Intelligent system for sports movement quantitative analysis

2020 ◽  
pp. 1-9
Author(s):  
Yanhong Ren ◽  
Bo Chen ◽  
Aizeng Li
Keyword(s):  
Deep Learning ◽  
Quantitative Analysis ◽  
Human Body ◽  
Intelligent System ◽  
Comprehensive Evaluation ◽  
Fuzzy Theory ◽  
Performance Comparison ◽  
The Core ◽  
Core Elements ◽  
Quantitative Analysis Method

Action is the key to sports and the core factor of standardization, quantification, and comprehensive evaluation. However, in the actual competition training, the occurrence of sports activities is often fleeting, and it is difficult for human eyes to identify quickly and accurately. There are many existing quantitative analysis methods of sports movements, but because there are many complex factors in the actual scene, the effect is not ideal. How to improve the accuracy of the model is the key to current research, but also the core problem to be solved. To solve this problem, this paper puts forward an intelligent system of sports movement quantitative analysis based on deep learning method. The method in this paper is firstly to construct the fuzzy theory human body feature method, through which the influencing factors in the quantitative analysis of movement can be distinguished, and the effective classification can be carried out to eliminate irrelevantly and simplify the core elements. Through the method of human body characteristics based on fuzzy theory, an intelligent system of deep learning quantitative analysis is established, which optimizes the algorithm and combines many modern technologies including DBN architecture. Finally, the accuracy of the method is improved by sports action detection, figure contour extraction, DBN architecture setting, and normalized sports action recognition and quantification. To verify the effect of this model, this paper established a performance comparison experiment based on the traditional method and this method. The experimental results show that compared with the traditional three methods, the accuracy of the in-depth learning sports movement quantitative analysis method in this paper has greatly improved and its performance is better.

Study on the Maintenance Capacity of “Man-Machine System” Based on Fuzzy Comprehensive Evaluation

2012 ◽  
Vol 203 ◽  
pp. 479-483
Author(s):  
Yong Jun Sun ◽  
Yi Qu
Keyword(s):  
Complex System ◽  
Quantitative Analysis ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Fuzzy Mathematics ◽  
Machine System ◽  
The Core ◽  
Evaluation Algorithm ◽  
The Stability ◽  
Index Weight

This paper, in accordance with the maintenance capacity of “Man-Machine System”, holds the core of the “Man-Machine System” and employs the analysis methods of the complex system, brings forward the model of the three-layer comprehensive evaluation based on fuzzy mathematics, then brings out the methods of the index weight and studies the maintenance capacity’s evaluation algorithm on the foundation of the quantitative analysis. In the last it gives the application of the model and algorithm by using the instances and studies the changing maintenance capacity including the stability and the undulation, which provides one thought for scientifically evaluating the maintenance capacity of “Man-Machine System”.

Underground Mining Method Selecting System Based on Fuzzy Theory

2011 ◽  
Vol 402 ◽  
pp. 631-635 ◽  
Author(s):  
Hai Wang Ye ◽  
Fang Liu
Keyword(s):  
Decision Making ◽  
Fuzzy Clustering ◽  
Comprehensive Evaluation ◽  
Underground Mining ◽  
Fuzzy Theory ◽  
Fuzzy Comprehensive Evaluation ◽  
Underground Mine ◽  
Mining Method ◽  
The Core ◽  
Mining Methods

It is well known that mining method is the core of an underground mine. In order to select mining methods scientifically and reasonably, after summarizing the underground mining methods, an underground mining methods selecting system based on fuzzy theory is established, which includes primary selecting based on fuzzy clustering and final selecting based on multi-objective decision-making and fuzzy comprehensive evaluation. And the application shows that the mining method selected by the system is the same as the method used in practice, which proves that the selecting system is feasible and reliable.

