Research on Sports Class Load Monitoring System Based on Threshold Classification Algorithm

In order to reduce the sports injury caused by high intensity sports classes, it is necessary to monitor the state of the sports load. Therefore, the sport’s load monitoring system based on a threshold classification algorithm is proposed. In this paper, we design the hardware and software structures of the sports load monitoring systems in a physical education class. In this system, the state parameters of the sports load are collected by wireless sensor network nodes, and the feature parameters are fused and clustered by the integrated information fusion method. After that, we establish the movement target image acquisition model, which unifies the ZigBee networking realization to the high intensity sports classroom movement load monitoring. Simulation results show that the designed PE classroom sports load monitoring system based on the threshold classification algorithm has high performance for sports parameter monitoring and can effectively avoid sports injury caused by overload.

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About Measures of the State Support for Technical Equipment of Agricultural Producers

Economy of agricultural and processing enterprises ◽

10.31442/0235-2494-2020-0-9-20-27 ◽

2020 ◽

pp. 20-27

Author(s):

A.Ya. Kibirov ◽

Keyword(s):

Statistical Analysis ◽

Energy Saving ◽

High Performance ◽

The State ◽

Agricultural Products ◽

Technical Equipment ◽

Agricultural Machinery ◽

State Support ◽

Agricultural Producers ◽

Technical Base

The article uses methods of statistical analysis, deduction and analogy to consider programs at the Federal, regional and economic levels, which provide for measures aimed at improving the technical equipment of agricultural producers. Particular attention is paid to the acquisition of energy-saving, high-performance agricultural machinery and equipment used in the production and processing of agricultural products. An assessment of the effectiveness of state support for updating the material and technical base of agriculture is given. Based on the results of the study, conclusions and recommendations were formulated.

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An arrhythmia classification algorithm using C-LSTM in physiological parameters monitoring system under internet of health things environment

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-021-03456-7 ◽

2021 ◽

Author(s):

Weijia Lu ◽

Jiehui Jiang ◽

Liang Ma ◽

Hao Chen ◽

Huiqun Wu ◽

...

Keyword(s):

Monitoring System ◽

Classification Algorithm ◽

Physiological Parameters

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Design of Coal Mine Power Grid Parameter Monitoring System Based on CAN Bus

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/651/3/032009 ◽

2021 ◽

Vol 651 (3) ◽

pp. 032009

Author(s):

Yunlong Ma

Keyword(s):

Monitoring System ◽

Coal Mine ◽

Can Bus ◽

Power Grid ◽

Parameter Monitoring

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Design of Intelligent Pest Monitoring System Based on Image Classification Algorithm

2020 3rd International Conference on Control and Robots (ICCR) ◽

10.1109/iccr51572.2020.9344213 ◽

2020 ◽

Author(s):

Yu Lina ◽

Yue Xiuming

Keyword(s):

Image Classification ◽

Monitoring System ◽

Classification Algorithm ◽

Pest Monitoring

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Semi-supervised Intrusive Appliance Load Monitoring in Smart Energy Monitoring System

ACM Transactions on Sensor Networks ◽

10.1145/3448415 ◽

2021 ◽

Vol 17 (3) ◽

pp. 1-20

Author(s):

Vanh Khuyen Nguyen ◽

Wei Emma Zhang ◽

Adnan Mahmood

Keyword(s):

Supervised Learning ◽

Monitoring System ◽

End Users ◽

Training Data ◽

Smart Device ◽

Energy Monitoring ◽

Load Monitoring ◽

Benchmark Datasets ◽

Smart Home Environment ◽

Smart Appliance

Intrusive Load Monitoring (ILM) is a method to measure and collect the energy consumption data of individual appliances via smart plugs or smart sockets. A major challenge of ILM is automatic appliance identification, in which the system is able to determine automatically a label of the active appliance connected to the smart device. Existing ILM techniques depend on labels input by end-users and are usually under the supervised learning scheme. However, in reality, end-users labeling is laboriously rendering insufficient training data to fit the supervised learning models. In this work, we propose a semi-supervised learning (SSL) method that leverages rich signals from the unlabeled dataset and jointly learns the classification loss for the labeled dataset and the consistency training loss for unlabeled dataset. The samples fit into consistency learning are generated by a transformation that is built upon weighted versions of DTW Barycenter Averaging algorithm. The work is inspired by two recent advanced works in SSL in computer vision and combines the advantages of the two. We evaluate our method on the dataset collected from our developed Internet-of-Things based energy monitoring system in a smart home environment. We also examine the method’s performances on 10 benchmark datasets. As a result, the proposed method outperforms other methods on our smart appliance datasets and most of the benchmarks datasets, while it shows competitive results on the rest datasets.

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