Advanced Methods for Computational Collective Intelligence

Nguyên, Ngoc Thanh; Trawiński, Bogdan; Katarzyniak, Radosław; Jo, Geun-Sik

doi:10.1007/978-3-642-23418-7_14

Cited by 4 publications

(9 citation statements)

References 28 publications

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“…Statistical process control charts have been extensively used in several industries to detect abnormal variations in their manufacturing processes in order to prevent defects, reduce scrap and rework, and avoid unnecessary process adjustment [1][2][3]. Literally, an item which does not meet one or more of the required specifications is considered as nonconforming.…”

Section: Introductionmentioning

confidence: 99%

“…In these cases, the recorded data are usually considered as fuzzy data [5][6][7][8]. Hence, the traditional control charts are necessarily extended to adapt to the fuzziness in practical manufacturing processes [3,4,6,9]. As such, this paper uses fuzzy u chart to monitor the nonconformities with an advanced classification approach proposed by Shu et al [10].…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Welding Discontinuities with Fuzzy Control Chart

Shu

Chiu

Nguyễn

et al. 2014

AMR

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Over the past few years, several extensions of the traditional control charts have been proposed to adapt to the fuzziness inevitably existing in practical manufacturing processes. This paper proposed a fuzzy u chart to monitor the nonconformity of industrial products. A thorough evaluation of manufacturing conditions was also developed with certain categorization rules which can classify the process into four consequences, including in-control, rather in-control, rather out-of-control and out-of-control. With an empirical study of monitoring the weld discontinuities, our proposed approach can efficiently provide more sufficient and justified classification results in contrast with the traditional online processes monitoring methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monitoring Welding Discontinuities with Fuzzy Control Chart

Shu

Chiu

Nguyễn

et al. 2014

AMR

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“…Nowadays, Shewhart-type control charts are extensively used in monitoring and examining manufacturing processes because they are specially able to detect process shifts, identify abnormal variations in on-line manufacturing process, and provide not only useful diagnostic information but also the key process parameters in order to prevent defects, reduce scrap and rework, and avoid unnecessary process adjustment [1][2][3]. As a matter of fact, the construction of the conventional control charts is based on random precise data collected from a key quality characteristic.…”

Section: Introductionmentioning

confidence: 99%

“…As a matter of fact, the construction of the conventional control charts is based on random precise data collected from a key quality characteristic. However, in practice, there are several quality characteristics that cannot be recorded or measured precisely; for instance, surface roughness [3] and coating thickness [4]. Furthermore, human subjectivity and perception usually affacts the decision-making [5].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Manufacturing Processes with a Novel Approach of Making Fuzzy Judgement

Shu

Huang

Nguyễn

et al. 2013

AMM

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To monitor manufacturing processes with the inevitable fuzzy (imprecise) information, several extensions of the traditional Shewhart-type control charts have been proposed over the past few years. Among them, fuzzy and s charts have been recently developed with certain categorization rules which can discriminate conditions of a manufacturing process into four consequences, including in-control, rather in-control, rather out-of-control and out-of-control. However, weakness of the fuzzy-number evaluation for the classification rules has been found. Thus, in this paper, an improved fuzzy-number ranking approach for the fuzzy charts is proposed to provide more sufficient and justified classification results for monitoring the online manufacturing processes.

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“…Piecewise Linear Representation (PLR) [19] methods are based on the approximation of each segment in the form of straight lines and include the perceptually important points (PIP), Piecewise Aggregate Approximation (PAA) [20], and the turning point (TP) method [21].…”

Section: Intervalsmentioning

confidence: 99%

BEATS: Blocks of Eigenvalues Algorithm for Time series Segmentation

González-Vidal

Barnaghi

Skarmeta

2018

IEEE Trans. Knowl. Data Eng.

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Abstract-The massive collection of data via emerging technologies like the Internet of Things (IoT) requires finding optimal ways to reduce the observations in the time series analysis domain. The IoT time series require aggregation methods that can preserve and represent the key characteristics of the data. In this paper, we propose a segmentation algorithm that adapts to unannounced mutations of the data (i.e. data drifts). The algorithm splits the data streams into blocks and groups them in square matrices, computes the Discrete Cosine Transform (DCT) and quantizes them. The key information is contained in the upper-left part of the resulting matrix. We extract this sub-matrix, compute the modulus of its eigenvalues and remove duplicates. The algorithm, called BEATS, is designed to tackle dynamic IoT streams, whose distribution changes over time. We implement experiments with six datasets combining real, synthetic, real-world data, and data with drifts. Compared to other segmentation methods like Symbolic Aggregate approXimation (SAX), BEATS shows significant improvements. Trying it with classification and clustering algorithms it provides efficient results. BEATS is an effective mechanism to work with dynamic and multi-variate data, making it suitable for IoT data sources. The datasets, code of the algorithm and the analysis results can be accessed publicly at: https://github.com/auroragonzalez/BEATS.

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Advanced Methods for Computational Collective Intelligence

Cited by 4 publications

References 28 publications

Monitoring Welding Discontinuities with Fuzzy Control Chart

Monitoring Welding Discontinuities with Fuzzy Control Chart

Evaluating Manufacturing Processes with a Novel Approach of Making Fuzzy Judgement

BEATS: Blocks of Eigenvalues Algorithm for Time series Segmentation

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