A survey of R&D of intelligent STR system based on behavior pattern recognition in China

2016 ◽  
Vol 19 (2) ◽  
pp. 109-121 ◽  
Author(s):  
Jun Tang
Keyword(s):  
Pattern Recognition ◽  
Intelligent System ◽  
Behavior Pattern ◽  
Recognition System ◽  
Decision Procedures ◽  
Customer Behavior ◽  
Content Type ◽  
The Core ◽  
Core Elements ◽  
Str System

Purpose The purpose of this paper is to systematically study the research and development history of suspicious transaction reporting (STR) system in China, and introduce the core elements in constructing an intelligent surveillance system which could provide a solution to the situation of low effectiveness and efficiency in Chinese Financial Institutions (FIs) STR procedure nowadays. The solution outputs those falling out of the normal customer behavior profiles instead of only extracting data by the rules issued by authorities. Design/methodology/approach This paper reviews the latest literature, regulations and guidelines of STR gathered domestically and overseas, and hands out questionnaire surveys to hundreds of software vendors, regulators and FIs, details the current situation of poor deployment of intelligent in China and tells the difficulties of subjective STR decision procedures. Findings Few Chinese FIs have deployed real intelligent STR systems, most are using rule-based filtering systems conformed to the objective STR supervisory regulations. To change the embarrassing situation, the regulators have tried to introduce self-regulatory mode which allows the FIs to define STR decision procedures themselves. Limited by the FIs’ ability of information sharing and investigation scope, FIs could hardly unveil the whole schema of a money laundering organization. The pursuant objective FIs can reach is to construct a system that could tell what the normal customer behaviors look like and extract all those falling out of the system’s expectations as suspicious activities. Research limitations/implications Only the core elements of the total intelligent STR system are discussed, that is, what, why and how about the customer behavior pattern recognition system. Besides this, a total solution should also use a watch list, reporting decision, cases management, risk control, etc. Originality/value This paper for the first time argues that the orientation of regulatory rules in China has actually hindered the spreading of really effective intelligent system for these years. The author creatively puts forward a solution to the difficult problem for FIs to spot criminal schema directly, instead the FIs should only be required to determine whether the transactions carrying out currently are falling within the expected behavior pattern scopes, which is under the FIs’ capabilities due to the internationally accepted obligations of “Know Your Customer”.

Study on erythrocytes by SEM photogrammetry (SEMP) technique

1990 ◽  
Vol 48 (3) ◽  
pp. 724-725
Author(s):  
Tong Wensheng ◽  
Lu Lianhuang ◽  
Zhang Zhijun
Keyword(s):  
Quantitative Analysis ◽  
Cell Membrane ◽  
Clinical Diagnosis ◽  
Human Body ◽  
Blood Cells ◽  
Three Dimensional ◽  
Normal Person ◽  
Blood Disease ◽  
Computation Technique ◽  
Important Basis

This is a combined study of two diffirent branches, photogrammetry and morphology of blood cells. The three dimensional quantitative analysis of erythrocytes using SEMP technique, electron computation technique and photogrammetry theory has made it possible to push the study of mophology of blood cells from LM, TEM, SEM to a higher stage, that of SEM P. A new path has been broken for deeply study of morphology of blood cells.In medical view, the abnormality of the quality and quantity of erythrocytes is one of the important changes of blood disease. It shows the abnormal blood—making function of the human body. Therefore, the study of the change of shape on erythrocytes is the indispensable and important basis of reference in the clinical diagnosis and research of blood disease.The erythrocytes of one normal person, three PNH Patients and one AA patient were used in this experiment. This research determines the following items: Height;Length of two axes (long and short), ratio; Crevice in depth and width of cell membrane; Circumference of erythrocytes; Isoline map of erythrocytes; Section map of erythrocytes.

What is the Core Elements of Social Innovation?

2019 ◽  
Vol 45 ◽  
pp. 83-109
Author(s):  
SangMi Cho ◽  
JongSerl Chun ◽  
SoYoung An ◽  
JiYeon Jung
Keyword(s):  
Social Innovation ◽  
The Core ◽  
Core Elements

Duoethnography: A Polytheoretical Approach to (Re)Storing, (Re)Storying the Meanings That One Gives

2020 ◽  
pp. 396-423
Author(s):  
John Joseph Norris ◽  
Richard D. Sawyer
Keyword(s):  
Social Justice ◽  
Critical Theory ◽  
Third Space ◽  
The Third ◽  
The Core ◽  
Wide Range ◽  
Core Elements ◽  
Feminist Inquiry

This chapter summarizes the advancement of duoethnography throughout its fifteen-year history, employing examples from a variety of topics in education and social justice to provide a wide range of approaches that one may take when conducting a duoethnography. A checklist articulates what its cofounders consider the core elements of duoethnographies, additional features that may or may not be employed and how some studies purporting to be duoethnographies may not be so. The chapter indicates connections between duoethnography and a number of methodological concepts including the third space, the problematics of representation, feminist inquiry, and critical theory using published examples by several duoethnographers.

