An automatic identification tracking system applied to motion analysis of industrial field

2011 ◽  
Author(s):  
Shuo Li ◽  
Wei Liu ◽  
Daibao Xin ◽  
Shuo Qiao
Keyword(s):  
Motion Analysis ◽  
Tracking System ◽  
Automatic Identification

scholarly journals Auto-tracking system for human lumbar motion analysis

2011 ◽  
Vol 19 (2) ◽  
pp. 205-218 ◽  
Author(s):  
Fuge Sui ◽  
Da Zhang ◽  
Shing Chun Benny Lam ◽  
Lifeng Zhao ◽  
Dongjun Wang ◽  
...  
Keyword(s):  
Motion Analysis ◽  
Tracking System ◽  
Lumbar Motion

scholarly journals The Dimensionless Squared Jerk: An Objective Parameter That Improves Assessment of Hand Motion Analysis during Simulated Shoulder Arthroscopy

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Erica Kholinne ◽  
Maulik J. Gandhi ◽  
Arnold Adikrishna ◽  
Hanpyo Hong ◽  
Haewon Kim ◽  
...  
Keyword(s):  
Motion Analysis ◽  
Path Length ◽  
Shoulder Arthroscopy ◽  
Tracking System ◽  
Arthroscopic Surgery ◽  
Optical Tracking ◽  
Hand Movements ◽  
Total Path Length ◽  
Acceleration Threshold ◽  
Objective Parameter

Purpose. Attempts to quantify hand movements of surgeons during arthroscopic surgery faced limited progress beyond motion analysis of hands and/or instruments. Surrogate markers such as procedure time have been used. The dimensionless squared jerk (DSJ) is a measure of deliberate hand movements. This study tests the ability of DSJ to differentiate novice and expert surgeons (construct validity) whilst performing simulated arthroscopic shoulder surgical tasks. Methods. Six residents (novice group) and six consultants (expert group) participated in this study. Participants performed three validated tasks sequentially under the same experimental setup (one performance). Each participant had ten performances assessed. Hand movements were recorded with optical tracking system. The DSJ, time taken, total path length, multiple measures of acceleration, and number of movements were recorded. Results. There were significant differences between novices and experts when assessed using time, number of movements with average and minimal acceleration threshold, and DSJ. No significant differences were observed in maximum acceleration, total path length, and number of movements with 10m/s2 acceleration threshold. Conclusion. DSJ is an objective parameter that can differentiate novice and expert surgeons’ simulated arthroscopic performances. We propose DSJ as an adjunct to more conventional parameters for arthroscopic surgery skills assessment.

scholarly journals AIS Politics: The Contested Use of Vessel Tracking at the EU’s Maritime Frontier

2019 ◽  
Vol 44 (5) ◽  
pp. 881-899 ◽  
Author(s):  
Lorenzo Pezzani ◽  
Charles Heller
Keyword(s):  
Mediterranean Sea ◽  
Collision Avoidance ◽  
Tracking System ◽  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
Vessel Tracking ◽  
Collision Avoidance System ◽  
The Mediterranean ◽  
Do So

Automatic identification system (AIS) is a vessel tracking system, which since 2004 has become a global tool for the detection and analysis of seagoing traffic. In this article, we look at how this technology, initially designed as a collision avoidance system, has recently become involved in debates concerning migration across the Mediterranean Sea. In particular, after having briefly discussed its emergence and characteristics, we examine how through different practices of (re)appropriation AIS, and the data it generate, have been seized upon, both to contest and to sustain the exclusionary nature of borders, and the mass dying of migrants at sea to which it leads. We do so by referring to forms of data activism we have contributed to in the frame of our Forensic Oceanography project as well as to situations in which AIS has been mobilized by xenophobic groups to demand even stronger exclusionary measures. At the same time, we point to the multiplicity of actors who participate in the politics of migration through AIS in unexpected ways. We conclude by highlighting the irreducible ambivalence of practices of appropriation and call for persistent attention to one’s own positioning within the global datascape constituted by AIS and other data.

scholarly journals A Deep Learning Streaming Methodology for Trajectory Classification

2021 ◽  
Vol 10 (4) ◽  
pp. 250
Author(s):  
Ioannis Kontopoulos ◽  
Antonios Makris ◽  
Konstantinos Tserpes
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
High Frequency ◽  
Tracking System ◽  
High Volume ◽  
Classification Performance ◽  
Automatic Identification ◽  
Mobility Patterns ◽  
Real World Data ◽  
Vessel Tracking

Due to the vast amount of available tracking sensors in recent years, high-frequency and high-volume streams of data are generated every day. The maritime domain is no different as all larger vessels are obliged to be equipped with a vessel tracking system that transmits their location periodically. Consequently, automated methodologies able to extract meaningful information from high-frequency, large volumes of vessel tracking data need to be developed. The automatic identification of vessel mobility patterns from such data in real time is of utmost importance since it can reveal abnormal or illegal vessel activities in due time. Therefore, in this work, we present a novel approach that transforms streaming vessel trajectory patterns into images and employs deep learning algorithms to accurately classify vessel activities in near real time tackling the Big Data challenges of volume and velocity. Two real-world data sets collected from terrestrial, vessel-tracking receivers were used to evaluate the proposed methodology in terms of both classification and streaming execution performance. Experimental results demonstrated that the vessel activity classification performance can reach an accuracy of over 96% while achieving sub-second latencies in streaming execution performance.

Network Design and Implementation of Intelligent Warehouse Based on EPC/RFID and WSN

2012 ◽  
Vol 236-237 ◽  
pp. 338-343 ◽  
Author(s):  
Hong Sheng Li ◽  
Guang Rong Bian ◽  
Ning Hui He
Keyword(s):  
Network Architecture ◽  
Tracking System ◽  
Wireless Sensor ◽  
The Internet ◽  
Automatic Identification ◽  
Location Tracking ◽  
Rfid Technology ◽  
Intelligent Terminal ◽  
The One ◽  
Localization And Tracking

The establishment of intelligent warehouse improves the efficiency of the items storage and realizes the localization and tracking of the various elements of the warehouse. Users can dynamically monitor the location and whereabouts of material, operating machinery and vehicles in warehouse by intelligent terminal. The overall architecture of the intelligent warehouse is built on the Internet and the Internet of Things.With the use of RFID technology, the wireless sensor network technology, the short-range wireless communication technology and automatic identification technology to locate and track the various elements of the warehouse for making warehouse management automatic, intelligent and accurate. The paper first analyzes the basic architecture of the intelligent warehouse system and designs the network architecture of the intelligent warehouse. It also compares the real-time location tracking system based on the RFID with the one based on wireless sensor networks. Therefore it can develop improved method of intelligent location tracking system and apply 8421 coding techniques in the localization and tracking of the intelligent warehouse so as to obtain a satisfactory effect of static and dynamic positioning.

Sign in / Sign up

Export Citation Format

Share Document