Movement Detection of Human Body Segments: Passive radio-frequency identification and machine-learning technologies.

2015 ◽  
Vol 57 (3) ◽  
pp. 23-37 ◽  
Author(s):  
Sara Amendola ◽  
Luigi Bianchi ◽  
Gaetano Marrocco
Keyword(s):  
Machine Learning ◽  
Radio Frequency ◽  
Human Body ◽  
Radio Frequency Identification ◽  
Learning Technologies ◽  
Movement Detection ◽  
Body Segments ◽  
Frequency Identification

scholarly journals Discovery of Characteristic Patterns from Transactions with Their Classes

2012 ◽  
Vol 2012 ◽  
pp. 1-12
Author(s):  
Shigeaki Sakurai
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Numerical Experiments ◽  
New Method ◽  
Minimum Support ◽  
Frequency Identification ◽  
The Difference

This paper deals with transactions with their classes. The classes represent the difference of conditions in the data collection. This paper redefines two kinds of supports: characteristic support and possible support. The former one is based on specific classes assigned to specific patterns. The latter one is based on the minimum class in the classes. This paper proposes a new method that efficiently discovers patterns whose characteristic supports are larger than or equal to the predefined minimum support by using their possible supports. Also, this paper verifies the effect of the method through numerical experiments based on the data registered in the UCI machine learning repository and the RFID (radio frequency identification) data collected from two apparel shops.

scholarly journals Integrating Machine Learning, Radio Frequency Identification, and Consignment Policy for Reducing Unreliability in Smart Supply Chain Management

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 247
Author(s):  
Suman Kalyan Sardar ◽  
Biswajit Sarkar ◽  
Byunghoon Kim
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Optimization Technique ◽  
Chain Management ◽  
Demand Information ◽  
Frequency Identification ◽  
Smart Supply Chain Management

Adopting smart technologies for supply chain management leads to higher profits. The manufacturer and retailer are two supply chain players, where the retailer is unreliable and may not send accurate demand information to the manufacturer. As an advanced smart technology, Radio Frequency Identification (RFID) is implemented to track and trace each product’s movement on a real-time basis in the inventory. It takes this supply chain to a smart supply chain management. This research proposes a Machine Learning (ML) approach for on-demand forecasting under smart supply chain management. Using Long-Short-Term Memory (LSTM), the demand is forecasted to obtain the exact demand information to reduce the overstock or understock situation. A measurement for the environmental effect is also incorporated with the model. A consignment policy is applied where the manufacturer controls the inventory, and the retailer gets a fixed fee along with a commission for selling each product. The manufacturer installs RFID technology at the retailer’s place. Two mathematical models are solved using a classical optimization technique. The results from those two models show that the ML-RFID model gives a higher profit than the existing traditional system.

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