Advanced Data Processing in the Business Network System

Ritter, Daniel

doi:10.7763/ijmlc.2013.v3.300

Cited by 3 publications

(3 citation statements)

References 14 publications

(27 reference statements)

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“…9,10 At present, big data technology has been applied to some specific production scenarios, such as scheduling, process optimization, fault tracking, process optimization, etc., most of them are machine learning, neural network, data mining, etc. 11 In the actual manufacturing process, most of the data collected by RFID is time series data, 12,13 which has a strong time correlation.…”

Section: Introductionmentioning

confidence: 99%

“…At present, big data technology has been applied to some specific production scenarios, such as scheduling, process optimization, fault tracking, process optimization, etc., most of them are machine learning, neural network, data mining, etc. 11 In the actual manufacturing process, most of the data collected by RFID is time series data, 12,13 which has a strong time correlation. Traditional machine learning and deep learning methods cannot effectively use the time correlation of data, while LSTM is widely used in machine translation, dialogue generation, coding and decoding technology, precisely because it is very suitable for dealing with the time series highly related problems.…”

Section: Introductionmentioning

confidence: 99%

“…Deep LSTM can stack multiple LSTM hidden layers, and the output sequence of the previous layer is the input sequence of the next layer, so that the whole model has more powerful processing power. 13…”

Section: Introduction To Lstm Model and Gru Modelmentioning

confidence: 99%

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The real-time big data processing method based on LSTM or GRU for the smart job shop production process

Wang

Zhu

et al. 2020

Journal of Algorithms & Computational Technology

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With the wide application of intelligent sensors and internet of things (IoT) in the smart job shop, a large number of real-time production data is collected. Accurate analysis of the collected data can help producers to make effective decisions. Compared with the traditional data processing methods, artificial intelligence, as the main big data analysis method, is more and more applied to the manufacturing industry. However, the ability of different AI models to process real-time data of smart job shop production is also different. Based on this, a real-time big data processing method for the job shop production process based on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) is proposed. This method uses the historical production data extracted by the IoT job shop as the original data set, and after data preprocessing, uses the LSTM and GRU model to train and predict the real-time data of the job shop. Through the description and implementation of the model, it is compared with KNN, DT and traditional neural network model. The results show that in the real-time big data processing of production process, the performance of the LSTM and GRU models is superior to the traditional neural network, K nearest neighbor (KNN), decision tree (DT). When the performance is similar to LSTM, the training time of GRU is much lower than LSTM model.

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