scholarly journals Deep Learning Model Comparison for Vision-Based Classification of Full/Empty-Load Trucks in Earthmoving Operations

2019 ◽  
Vol 9 (22) ◽  
pp. 4871 ◽  
Author(s):  
Quan Liu ◽  
Chen Feng ◽  
Zida Song ◽  
Joseph Louis ◽  
Jian Zhou
Keyword(s):  
Deep Learning ◽  
Model Comparison ◽  
Surveillance Systems ◽  
Comparison Study ◽  
Learning Models ◽  
The Core ◽  
Dump Trucks ◽  
Deep Learning Model ◽  
Contact Field

Earthmoving is an integral civil engineering operation of significance, and tracking its productivity requires the statistics of loads moved by dump trucks. Since current truck loads’ statistics methods are laborious, costly, and limited in application, this paper presents the framework of a novel, automated, non-contact field earthmoving quantity statistics (FEQS) for projects with large earthmoving demands that use uniform and uncovered trucks. The proposed FEQS framework utilizes field surveillance systems and adopts vision-based deep learning for full/empty-load truck classification as the core work. Since convolutional neural network (CNN) and its transfer learning (TL) forms are popular vision-based deep learning models and numerous in type, a comparison study is conducted to test the framework’s core work feasibility and evaluate the performance of different deep learning models in implementation. The comparison study involved 12 CNN or CNN-TL models in full/empty-load truck classification, and the results revealed that while several provided satisfactory performance, the VGG16-FineTune provided the optimal performance. This proved the core work feasibility of the proposed FEQS framework. Further discussion provides model choice suggestions that CNN-TL models are more feasible than CNN prototypes, and models that adopt different TL methods have advantages in either working accuracy or speed for different tasks.

scholarly journals 178. Implementation of the Core Elements of Antibiotic Stewardship in Nursing Homes – National Healthcare Safety Network (NHSN), 2016–2018

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S96-S96
Author(s):  
Katryna A Gouin ◽  
Sarah Kabbani; Angela Anttila ◽  
Josephine Mak ◽  
Elisabeth Mungai ◽  
Ti Tanissha McCray ◽  
...  
Keyword(s):  
Nursing Homes ◽  
Antibiotic Stewardship ◽  
Care Facility ◽  
Antibiotic Use ◽  
Infection Prevention And Control ◽  
Term Care ◽  
Long Term Care Facility ◽  
The Core ◽  
Use Of Resources ◽  
Core Elements

Abstract Background Since 2016, nursing homes (NHs) enrolled in the Centers for Disease Control and Prevention’s NHSN Long-term Care Facility (LTCF) Component have reported on their implementation of the core elements of antibiotic stewardship. In 2016, 42% of NHs reported implementing all seven core elements. Recent regulations require antibiotic stewardship programs in NHs. The objectives of this analysis were to track national progress in implementation of the core elements and evaluate how time dedicated to infection prevention and control (IPC) is associated with the implementation of the core elements. Methods We used the NHSN LTCF 2016–2018 Annual Surveys to assess NH characteristics and implementation of the core elements, defined as self-reported implementation of at least one corresponding stewardship activity. We reported absolute differences in percent implementation. We used log-binomial regression models to estimate the association between weekly IPC hours and the implementation of all seven core elements, while controlling for confounding by facility characteristics. Results We included 7,506 surveys from 2016–2018. In 2018, 71% of NHs reported implementation of all seven core elements, a 28% increase from 2016 (Fig. 1). The greatest increases in implementation from 2016–2018 were in Education (+19%), Reporting (+18%) and Drug Expertise (+15%) (Fig. 2). Ninety-eight percent of NHs had an individual responsible for antibiotic stewardship activities (Accountability), with 30% indicating that the role was fulfilled by an infection preventionist. Furthermore, 71% of NHs reported pharmacist involvement in improving antibiotic use, an increase of 27% since 2016. NHs that reported at least 20 hours of IPC activity per week were 14% more likely to implement all seven core elements, when controlling for facility ownership and affiliation, 95% CI: (1.07, 1.20). Conclusion NHs reported substantial progress in antibiotic stewardship implementation from 2016–2018. Improvements in accessing drug expertise, providing education and reporting antibiotic use may reflect increased stewardship awareness and use of resources among NH providers under new regulatory requirements. NHs with at least 20 hours dedicated to IPC per week may have greater capacity to implement all core elements. Disclosures All Authors: No reported disclosures

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

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