Hybrid fuzzy interface model of sports rehabilitation activities

2021 ◽  
pp. 1-10
Author(s):  
Wu Shoujiang
Keyword(s):  
Evaluation System ◽  
Clustering Algorithm ◽  
Mixed Model ◽  
Expectation Maximization Algorithm ◽  
Rehabilitation Training ◽  
Development Platform ◽  
Longitudinal Tracking ◽  
Database Structure ◽  
The Matrix ◽  
Matrix Normal

At present, the relevant test data and training indicators of athletes during rehabilitation training lack screening and analysis, so it is impossible to establish a long-term longitudinal tracking research system and evaluation system. In order to improve the practical effect of sports rehabilitation activities, this paper successively introduces the matrix normal mixed model and the fuzzy clustering algorithm based on the K-L information entropy regularization and the matrix normal mixed model. Moreover, this paper uses the expectation maximization algorithm to estimate the parameters of the model, discusses the framework, key technologies and core services of the development platform, and conducts certain research on the related technologies of the three-tier architecture. At the same time, according to the actual needs of sports rehabilitation training, this paper designs the functions required for exercise detection and prescription formulation. In addition, this paper analyzes and designs the database structure involved in each subsystem. Finally, this paper designs experiments to verify the performance of the model constructed in this paper. The research results show that the performance of the model constructed in this paper meets the expectations of model construction, so it can be applied to practice.

scholarly journals Research on urban public bicycle dispatching optimization method

2019 ◽  
Author(s):  
Lin Fei ◽  
Yang Yang ◽  
Wang Shihua ◽  
Xu Yudi ◽  
Ma Hong
Keyword(s):  
Clustering Algorithm ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Data Set ◽  
Community Discovery ◽  
The Public ◽  
Multi Objective ◽  
Scheduling Scheme ◽  
Theoretical Support ◽  
The Matrix

Unreasonable public bicycle dispatching area division seriously affects the operational efficiency of the public bicycle system. To solve this problem, this paper innovatively proposes an improved community discovery algorithm based on multi-objective optimization (CDoMO). The data set is preprocessed into a lease/return relationship, thereby it calculated a similarity matrix, and the community discovery algorithm Fast Unfolding is executed on the matrix to obtain a scheduling scheme. For the results obtained by the algorithm, the workload indicators (scheduled distance, number of sites, and number of scheduling bicycles) should be adjusted to maximize the overall benefits, and the entire process is continuously optimized by a multi-objective optimization algorithm NSGA2. The experimental results show that compared with the clustering algorithm and the community discovery algorithm, the method can shorten the estimated scheduling distance by 20%-50%, and can effectively balance the scheduling workload of each area. The method can provide theoretical support for the public bicycle dispatching department, and improve the efficiency of public bicycle dispatching system.

scholarly journals Research and Practice of individualized talent Training Mode for rail transit majors from the perspective of new engineering

2020 ◽  
Vol 218 ◽  
pp. 02003
Author(s):  
Zhao Wu ◽  
Hai Xiang Li ◽  
Jun Ying Qi
Keyword(s):  
Evaluation System ◽  
Teaching Practice ◽  
Implementation Process ◽  
Training Model ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Existing Problems ◽  
Training Mode ◽  
The Matrix ◽  
Set Up

In order to cultivate application-oriented talents of urban rail transit, individualized talent training mode is an important measure. In view of the existing problems in the training of rail transit professionals, the research group proposed the framework of individualized talent training under the background of new engineering, planned the matrix corresponding to graduation requirements and knowledge, ability and quality, and then set up the curriculum system and built the multi-evaluation system in the implementation process. The developed solution has been put into practice and will be tested in the future teaching practice activities in order to constantly improve the personalized talent training model.

