Product Quality Detection through Manufacturing Process Based on Sequential Patterns Considering Deep Semantic Learning and Process Rules

Yao, Liguo; Huang, Haisong; Chen, Shih-Huan

doi:10.3390/pr8070751

Cited by 8 publications

(5 citation statements)

References 36 publications

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“…Compared with LSA model, the LDA [28,29] model focuses on constructing the mapping relationship between the above three variable spaces from the perspective of probability distribution, to avoid the huge amount of matrix operation caused by singular value decomposition operation. By introducing the relevant theories of Dirichlet distribution and multinomial distribution [30,31], the LDA model can carry out latent semantic mining from the perspective of probability distribution [32][33][34].…”

Section: Literature Reviewmentioning

confidence: 99%

A Study of Text Vectorization Method Combining Topic Model and Transfer Learning

Yang

Cui

et al. 2022

Processes

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With the development of Internet cloud technology, the scale of data is expanding. Traditional processing methods find it difficult to deal with the problem of information extraction of big data. Therefore, it is necessary to use machine-learning-assisted intelligent processing to extract information from data in order to solve the optimization problem in complex systems. There are many forms of data storage. Among them, text data is an important data type that directly reflects semantic information. Text vectorization is an important concept in natural language processing tasks. Because text data can not be directly used for model parameter training, it is necessary to vectorize the original text data and make it numerical, and then the feature extraction operation can be carried out. The traditional text digitization method is often realized by constructing a bag of words, but the vector generated by this method can not reflect the semantic relationship between words, and it also easily causes the problems of data sparsity and dimension explosion. Therefore, this paper proposes a text vectorization method combining a topic model and transfer learning. Firstly, the topic model is selected to model the text data and extract its keywords, to grasp the main information of the text data. Then, with the help of the bidirectional encoder representations from transformers (BERT) model, which belongs to the pretrained model, model transfer learning is carried out to generate vectors, which are applied to the calculation of similarity between texts. By setting up a comparative experiment, this method is compared with the traditional vectorization method. The experimental results show that the vector generated by the topic-modeling- and transfer-learning-based text vectorization (TTTV) proposed in this paper can obtain better results when calculating the similarity between texts with the same topic, which means that it can more accurately judge whether the contents of the given two texts belong to the same topic.

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Section: Literature Reviewmentioning

confidence: 99%

A Study of Text Vectorization Method Combining Topic Model and Transfer Learning

Yang

Cui

et al. 2022

Processes

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“…The emergence of pre-training has accelerated the development of word representation, and solve the problem of one word has multi-meaning. ELMo [18] is a pretrained contextual word embedding model, which uses a BiLSTM language model consisting of a forward and a backward language model. The goal of GPT (Generative Pre-Training) [19] is to learn a general representation that can be applied to many tasks.…”

Section: Word Representation Technologiesmentioning

confidence: 99%

“…In natural language processing problems, each word is influenced by its front and back words, so the textual context must be taken into account [18]. Therefore, in this paper, BiLSTM is used for Here, σ is the activation function sigmoid; ⊗ is a point multiplication operation; tanh is a hyperbolic tangent activation function; x t is the unit input, i t , f t , o t are the input gate, forget gate, and output gate at moment t; w, b are the weight matrix and bias vector of the input gate, forget gate, and output gate; c t is the state at moment t; and h t is the output at moment t.…”

Section: Feature Extraction Based On Attbilstmmentioning

confidence: 99%

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Fine-Grained Mechanical Chinese Named Entity Recognition Based on ALBERT-AttBiLSTM-CRF and Transfer Learning

et al. 2020

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Manufacturing text often exists as unlabeled data; the entity is fine-grained and the extraction is difficult. The above problems mean that the manufacturing industry knowledge utilization rate is low. This paper proposes a novel Chinese fine-grained NER (named entity recognition) method based on symmetry lightweight deep multinetwork collaboration (ALBERT-AttBiLSTM-CRF) and model transfer considering active learning (MTAL) to research fine-grained named entity recognition of a few labeled Chinese textual data types. The method is divided into two stages. In the first stage, the ALBERT-AttBiLSTM-CRF was applied for verification in the CLUENER2020 dataset (Public dataset) to get a pretrained model; the experiments show that the model obtains an F1 score of 0.8962, which is better than the best baseline algorithm, an improvement of 9.2%. In the second stage, the pretrained model was transferred into the Manufacturing-NER dataset (our dataset), and we used the active learning strategy to optimize the model effect. The final F1 result of Manufacturing-NER was 0.8931 after the model transfer (it was higher than 0.8576 before the model transfer); so, this method represents an improvement of 3.55%. Our method effectively transfers the existing knowledge from public source data to scientific target data, solving the problem of named entity recognition with scarce labeled domain data, and proves its effectiveness.

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“…At present, the target detection algorithm with a CNN as the backbone network has made major breakthroughs in various fields, and the target detection algorithm based on deep learning is also the closest task to product quality monitoring in the traditional sense. Structurally, this detection method can be divided into a two-stage network represented by the faster-region-based CNN (RCNN) [16] and a one-stage network represented by the You Only Look Once (YOLO) series [17][18][19][20] and the single-shot detector (SSD) [21]. The former first trains and generates proposal boxes that may contain the target and then further obtains the detection result, and the latter directly uses the features extracted from the backbone to predict the target category and location.…”

Section: Introductionmentioning

confidence: 99%

Research on a Product Quality Monitoring Method Based on Multi Scale PP-YOLO

Huang

Chen

et al. 2021

IEEE Access

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To monitor product quality in the production process in real time, this thesis proposes a quality monitoring model based on PaddlePaddle You Only Look Once (PP-YOLO). First, in the preprocessing stage, the data enhancement method and the K-means++ method are used to improve the robustness of the algorithm, and the generated anchor box can screen more refined features earlier. Second, ResNet50-vd with the deformable convolution idea is selected as the backbone of the detection model, the feature pyramid network structure and the composition of the loss function are improved, and the feature learning ability of the model is enhanced to enable it to detect multiple scales of defects. Finally, pruning is performed on the basis of the trained model to reduce the number of model parameters so that it can be deployed in industrial scenarios with limited hardware conditions. Experimental results show that the proposed quality monitoring model can meet the requirements for detection speed and accuracy in actual production, providing a new concept for the deployment of deep learning models in the industrial field.

show abstract

Product Quality Detection through Manufacturing Process Based on Sequential Patterns Considering Deep Semantic Learning and Process Rules

Cited by 8 publications

References 36 publications

A Study of Text Vectorization Method Combining Topic Model and Transfer Learning

A Study of Text Vectorization Method Combining Topic Model and Transfer Learning

Fine-Grained Mechanical Chinese Named Entity Recognition Based on ALBERT-AttBiLSTM-CRF and Transfer Learning

Research on a Product Quality Monitoring Method Based on Multi Scale PP-YOLO

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