scholarly journals Mining RFID Behavior Data using Unsupervised Learning

2010 ◽  
Vol 1 (1) ◽  
pp. 28-47 ◽  
Author(s):  
Guénaël Cabanes ◽  
Younès Bennani ◽  
Dominique Fresneau
Keyword(s):  
Data Mining ◽  
Unsupervised Learning ◽  
Radio Frequency Identification ◽  
Spatial Organization ◽  
Self Organizing Map ◽  
Automatic Data ◽  
Frequency Identification ◽  
Spatio Temporal ◽  
Tracking Technology ◽  
Learning Data

Radio Frequency IDentification (RFID) is an advanced tracking technology that can be used to study the spatial organization of individual’s spatio-temporal activity. The aim of this work is firstly to build a new RFID-based autonomous system which can follow individuals’ spatio-temporal activity, a tool not currently available. Secondly, the authors aim to develop new tools for automatic data mining. In this paper, they study how to transform these data to investigate the division of labor, the intra-colonial cooperation and conflict in an ant colony. They also develop a new unsupervised learning data mining method (DS2L-SOM: Density based Simultaneous Two-Level - Self Organizing Map) to find homogeneous clusters (i.e., sets of individual which share a similar behavior). According to the experimental results, this method is very fast and efficient. It also allows a very useful visualization of the results.

scholarly journals Mining RFID Behavior Data using Unsupervised Learning

2012 ◽  
pp. 28-48
Author(s):  
Guénaël Cabanes ◽  
Younès Bennani ◽  
Dominique Fresneau
Keyword(s):  
Data Mining ◽  
Unsupervised Learning ◽  
Radio Frequency Identification ◽  
Spatial Organization ◽  
Self Organizing Map ◽  
Automatic Data ◽  
Frequency Identification ◽  
Spatio Temporal ◽  
Tracking Technology ◽  
Learning Data

Radio Frequency IDentification (RFID) is an advanced tracking technology that can be used to study the spatial organization of individual’s spatio-temporal activity. The aim of this work is firstly to build a new RFID-based autonomous system which can follow individuals’ spatio-temporal activity, a tool not currently available. Secondly, the authors aim to develop new tools for automatic data mining. In this paper, they study how to transform these data to investigate the division of labor, the intra-colonial cooperation and conflict in an ant colony. They also develop a new unsupervised learning data mining method (DS2L-SOM: Density-based Simultaneous Two-Level - Self Organizing Map) to find homogeneous clusters (i.e., sets of individual which share a similar behavior). According to the experimental results, this method is very fast and efficient. It also allows a very useful visualization of the results.

RFID Technologies and Applications

2010 ◽  
pp. 44-53
Author(s):  
Christian Kaspar ◽  
Adam Melski ◽  
Britta Lietke ◽  
Madlen Boslau ◽  
Svenja Hagenhoff
Keyword(s):  
Data Storage ◽  
Radio Frequency Identification ◽  
Data Entry ◽  
Radio Waves ◽  
Automatic Data ◽  
American Military ◽  
Digital World ◽  
Transparent Objects ◽  
Frequency Identification ◽  
Passive Transponders

Radio frequency identification (RFID) is a radiosupported identification technology that typically operates by saving a serial number on a radio transponder that contains a microchip for data storage. Via radio waves, the coded information is communicated to a reading device (Jones et al., 2005). RFID does not represent a new development; it was devised by the American military in the 1940s. Since the technology’s clearance for civil use in 1977, RFID has been successfully used for the identification of productive livestock, for electronic immobilizer systems in vehicles, or for the surveillance of building entrances (Srivastava, 2005). Due to decreasing unit costs (especially for passive transponders), RFID technologies now seem increasingly applicable for the labeling of goods and semi-finished products. By this, manual or semi-automatic data entry, for instance through the use of barcodes, can be avoided. This closes the technical gap between the real world (characterized by the lack of distribution transparency of its objects) and the digital world (characterized by logically and physically unambiguous and therefore distribution-transparent objects). In addition, RFID facilitates fully automated simultaneous recognition of more than one transponder without direct line of sight between reader and transponders.

Emerging Mobile Technology and Supply Chain Integration

2011 ◽  
pp. 859-869
Author(s):  
Richard Schilhavy ◽  
A. F. Salam
Keyword(s):  
Supply Chain ◽  
Radio Frequency ◽  
Mobile Technology ◽  
Radio Frequency Identification ◽  
Supply Chain Integration ◽  
Mobile Tracking ◽  
Frequency Identification ◽  
Integrated Supply Chain ◽  
Tracking Technology ◽  
Share Information

This chapter explores how a mobile tracking technology is able to further streamline the integrated supply chain. Previous technologies which have attempted to integrate suppliers, manufactures, distributors and retailers have lacked the flexibility and efficiency necessary to justify the prohibiting costs. Radio frequency identification (RFID) technology however enables various organizations along the supply chain to share information regarding specific products and easily remotely manage internal inventory levels. These applications are only a sample of what RFID is able to accomplish for the integrated supply chain, and this chapter seeks to explore those applications.

Agricultural Data Mining in the 21st Century

2010 ◽  
pp. 229-246
Author(s):  
E. Arlin Torbett ◽  
Tanya M. Candia
Keyword(s):  
Public Health ◽  
Data Mining ◽  
Data Collection ◽  
Radio Frequency Identification ◽  
Fresh Produce ◽  
Online Marketing ◽  
Current State ◽  
Working Together ◽  
Frequency Identification ◽  
Rich Information

Data on the production, sale, repackaging, and transportation of fresh produce is scarce, yet with recent threats to national safety and security, forward and backward traceability of produce is mandatory. Recent advances in online marketing of fresh produce, a new international codification system and use of advanced technologies such as Radio Frequency Identification (RFID) and bar coding are working together to fill the gap, building a solid database of rich information that can be mined. While agricultural data mining holds much promise for farmers, with better indications of what and when to plant, and for buyers, giving them access to improved food quality and availability information, it is the world’s health organizations and governments who stand to be the biggest beneficiaries. This chapter describes the current state of fresh produce data collection and access, new trends that fill important gaps, and emerging methods of mining fresh produce data for improved production, product safety and public health through traceability.

