An improved method for soft tissue modeling

Tang, Yushan; Liu, Shan; Deng, Yaru; Zhang, Yuhui; Yin, Lirong; Zheng, Wenfeng

doi:10.1016/j.bspc.2020.102367

Cited by 125 publications

(49 citation statements)

References 24 publications

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“…From the pre-processed data, the current study selects the earthquake activity data that meets the conditions of Ms ≥ 2.0 and Ms ≥ 7.0 from 1900 to 2015. The sliding window was set to two years, and a month was considered a sliding step [36][37][38]. Strong earthquakes in the earthquake zone were analyzed by using the R/S method.…”

Section: Analysis Of Earthquake Time Series In the Eurasian Earthquake Zone Using The R/s Methodsmentioning

confidence: 99%

The Nonlinear Time Sequence Analysis in the Alpine-Himalayan Earthquake Zone

Chen

Yan

et al. 2021

E3S Web Conf.

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The characteristics of the earthquake activity in the Eurasian earthquake zone, which is the second largest earthquake zone in the world, was investigated by researchers. The earthquake activity of the Eurasian earthquake zone was analysed in various disciplines, such as earth dynamics, rock mechanics, geology and tectonics. The emergence of fractal theory provided a new direction in exploring the characteristics of the earthquake activity in the Eurasian earthquake zone. This study processed the data on the earthquake activity in the Eurasian earthquake zone by self-similarity method and scaled invariant feature test and used the rescaled range analysis method to analyse the nonlinear time series fractal characteristics of the earthquake activity in the Eurasian earthquake zone. Results show that the time series of earthquake activity in the study area is not an independent Poisson process, which exhibits the characteristics of scale invariance and long-range correlation. Approximately 80% of the H values of the earthquake activity iteratively increase and decrease for moderate earthquakes, which is mainly concentrated during the increasing stage. The time difference of the H value between the two-neighbouring earthquake shows that the H value fluctuates in the active earthquake region and is stationary in the relatively stable region. Strong earthquakes will likely occur in the next few years because the H value fluctuates.

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Section: Analysis Of Earthquake Time Series In the Eurasian Earthquake Zone Using The R/s Methodsmentioning

confidence: 99%

The Nonlinear Time Sequence Analysis in the Alpine-Himalayan Earthquake Zone

Chen

Yan

et al. 2021

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…Natural language reasoning technology is widely used in automatic reasoning [2], machine translation [3], question answering systems [4], and large-scale content analysis. Compared with computer vision and speech recognition technology, natural language reasoning has not reached a high level because of its technical difficulties and complex application scenarios [5,6]. Once the natural language reasoning technology makes a breakthrough and realizes the real barrier-free communication between humans and machines, human life quality will be greatly improved.…”

Section: Introductionmentioning

confidence: 99%

Sentence Representation Method Based on Multi-Layer Semantic Network

2021

Self Cite

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With the development of artificial intelligence, more and more people hope that computers can understand human language through natural language technology, learn to think like human beings, and finally replace human beings to complete the highly difficult tasks with cognitive ability. As the key technology of natural language understanding, sentence representation reasoning technology mainly focuses on the sentence representation method and the reasoning model. Although the performance has been improved, there are still some problems such as incomplete sentence semantic expression, lack of depth of reasoning model, and lack of interpretability of the reasoning process. In this paper, a multi-layer semantic representation network is designed for sentence representation. The multi-attention mechanism obtains the semantic information of different levels of a sentence. The word order information of the sentence is also integrated by adding the relative position mask between words to reduce the uncertainty caused by word order. Finally, the method is verified on the task of text implication recognition and emotion classification. The experimental results show that the multi-layer semantic representation network can promote sentence representation’s accuracy and comprehensiveness.

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“…Most non-deep learning models are based on Bayesian theory. Some researchers [2][3][4][5][6][7][8][9][10][11][12][13] proposed a Bayesian framework, predicting the type of answer to a question and generating an answer. Mateusz et al proposed the multi-world question and answer model in 2014, proposed the DAQUAR data set, and modeled visual question and answer as SWQA model [14].…”

Section: Introductionmentioning

confidence: 99%

“…Based on this, three variant models were constructed 2-VIS+BLSTM, IMG+BOW, and FULL. In 2015, Mateusz et al proposed a neural-image-QA model, which is also known as the Neural query model [6,17]. The feature of this model is that it can generate answers of variable length.…”

Section: Introductionmentioning

confidence: 99%

Improving Visual Reasoning Through Semantic Representation

et al. 2021

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In visual reasoning, the achievement of deep learning significantly improved the accuracy of results. Image features are primarily used as input to get answers. However, the image features are too redundant to learn accurate characterizations within a limited complexity and time. While in the process of human reasoning, abstract description of an image is usually to avoid irrelevant details. Inspired by this, a higher-level representation named semantic representation is introduced. In this paper, a detailed visual reasoning model is proposed. This new model contains an image understanding model based on semantic representation, feature extraction and process model refined with watershed and u-distance method, a feature vector learning model using pyramidal pooling and residual network, and a question understanding model combining problem embedding coding method and machine translation decoding method. The feature vector could better represent the whole image instead of overly focused on specific characteristics. The model using semantic representation as input verifies that more accurate results can be obtained by introducing a highlevel semantic representation. The result also shows that it is feasible and effective to introduce high-level and abstract forms of knowledge representation into deep learning tasks. This study lays a theoretical and experimental foundation for introducing different levels of knowledge representation into deep learning in the future.

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An improved method for soft tissue modeling

Cited by 125 publications

References 24 publications

The Nonlinear Time Sequence Analysis in the Alpine-Himalayan Earthquake Zone

The Nonlinear Time Sequence Analysis in the Alpine-Himalayan Earthquake Zone

Sentence Representation Method Based on Multi-Layer Semantic Network

Improving Visual Reasoning Through Semantic Representation

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