Region-Based Convolutional Neural Network for Segmenting Text in Epigraphical Images

Preethi, P.; Mamatha, H. R.

doi:10.47852/bonviewaia2202293

Cited by 48 publications

(10 citation statements)

References 18 publications

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“…20,21 LSTM is one of the recurrent neural networks (RNN), and its internal recurrent network structure is different but similar to that of RNN. 22,23 Since the LSTM needs to be used to solve the long-term dependency problem of RNN, each module of the internal network structure has a different structure. 24 In Figure 1, LSTM mainly contains the tuple and the horizontal line through the tuple.…”

Section: Design Of Deep Learning-based Feature Extraction Methodsmentioning

confidence: 99%

A deep learning model of dance generation for young children based on music rhythm and beat

Kong

2024

Concurrency and Computation

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SummaryWith the development of technology, research related to music‐based dance generation models has been increasing. Some of the studies have applied algorithms to dance generation models, but these algorithms suffer from problems such as the inability to make an exact match between music and dance. To solve these problems, the research innovatively proposes a deep learning toddler dance generation model based on music rhythm and beat to extract music and dance features respectively. In addition, the study generates smooth dances through a generator module, improves the match between the dances and music generated by the model through a discriminator, and enhances the representativeness of audio features through a self‐encoder module. Finally, the study completes the validation of the model by comparing the loss functions and other aspects. Results demonstrate that this model has the smallest loss value of 17.58, and that the model generates a better match between dance and music for different music, with values of 8.9, 8, and 7.4. Compared with the comparison model, the study's model for generating dances for young children has better results in terms of dance generation. The study solves the problem that previous algorithms cannot make an exact match between music and dance, and has implications for fields such as cross‐modal generation and games.

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Section: Design Of Deep Learning-based Feature Extraction Methodsmentioning

confidence: 99%

A deep learning model of dance generation for young children based on music rhythm and beat

Kong

2024

Concurrency and Computation

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“…However certain evaluation indicators need to be quantified to be visualized, and the quantified indicators can be found in Eq. ( 13) [19].…”

Section:  mentioning

confidence: 99%

Network Security Situation Prediction Technology Based on Fusion of Knowledge Graph

Luo

2024

IJACSA

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It is difficult to accurately reflect different network attack events in real time, which leads to poor performance in predicting network security situations. A knowledge graph-based entity recognition model and entity relationship extraction model was developed for enhancing the reliability and processing efficiency of secure data. Then a knowledge graph-based situational assessment method was introduced, and a network security situational prediction model based on self-attention mechanism and gated recurrent unit was constructed. The study's results showed that the constructed prediction model achieved stable mean square error values of approximately 0.0127 and 0.0136 after being trained on the NSL-KDD and CICIDS2017 datasets for 678 and 589 iterations, respectively. The mean square error value was lower due to fewer training iterations compared to other prediction models. The model was embedded into the information security system of an actual Internet company, and the detection accuracy of the number of network attacks was more than 95%. The results of our study indicate that the method used in the study can accurately predict the network security situation and provide technical support for predicting network information security of the same type.

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“…The FPN is a crucial component in the YOLOX-s model, responsible for merging feature maps of different scales to enhance the model's multi-scale detection capabilities. In FPN, different scale feature maps are fused together through upsampling and downsampling operations (Preethi and Mamatha, 2023). Let the scale factor for the upsampling operation be denoted as S; then the upsampled feature map can be represented as shown in Eq.…”

Section: Construction Of Weld Seam Defect Detection Model Based On Yo...mentioning

confidence: 99%

Welding robot automation technology based on digital twin

Kang,

Chen

2024

Front. Mech. Eng.

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In the era of intelligence and automation, robots play a significant role in the field of automated welding, enhancing efficiency and precision. However, challenges persist in scenarios demanding complexity and higher precision, such as low welding planning efficiency and inaccurate weld seam defect detection. Therefore, based on digital twin technology and kernel correlation filtering algorithm, a welding tracking model is proposed. Firstly, the kernel correlation filtering algorithm is used to train the filter on the first frame of the collected image, determine the position of image features in the region, extract histogram features of image blocks, and then train the filter using ridge regression to achieve welding trajectory tracking. Additionally, an intelligent weld seam detection model is introduced, employing a backbone feature network for feature extraction, feature fusion through a feature pyramid, and quality detection of weld seams through head classification. During testing of the tracking model, the maximum tracking error is −0.232 mm, with an average absolute tracking error of 0.08 mm, outperforming other models. Comparatively, in tracking accuracy, the proposed model exhibits the fastest convergence with a precision rate of 0.845, surpassing other models. In weld seam detection, the proposed model excels with a detection accuracy of 97.35% and minimal performance loss at 0.023. In weld seam quality and melt depth error detection, the proposed model achieves errors within the range of −0.06 mm, outperforming the other two models. These results highlight the outstanding detection capabilities of the proposed model. The research findings will serve as technical references for the development of automated welding robots and welding quality inspection.

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Region-Based Convolutional Neural Network for Segmenting Text in Epigraphical Images

Cited by 48 publications

References 18 publications

A deep learning model of dance generation for young children based on music rhythm and beat

A deep learning model of dance generation for young children based on music rhythm and beat

Network Security Situation Prediction Technology Based on Fusion of Knowledge Graph

Welding robot automation technology based on digital twin

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