Embedded System for PCB Soldering Platform Controller Design

2012 ◽  
Vol 472-475 ◽  
pp. 2471-2475
Author(s):  
Mao Lin Chen ◽  
Kai Jung Chen
Keyword(s):  
Neural Network ◽  
Embedded System ◽  
Controller Design ◽  
Hopfield Neural Network ◽  
Soldering Technology ◽  
Low Efficiency ◽  
Set Up

We make use of embedded BASIC Stamp 2 (BS2) microchip controller to design with Hopfield neural network (HNN) as the foundation of sample training, which applies for a soldering platform of mechanical vision and accomplishes PCB soldering positioning technology. The system is verified by Matlab and Simulink to set up the simulation of PCB image soldering positioning. The experiment is proved to improve the traditional low efficiency of PCB soldering technology, to achieve the feasibility of PCB image positioning and to promote the soldering quality.

Application of Kalman Filter and Mean Field Annealing Algorithms in GPS-Based Attitude Determination

1998 ◽  
Vol 51 (1) ◽  
pp. 117-131 ◽  
Author(s):  
Jyh-Ching Juang ◽  
Guo-Shing Huang
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Attitude Determination ◽  
Mean Field ◽  
Hopfield Neural Network ◽  
Integer Ambiguity ◽  
Neural Network Approach ◽  
Field Annealing ◽  
Filter Approach ◽  
Set Up

In this paper, two algorithms of Global Positioning System based attitude determination are proposed. The first algorithm extends the Kalman filter approach to determine the integer ambiguity and the orientation that is needed in a typical gps-based attitude determination problem. The second algorithm explores the mean field annealing neural network approach, which is a combination of the competitive Hopfield neural network and the stochastic simulated annealing technique, to resolve the optimal attitude problems. A test platform is set up for verifying these algorithms. The two algorithms are further compared in terms of computation speed and convergence rate.

Detection and localization of helipad in autonomous UAV landing: a coupled visual-inertial approach with artificial intelligence

2020 ◽  
Vol 71 (7) ◽  
pp. 828-839
Author(s):  
Thinh Hoang Dinh ◽  
Hieu Le Thi Hong
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Fiducial Marker ◽  
Localization Algorithm ◽  
Challenging Problem ◽  
Autonomous Landing ◽  
Aerial Vehicles ◽  
Set Up ◽  
Detection And Localization ◽  
Rotary Wing

Autonomous landing of rotary wing type unmanned aerial vehicles is a challenging problem and key to autonomous aerial fleet operation. We propose a method for localizing the UAV around the helipad, that is to estimate the relative position of the helipad with respect to the UAV. This data is highly desirable to design controllers that have robust and consistent control characteristics and can find applications in search – rescue operations. AI-based neural network is set up for helipad detection, followed by optimization by the localization algorithm. The performance of this approach is compared against fiducial marker approach, demonstrating good consensus between two estimations

Application of Hopfield neural network in unit commitment problem

2009 ◽  
Vol 29 (4) ◽  
pp. 1028-1031
Author(s):  
Wei-xin GAO ◽  
Xiang-yang MU ◽  
Nan TANG ◽  
Hong-liang YAN
Keyword(s):  
Neural Network ◽  
Unit Commitment ◽  
Hopfield Neural Network ◽  
Unit Commitment Problem ◽  
Commitment Problem

Gel Image Matching Using Hopfield Neural Network

2006 ◽  
Vol 13B (3) ◽  
pp. 323-328
Author(s):  
Yukhuu Ankhbayar ◽  
Suk-Hyung Hwang ◽  
Young-Sup Hwang
Keyword(s):  
Neural Network ◽  
Image Matching ◽  
Hopfield Neural Network
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