suggested over the last decade. These, however, have demonstrated only modest improvements and brought no paradigm shift in technology. The goal of this paper is to examine why HMM performs so well in spite of its incorrect bias due to the naive Bayes assumption. To do this we create an algorithmic framework that allows us to experiment with alternative combination schemes and helps us understand the factors that influence recognition performance. From the findings we argue that the bias peculiar to the naive Bayes rule is not really detrimental to phoneme classification performance. Furthermore, it ensures consistent behavior in outlier modeling, allowing efficient management of insertion and deletion errors.

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Modelowanie procesów produkcyjnych za pomocą naiwnego klasyfikatora Bayesa i sztucznych sieci neuronowych

Perzyk M., Biernacki R.

Informatyka w Technologii Materiałów

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2004

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T. 4, Nr 3

98-104

PL

Badania porównawcze przeprowadzone w niniejszej pracy pozwoliły na stwierdzenie, że błędy przewidywania naiwnego klasyfikatora Bayesa być mniejsze lub większe od błędów sieci neuronowej. Dla dwóch identycznych symulowanych zbiorów o wyjściu binarnym, różniących się tylko liczebnością, NKB wykazał mniejszy błąd (% mylnych kategorii) niż SSN w przypadku zbioru mniej licznego. Współczynnik istotności względnej wielkości weściowych związany z NKB w obecnej postaci daje mało precyzyjne wyniki dla sygnałów o mniejszym znaczeniu. Dla wyjścia typu binarnego NKB dał wyraźnie lepszy rezultat w postaci identyfikacji sygnału zdecydowanie wyróżniającego się, niż SSN. Należy stwierdzić, że główną zaletą NKB w stosunku do SSN jest jednoznaczność tego modelu, a także prostota jego stosowania. Sadzić można także, że NKB jest mniej wymagający, jeśli chodzi o liczebność zbioru uczącego (np. nie wymaga on zbioru weryfikującego przy uczeniu). Wydaje się, że NKB może stanowić pożyteczny dla zastosowań przemysłowych system uczący się, w niektórych przypadkach lepszy od SSN. Celowe są jednak dalsze, systematyczne badania, zwłaszcza nad rozszerzeniem możliwości interpretacji wyników obu systemów, w tym analizy istotności wielkości wejściowych. Obecna praca pozwoliła jedynie na zwrócenie uwagi na potencjalne możliwości zastosowań NKB do modelowania procesów przemysłowych, jako alternatywnego wobec SSN systemu uczącego się oraz zasygnalizowanie szeregu problemów z tym związanych.

EN

Modeling qualities of two types learning systems are compared: naive Bayesian classifier (NBC) and artificial neural networks (ANN), based on prediction errors and relevant importance factors of input signals. Simulated and real industrial data were used. It was found that NBC can be an effective and a better tool in some applications, compared to ANN.