Neural network classifier of time series: A case study of symbolic representation preprocessing for Control Chart Patterns

Lavangnananda, Kittichai; Sawasdimongkol, P.

doi:10.1109/icnc.2012.6234651

Cited by 11 publications

(4 citation statements)

References 17 publications

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“…Therefore, we aim to use more symbolic embedding approaches, like Symbolic Aggregate approXimation (SAX) (Lin et al 2003) or Symbolic Fourier Approximation (SFA) (Schäfer and Högqvist 2012). SAX, in particular, has been successfully applied as a symbolic embedding for deep learning (Lavangnananda and Sawasdimongkol 2012;Schwenke and Atzmueller 2021c;2021b;Criado-Ramón, Ruiz, and Pegalajar 2022;Tabassum, Menon, and Jastrzebska 2022), via a more human related representation, cf. Atzmueller et al (2017); Ramirez, Wimmer, and Atzmueller (2019).…”

Section: Symbolic Time Series Embeddingsmentioning

confidence: 99%

Identifying Informative Nodes in Attributed Spatial Sensor Networks Using Attention for Symbolic Abstraction in a GNN-based Modeling Approach

Schwenke

Bloemheuvel²,

Atzmueller

2023

FLAIRS

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Modeling complex data, e.g. time series as well as network-based data, is a prominent area of research. In this paper, we focus on a combination of both, analyzing network-based spatial sensor data which is attributed with high frequency time series information. We apply a symbolic representation and an attention-based local abstraction approach, to enhance interpretability on the respective complex high frequency time series data. For this, we aim at identifying informative measurements captured by the respective nodes of the sensor network. To do so, we demonstrate the efficacy of the Symbolic Fourier Approximation (SFA) and the attention-based symbolic abstraction method to localize relevant node sensor-information, by using a transformer architecture as an encoder for a graph neural network. In our experiments, we compare two seismological datasets to their previous state-of-the-art model, demonstrating the advantages and benefits of our presented approach.

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Section: Symbolic Time Series Embeddingsmentioning

confidence: 99%

Identifying Informative Nodes in Attributed Spatial Sensor Networks Using Attention for Symbolic Abstraction in a GNN-based Modeling Approach

Schwenke

Bloemheuvel²,

Atzmueller

2023

FLAIRS

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“…[26] developed a NN classifier for CCPs by Generalized Autoregressive Conditional Heteroskedasticity (GARH) Model. [27] Applies multivariate exponentially weighted moving average (MEWMA) and NNs for identifying the start point of the variations. [28] presents combination of intelligent model of ANN and support vector machine (SVM) learning methods for CCP recognition.…”

Section: Application Of Anns In Spcmentioning

confidence: 99%

A Hybrid Expert Decision Support System Based on Artificial Neural Networks in Process Control of Plaster Production – An Industry 4.0 Perspective

Ramezani¹,

Jassbi²

2017

IFIP Advances in Information and Communication Technology

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Emerging technologies could affect future of factories and smartness is the main trend to receive that points. Quality was important and will be crucial in future but the question is how to build Smart Systems to guaranty quality in workshop level. This is an important challenge in Industry 4.0 paradigm. In this paper the main objective is to present practical solution under the light of Industry 4.0. The aim of this study is to presents propose a Hybrid Expert Decision Support System (EDSS) model, which integrates Neural Network (NN) and Expert System (ES) to detect unnatural CCPs and to estimate the corresponding parameters and starting point of the detected CCP. For this purpose, Learning Vector Quantization (LVQ) and Multi-Layer Perceptron (MLP) networks architecture have been designed to identify unnatural CCPs. Moreover, a rule based ES has been developed for diagnosing causes of process variations and subsequently recommending corrective action. The proposed model was successfully implemented in Construction Plaster producing company to demonstrate the capabilities and applicability of the model.

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“…Common causes are because of the inherent characteristics of the process, and, if they exist, deviations (background noise) are in control [5,6]. However, the most crucial ability of control charts is detecting various types of patterns consisting of a series of consecutive points that are observed on these charts, which reflects fluctuations in the process [7]. The control chart patterns (CCPs) are generally divided into natural and unnatural patterns.…”

Section: Introductionmentioning

confidence: 99%

“…Processes 2020, 8, x FOR PEER REVIEW 2 of 34 consisting of a series of consecutive points that are observed on these charts, which reflects fluctuations in the process [7]. The control chart patterns (CCPs) are generally divided into natural and unnatural patterns.…”

Section: Introductionmentioning

confidence: 99%

Quality 4.0 in Action: Smart Hybrid Fault Diagnosis System in Plaster Production

Ramezani

Jassbi

2020

Processes

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Industry 4.0 (I4.0) represents the Fourth Industrial Revolution in manufacturing, expressing the digital transformation of industrial companies employing emerging technologies. Factories of the future will enjoy hybrid solutions, while quality is the heart of all manufacturing systems regardless of the type of production and products. Quality 4.0 is a branch of I4.0 with the aim of boosting quality by employing smart solutions and intelligent algorithms. There are many conceptual frameworks and models, while the main challenge is to have the experience of Quality 4.0 in action at the workshop level. In this paper, a hybrid model based on a neural network (NN) and expert system (ES) is proposed for dealing with control chart patterns (CCPs). The idea is to have, instead of a passive descriptive model, a smart predictive model to recommend corrective actions. A construction plaster-producing company was used to present and evaluate the advantages of this novel approach, while the result shows the competency and eligibility of Quality 4.0 in action.

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Neural network classifier of time series: A case study of symbolic representation preprocessing for Control Chart Patterns

Cited by 11 publications

References 17 publications

Identifying Informative Nodes in Attributed Spatial Sensor Networks Using Attention for Symbolic Abstraction in a GNN-based Modeling Approach

Identifying Informative Nodes in Attributed Spatial Sensor Networks Using Attention for Symbolic Abstraction in a GNN-based Modeling Approach

A Hybrid Expert Decision Support System Based on Artificial Neural Networks in Process Control of Plaster Production – An Industry 4.0 Perspective

Quality 4.0 in Action: Smart Hybrid Fault Diagnosis System in Plaster Production

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