Testing Potential Interference with RFID Usage in the Patient Care Environment

2008 ◽  
Vol 42 (6) ◽  
pp. 479-484 ◽  
Author(s):  
Barbara Christe ◽  
Elaine Cooney ◽  
Gregg Maggioli ◽  
Dustin Doty ◽  
Robert Frye ◽  
...  
Keyword(s):  
Patient Care ◽  
Radio Frequency Identification ◽  
Near Field ◽  
Care Environment ◽  
Frequency Identification ◽  
Testing Potential ◽  
Effective Use ◽  
Potential Interactions ◽  
Rfid Antennas ◽  
Tracking Technology

Abstract The use of radio frequency identification (RFID) equipment in the clinical setting has become prevalent. The exploration of the potential interactions between the equipment used to implement RFID and medical devices is vital to ensure safe and effective use of both the tracking technology and the patient care equipment. This study examines the effects of two common RFID antennas, Near-Field and Far-Field, and five general types of patient care equipment. Data were collected regarding the function of the patient care equipment in the fields of the antennas. No device performance alterations were observed.

scholarly journals Unsupervised Topographic Learning for Spatiotemporal Data Mining

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Guénaël Cabanes ◽  
Younès Bennani
Keyword(s):  
Data Mining ◽  
Radio Frequency Identification ◽  
Spatiotemporal Data ◽  
Rfid Technology ◽  
Spatiotemporal Information ◽  
Behavioral Studies ◽  
Spatiotemporal Data Mining ◽  
Efficient Data ◽  
Frequency Identification ◽  
Mining Tool

In recent years, the size and complexity of datasets have shown an exponential growth. In many application areas, huge amounts of data are generated, explicitly or implicitly containing spatial or spatiotemporal information. However, the ability to analyze these data remains inadequate, and the need for adapted data mining tools becomes a major challenge. In this paper, we propose a new unsupervised algorithm, suitable for the analysis of noisy spatiotemporal Radio Frequency IDentification (RFID) data. Two real applications show that this algorithm is an efficient data-mining tool for behavioral studies based on RFID technology. It allows discovering and comparing stable patterns in an RFID signal and is suitable for continuous learning.

Facing the Challenges of RFID Data Management

2010 ◽  
pp. 2353-2370
Author(s):  
Indranil Bose ◽  
Chun Wai Lam
Keyword(s):  
Data Management ◽  
Radio Frequency Identification ◽  
Business Processes ◽  
Security And Privacy ◽  
Quality Data ◽  
Business Decisions ◽  
Automatic Data ◽  
Supply Chain Logistics ◽  
Frequency Identification ◽  
Time Acquisition

Radio frequency identification (RFID) has generated vast amounts of interest in the supply chain, logistics, and the manufacturing area. RFID can be used to significantly improve the efficiency of business processes by providing automatic data identification and capture. Enormous data would be collected as items leave a trail of data while moving through different locations. Some important challenges such as false read, data overload, real-time acquisition of data, data security, and privacy must be dealt with. Good quality data is needed because business decisions depend on these data. Other important issues are that business processes must change drastically as a result of implementing RFID, and data must be shared between suppliers and retailers. The main objective of this article is focused on data management challenges of RFID, and it provides potential solutions for each identified risk.

Framework for Testing Critical Technology Systems’ Reliability in Space

2018 ◽  
Vol 2 (12) ◽  
Author(s):  
Gnanesswaran Vettrivel
Keyword(s):  
Radio Frequency ◽  
Inventory Control ◽  
Inventory Management ◽  
Radio Frequency Identification ◽  
Rfid Technology ◽  
Automatic Data ◽  
Rfid Systems ◽  
Critical Technology ◽  
Data Capturing ◽  
Frequency Identification

Radio frequency identification (RFID) system is an automatic data capturing system that relies on remotely retrieving data stored on tags using devices called readers. The National Aeronautics and Space Administration (NASA) and a team of contractors, university researchers and technology vendors collaborated to investigate the reliability of RFID systems for automated crew-free inventory control aboard the ISS. In this manuscript we detail the decade long research accomplishments and the maturity of this RFID technology that is being currently used in Space for inventory management.

Facing the Challenges of RFID Data Management

2011 ◽  
pp. 142-158
Author(s):  
Indranil Bose ◽  
Chun Wai Lam
Keyword(s):  
Data Management ◽  
Radio Frequency Identification ◽  
Business Processes ◽  
Security And Privacy ◽  
Quality Data ◽  
Business Decisions ◽  
Automatic Data ◽  
Supply Chain Logistics ◽  
Frequency Identification ◽  
Time Acquisition

Radio frequency identification (RFID) has generated vast amounts of interest in the supply chain, logistics, and the manufacturing area. RFID can be used to significantly improve the efficiency of business processes by providing automatic data identification and capture. Enormous data would be collected as items leave a trail of data while moving through different locations. Some important challenges such as false read, data overload, real-time acquisition of data, data security, and privacy must be dealt with. Good quality data is needed because business decisions depend on these data. Other important issues are that business processes must change drastically as a result of implementing RFID, and data must be shared between suppliers and retailers. The main objective of this article is focused on data management challenges of RFID, and it provides potential solutions for each identified risk.

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