Low-cost livestock sorting information management system based on deep learning

Pan, Yuanzhi; Zhang, Yuzhen; Wang, Xiaoping; Gao, Xiang Xiang; Hou, Zhongyu

doi:10.1016/j.aiia.2023.08.007

Cited by 7 publications

(1 citation statement)

References 32 publications

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“…For example, Data Point 1 is estimated to have coordinates (10,20,15) in the X, Y, and Z dimensions respectively. Similarly, the coordinates for Data Point 2 are (12,18,14), and so forth. These coordinate estimations are crucial for understanding the spatial distribution and positioning of objects or entities within a given environment.…”

Section: Dissimilarity Computationmentioning

confidence: 99%

Design of Badminton Technical Movement Recognition System Based on Improved Agnes Algorithm

Yulin Wang

2024

jes

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The Badminton Technical Movement Recognition System is a technology-driven solution aimed at identifying and analyzing various technical movements performed by badminton players during gameplay. Leveraging advanced sensors, motion tracking devices, and machine learning algorithms, this system captures and interprets data related to player movements, racket swings, footwork, and other key actions on the court. By analyzing this data in real-time or post-match, coaches, players, and analysts can gain valuable insights into performance, technique, and areas for improvement. The system's ability to recognize and quantify specific movements allows for detailed performance assessment, personalized training programs, and strategic game planning. The Badminton Technical Movement Recognition System serves as a powerful tool for enhancing player development and optimizing performance in the sport of badminton. The paper presents a comprehensive study on the application of the Multi-Modal AGNES algorithm across diverse domains, encompassing movement pattern estimation, modal feature extraction, coordinate estimation, and badminton feature estimation. Through rigorous experimentation and analysis, the algorithm's efficacy in accurately identifying movement patterns, robustly extracting features from varied datasets, precisely localizing objects in three-dimensional space, and proficiently estimating badminton-specific metrics has been demonstrated. Through rigorous experimentation and analysis, the algorithm's efficacy in accurately identifying movement patterns, robustly extracting features from varied datasets, precisely localizing objects in three-dimensional space, and proficiently estimating badminton-specific metrics has been demonstrated. For instance, the algorithm achieved an average accuracy of 90% in classifying movement patterns in a dataset of 1000 observations. Additionally, it accurately estimated modal features such as swing speed, racket angle, and shuttlecock speed with a mean error rate of less than 5%.

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