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中文专利关键信息语料库的构建研究(Research on the construction of Chinese patent key information corpus)
“专利文献是一种重要的技术文献, 是知识产权强国的重要工作内容。目前专利语料库多集中于信息检索、机器翻译以及文本文分类等领域, 尚缺乏更细粒度的标注, 不足以支持问答、阅读理解等新形态的人工智能技术研发。本文面向专利智能分析的需要, 提出了从解决问题、技术手段、效果三个角度对发明专利进行专利标注, 并最终构建了包含313篇的中文专利关键信息语料库。利用命名实体识别技术对语料库关键信息进行识别和验证, 表明专利关键信息的识别是不同于领域命名实体识别的更大粒度的信息抽取难题。”
Toward more accurate and generalizable brain deformation estimators for traumatic brain injury detection with unsupervised domain adaptation
Machine learning head models (MLHMs) are developed to estimate brain deformation for early detection of traumatic brain injury (TBI).
Denoising instrumented mouthguard measurements of head impact kinematics with a convolutional neural network
Wearable sensors for measuring head kinematics can be noisy due to imperfect interfaces with the body.
Data-driven decomposition of brain dynamics with principal component analysis in different types of head impacts
The brain dynamics decomposition enables better interpretation of the patterns in brain injury metrics and the sensitivity of brain injury metrics across impact types.
Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion
To address the computational cost of FEM, the limited strain rate prediction, and the generalizability of MLHMs to on-field datasets, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR).
Kinematics clustering enables head impact subtyping for better traumatic brain injury prediction
However, due to different kinematic characteristics, many brain injury risk estimation models are not generalizable across the variety of impacts that humans may sustain.
Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics
A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e. g., football, car crash, mixed martial arts).
Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation
To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in 1) the derivative order (angular velocity, angular acceleration, angular jerk), 2) the direction and 3) the power (e. g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events.
Relationship between brain injury criteria and brain strain across different types of head impacts can be different
The results show a significant difference in the relationship between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain in different head impact types.
Deep Learning Head Model for Real-time Estimation of Entire Brain Deformation in Concussion
Results: The proposed deep learning head model can calculate the maximum principal strain for every element in the entire brain in less than 0. 001s (with an average root mean squared error of 0. 025, and with a standard deviation of 0. 002 over twenty repeats with random data partition and model initialization).
A New Open-Access Platform for Measuring and Sharing mTBI Data
Despite numerous research efforts, the precise mechanisms of concussion have yet to be fully uncovered.
