Fuzzy Inference System for Data Processing in Industrial Applications

Cateni, Silvia; Colla, Valentina

doi:10.5772/35713

Cited by 4 publications

(3 citation statements)

References 85 publications

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“…This approach is considered a less challenging approach for detecting fatigue. For complicated detection system a system/algorithm known as fuzzy inference systems (FIS) 16 , 40 is preferred over rule‐based approach. It uses fuzzy rules and membership functions to find an accurate outcome.…”

Section: Fatigue Detection Methodsmentioning

confidence: 99%

A comprehensive review of approaches to detect fatigue using machine learning techniques

Hooda

Sandeep

Shah

2022

Chronic Diseases and Translational Medicine

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In the past decades, there have been numerous advancements in the field of technology. This has led to many scientific breakthroughs in the field of medical sciences. In this, rapidly transforming world we are having a difficult time and the problem of fatigue is becoming prevalent. So, this study aimed to understand what is fatigue, its repercussions, and techniques to detect it using machine learning (ML) approaches. This paper introduces, discusses methods and recent advancements in the field of fatigue detection. Further, we categorized the methods that can be used to detect fatigue into four diverse groups, that is, mathematical models, rule‐based implementation, ML, and deep learning. This study presents, compares, and contrasts various algorithms to find the most promising approach that can be used for the detection of fatigue. Finally, the paper discusses the possible areas for improvement.

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Section: Fatigue Detection Methodsmentioning

confidence: 99%

A comprehensive review of approaches to detect fatigue using machine learning techniques

Hooda

Sandeep

Shah

2022

Chronic Diseases and Translational Medicine

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“…The fuzzy inference was used because it has several advantages over the conventional methods in the boundary calculation of image, for example, Sobel's method, Prewitt's method, and Robert's method [ 8 ]. The fuzzy inference can handle problems with imprecise, noisy, inconsistent, and incomplete data set [ 9 ]. IVUS images often have noise and the plaque boundaries are usually missing in several areas.…”

Section: Introductionmentioning

confidence: 99%

Coronary Plaque Boundary Enhancement in IVUS Image by Using a Modified Perona-Malik Diffusion Filter

Anam

Uchino

Suetake

2014

International Journal of Biomedical Imaging

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We propose a modified Perona-Malik diffusion (PMD) filter to enhance a coronary plaque boundary by considering the conditions peculiar to an intravascular ultrasound (IVUS) image. The IVUS image is commonly used for a diagnosis of acute coronary syndrome (ACS). The IVUS image is however very grainy due to heavy speckle noise. When the normal PMD filter is applied for speckle noise reduction in the IVUS image, the coronary plaque boundary becomes vague. For this problem, we propose a modified PMD filter which is designed in special reference to the coronary plaque boundary detection. It can then not only reduce the speckle noise but also enhance clearly the coronary plaque boundary. After applying the modified PMD filter to the IVUS image, the coronary plaque boundaries are successfully detected further by applying the Takagi-Sugeno fuzzy model. The accuracy of the proposed method has been confirmed numerically by the experiments.

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“…The raw data are treated using artificial intelligent techniques and fuzzy system theory, a research field well known by some authors and successfully used in the past. [5][6][7] The paper depicts the design and the installation of a novel monitoring system providing the operator with an on-line control of the combustion process by pointing out the BTPs positions and the permeability of the sinter bed. Moreover, the possibility is provided to affect the permeability of the different areas of the bed through the opening level of the charging gates, by thus achieving an indirect control of the BTPs which allows to improve the stability and productivity of the sintering process.…”

Section: Introductionmentioning

confidence: 99%

Advanced monitoring system of sinter plant

Vannocci

Colla

Pulito³

et al. 2014

Ironmaking & Steelmaking

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A correct management of the sinter plant is crucial for the productivity of an integrated steelwork, as the sinter quality has a direct and considerable impact of the blast furnace productivity and efficiency. To this aim, it is of utmost importance to continuously monitor the sintering process in order to preserve its stability as well as the homogeneity of the combustion along the entire length of the sinter bed. In particular, the permeability of the sinter mix once it lays on the strand is a crucial factor to monitor in the different areas of the bed together with the temperature distribution on the final part of the strand, just before the material discharge in the crusher. The charging gates, on the other hand, are the most natural mean to affect permeability itself, as their opening levels affect the distribution of the material on the strand. Here an innovative monitoring system is presented, which have been installed and tested at ILVA Taranto Works and integrates a series of measuring devices distributed on different zones of the machine. All the information collected by the sensing system is jointly exploited in order to obtain a clear and updated picture of the ongoing process, by providing a valuable support to plant engineers and technicians managing the sinter machine both for standard operations and for programming maintenance interventions.

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Fuzzy Inference System for Data Processing in Industrial Applications

Cited by 4 publications

References 85 publications

A comprehensive review of approaches to detect fatigue using machine learning techniques

A comprehensive review of approaches to detect fatigue using machine learning techniques

Coronary Plaque Boundary Enhancement in IVUS Image by Using a Modified Perona-Malik Diffusion Filter

Advanced monitoring system of sinter plant

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