Soft quantification in statistical relational learning

Progress and directions in low-cost redox flow batteries for large-scale energy storage National Science Review 4, 91 (2017); Research trend of large-scale supercomputers and applications from the TOP500 and Gordon Bell Prize SCIENCE CHINA Information Sciences 63, 171001 (2020); Research on evolution and prevention of the No.1 large-scale dangerous slope of Erlang Mountain on the Sichuan-Tibet Highway Science in China Series E-Technological Sciences 46, 42 (2003); Developing trend of design and manufacture technology for large-scale wind turbine blade

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Section: 基于统计关系学习的类型推理方法unclassified

Research progress of large-scale knowledge graph completion technology

Wang

Meng

2020

Sci. Sin.-Inf.

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“…The point Fourier transform of and is carried out respectively, and the optimal design of data transmission channel for lithium ion screen precursor preparation is realized [ 7 ].…”

Section: Data Transmission Channel Model and Interference Suppressionmentioning

confidence: 99%

Study on the Preparation of the Precursor of the Li-ion Screen Based on Big Data Analysis

Xiao

Wei

Pei

2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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In order to improve the preparation ability of lithium ion screen precursor, an anti-interference high definition lithium ion screen precursor preparation data dynamic migration and information enhancement method based on dynamic migration equilibrium modulation is proposed. The dynamic migration transmission channel model of HD lithium ion screen precursor preparation is constructed, and the anti-interference design of HD lithium ion screen precursor preparation is carried out by using interference filtering algorithm. The dynamic migration state characteristic of the preparation data of HD lithium ion screen precursor is extracted, and the Porter interval equilibrium method is used to control the dynamic migration of HD lithium ion screen precursor preparation data. The dynamic migration optimization of preparation data of anti-interference HD lithium ion screen precursor is realized, and the anti-interference and safety of lithium ion screen precursor preparation are improved. The simulation results show that the preparation of HD lithium ion screen precursor by this method is safe and has strong anti-interference ability.

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“…However, this method has poor adaptability to the safety assessment of athlete training and has a large time expenditure. In reference [11], an athlete training safety evaluation model based on big data fusion feature analysis is proposed. e model of integrated information statistics of athlete training safety is constructed and the method of fuzzy association rule scheduling is used to evaluate the safety of athlete training.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Collection Method of Athletes’ Abnormal Training Data Based on Machine Learning

Wang¹

2021

Mobile Information Systems

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Real-time collection of athletes’ abnormal training data can improve the training effect of athletes. This paper studies the real-time collection method of athletes’ abnormal training data based on machine learning. The main motivation of this paper is to collect the athletes’ abnormal training data in time, which can help to evaluate and improve the training effect. Four sensor nodes are arranged in the upper and lower limbs of athletes to collect the angular velocity, acceleration, and magnetic field strength data of athletes in training state. The data are sent to the data transmission base station through wireless sensors, and the data transmission base station transmits the data to the data processing terminal. The data processing terminal calculates the difference between the sample values of each sensor to obtain the data dispersion of each sensor. The features of each dimension data in a time domain and frequency domain are obtained by using the dispersion degree to construct 32-dimensional feature vectors, and the extracted feature vectors are input into the hidden Markov model. The forward algorithm is used to obtain the probability of the final observation sequence, so as to realize the final collection of athletes’ abnormal training data. The experimental results show that the accuracy and recall rate of the abnormal data collected by this method is higher than 98%, which requires less time.

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Soft quantification in statistical relational learning

Cited by 21 publications

References 35 publications

Research progress of large-scale knowledge graph completion technology

Research progress of large-scale knowledge graph completion technology

Study on the Preparation of the Precursor of the Li-ion Screen Based on Big Data Analysis

Real-Time Collection Method of Athletes’ Abnormal Training Data Based on Machine Learning

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