scholarly journals Application and Effect of the Gymnastic Exercise Mode during Chemotherapy for Breast Cancer Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Feifei Xie
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Incidence Rate ◽  
Evaluation System ◽  
Malignant Tumors ◽  
Treatment Plan ◽  
Breast Cancer Surgery ◽  
Breast Cancer Patients ◽  
Rehabilitation Training ◽  
Cancer Related Fatigue

Breast cancer is one of the most common malignant tumors in women, which seriously threatens the health of women. With the improvement of living standards, the incidence rate of breast cancer is also rising. In the past ten years, the incidence rate of breast cancer in China’s major cities has increased by 37%, far higher than that in Europe and America. At present, chemotherapy and radiotherapy are the main treatment methods for breast cancer, but many patients will have cancer-related fatigue after surgery. Some studies believe that appropriate sports can improve cancer-related fatigue, but there is no specific research in this area. In view of this problem, this paper puts forward a rehabilitation training method based on gymnastics for breast cancer surgery. This paper is divided into three parts. The first part is the basic theory and core concept of breast cancer and cancer-related fatigue. Through the in-depth study of the theory, this paper believes that breast cancer patients paying attention to rehabilitation training can effectively improve cancer-related fatigue and affect the final therapeutic effect. The second part is the rehabilitation training program based on the way of gymnastics. The corresponding experimental model is established by using real cases as samples. In order to ensure the quality of the experiment, this paper gives the treatment plan in detail and establishes a unified evaluation system. In the third part of this paper, the relevant experiments and results analysis are given, and through data analysis, this paper believes that gymnastics can effectively help breast cancer patients with postoperative rehabilitation and continuous recovery of the upper limb function and improve cancer-related fatigue and other issues.

scholarly journals Supervised clustering of high-dimensional data using regularized mixture modeling

2020 ◽  
Author(s):  
Wennan Chang ◽  
Changlin Wan ◽  
Yong Zang ◽  
Chi Zhang ◽  
Sha Cao
Keyword(s):  
Clustering Algorithm ◽  
Expectation Maximization Algorithm ◽  
Substantial Improvement ◽  
Superior Performance ◽  
High Dimensional ◽  
Analysis Tool ◽  
Supervised Clustering ◽  
Mixture Regression ◽  
Clustering Problem ◽  
Clinical Presentations

Abstract Identifying relationships between genetic variations and their clinical presentations has been challenged by the heterogeneous causes of a disease. It is imperative to unveil the relationship between the high-dimensional genetic manifestations and the clinical presentations, while taking into account the possible heterogeneity of the study subjects.We proposed a novel supervised clustering algorithm using penalized mixture regression model, called component-wise sparse mixture regression (CSMR), to deal with the challenges in studying the heterogeneous relationships between high-dimensional genetic features and a phenotype. The algorithm was adapted from the classification expectation maximization algorithm, which offers a novel supervised solution to the clustering problem, with substantial improvement on both the computational efficiency and biological interpretability. Experimental evaluation on simulated benchmark datasets demonstrated that the CSMR can accurately identify the subspaces on which subset of features are explanatory to the response variables, and it outperformed the baseline methods. Application of CSMR on a drug sensitivity dataset again demonstrated the superior performance of CSMR over the others, where CSMR is powerful in recapitulating the distinct subgroups hidden in the pool of cell lines with regards to their coping mechanisms to different drugs. CSMR represents a big data analysis tool with the potential to resolve the complexity of translating the clinical representations of the disease to the real causes underpinning it. We believe that it will bring new understanding to the molecular basis of a disease and could be of special relevance in the growing field of personalized medicine.

Mixed model for interoccurrence times of earthquakes based on the expectation-maximization algorithm

2011 ◽  
Vol 59 (5) ◽  
pp. 872-890 ◽  
Author(s):  
Nadia Tahernia ◽  
Morteza Khodabin ◽  
Noorbakhsh Mirzaei
Keyword(s):  
Expectation Maximization ◽  
Mixed Model ◽  
Expectation Maximization Algorithm
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