An hybrid detection system of control chart patterns using cascaded SVM and neural network–based detector

Das, Prasun; Banerjee, Indranil

doi:10.1007/s00521-010-0443-z

Cited by 16 publications

(3 citation statements)

References 15 publications

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“…Cao et al [20] proposed a parallel SVM algorithm based on the distributed memory system. Das et al [21] proposed Cascade SVM based on cascade and feedback architecture. In the initial stage, the support vector machine randomly divides the whole training set into even subsets.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Parallel Support Vector Machine Based on Spark Big Data Platform

Yao

2021

Scientific Programming

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With the development of cloud computing and distributed cluster technology, the concept of big data has been expanded and extended in terms of capacity and value, and machine learning technology has also received unprecedented attention in recent years. Traditional machine learning algorithms cannot solve the problem of effective parallelization, so a parallelization support vector machine based on Spark big data platform is proposed. Firstly, the big data platform is designed with Lambda architecture, which is divided into three layers: Batch Layer, Serving Layer, and Speed Layer. Secondly, in order to improve the training efficiency of support vector machines on large-scale data, when merging two support vector machines, the “special points” other than support vectors are considered, that is, the points where the nonsupport vectors in one subset violate the training results of the other subset, and a cross-validation merging algorithm is proposed. Then, a parallelized support vector machine based on cross-validation is proposed, and the parallelization process of the support vector machine is realized on the Spark platform. Finally, experiments on different datasets verify the effectiveness and stability of the proposed method. Experimental results show that the proposed parallelized support vector machine has outstanding performance in speed-up ratio, training time, and prediction accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Research on Parallel Support Vector Machine Based on Spark Big Data Platform

Yao

2021

Scientific Programming

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“…In the domain of SPC, fast and accurate control, as well as observing the variation of quality characteristics and, consequently, recognition of unnatural patterns, is the primary purpose of each fault detection and diagnosis system. There are numerous studies in this field on CCP recognition that used different machine learning algorithms and other intelligent approaches, namely, K-nearest neighbors (KNN), decision trees (DT), NN-based models, ES-based models, support vector machine (SVM), wavelet-based models, and fuzzy logic [14][15][16]. These approaches aim at extracting meaningful information from a large amount of data to detect instabilities in the process with minimal time and cost and maximum accuracy [17].…”

Section: Background and Problem Statementmentioning

confidence: 99%

“…-Low speed and the long run time of the system. [5,14,21,26] The literature review shows that ANNs and ESs are the most widely used approaches, being easier to understand and implement and having higher performance in comparison to other CCP recognition approaches mentioned above. NNs are suitable for SPC as they are good at classification and pattern recognition, and they are able to handle the noisy measurements with no requirement for the provision of explicit rules regarding the monitored data [20].…”

Section: Background and Problem Statementmentioning

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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Support Vector Machines: A Review and Applications in Statistical Process Monitoring

Apsemidis¹,

Psarakis²

2020

Data Analysis and Applications 3

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An hybrid detection system of control chart patterns using cascaded SVM and neural network–based detector

Cited by 16 publications

References 15 publications

Research on Parallel Support Vector Machine Based on Spark Big Data Platform

Research on Parallel Support Vector Machine Based on Spark Big Data Platform

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

Support Vector Machines: A Review and Applications in Statistical Process Monitoring

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