HDL-Mutation Based Simulation Data Generation by Propagation Guided Search

2011 ◽  
Author(s):  
Tao Xie ◽  
Wolfgang Mueller ◽  
Florian Letombe
Keyword(s):  
Data Generation ◽  
Simulation Data ◽  
Guided Search

scholarly journals Application of Extreme Learning Machine in GPS Positioning Process

2018 ◽  
Vol 176 ◽  
pp. 01034
Author(s):  
Chengxin Li ◽  
Jing Peng ◽  
Lv Zhicheng ◽  
Mengli Wang ◽  
Gang Ou
Keyword(s):  
Extreme Learning Machine ◽  
Experimental Simulation ◽  
Data Generation ◽  
Simulation Experiments ◽  
Simulation Data ◽  
Gps Positioning ◽  
Network Training ◽  
Generation Network ◽  
Network Prediction ◽  
Learning Machine

In the positioning process of GPS, the linear least squares algorithm and Kalman filtering algorithm are widely used but still have shortcomings. Application of extreme learning machine in this area is proposed in this paper, which breaks through the limitations of the traditional method of positioning based on mathematical models. Two simulation experiments of ELM in GPS positioning process are presented in this paper while the latter is a supplement to the former. Each one contains three phases, including simulation data generation, network training and network prediction, each of which is considered carefully. The feasibility of extreme learning machine is verified through experimental simulation. A more accurate positioning result can be obtained.

Research on Data Generation Method in Cloud Manufacturing Simulation Platform

2017 ◽  
Author(s):  
Chun Zhao ◽  
Lin Zhang
Keyword(s):  
Cloud Manufacturing ◽  
Simulation Platform ◽  
Data Generation ◽  
Manufacturing Environment ◽  
Simulation Data ◽  
Simulation Accuracy ◽  
Manufacturing Simulation ◽  
The Real ◽  
Actual Environment ◽  
Resource Characteristics

Cloud manufacturing simulation platform is used to simulate the collaboration and evolution, which among the resources, services, tasks, participants in cloud manufacturing environment. As an important part of the platform of simulation, simulation data generation method can effectively support the simulation accuracy. Data in cloud manufacturing environment are not completely random, and are closely related to the actual environment and resource characteristics. The workload of traditional random generate method or artificial method is very heavy and cannot completely rebuild the simulation environment. In this paper, Using clustering method to extract characteristics from an actual environment, and then extend the characteristics to generate new simulation data. To build a similar environment to the real environment used in the simulation. The result is shown that compared with the method to generate random data. This method can generate the reference data similar environment, the simulation can reflect the real effect in the process.

scholarly journals Spline Function Simulation Data Generation for Walking Motion Using Foot-Mounted Inertial Sensors

Electronics ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 18 ◽  
Author(s):  
Thanh Pham ◽  
Young Suh
Keyword(s):  
Inertial Sensors ◽  
Map Matching ◽  
Control Points ◽  
Data Generation ◽  
Noise Levels ◽  
Eighth Order ◽  
Simulation Data ◽  
B Spline ◽  
Walking Motion ◽  
Order Algebraic

This paper investigates the generation of simulation data for motion estimation using inertial sensors. The smoothing algorithm with waypoint-based map matching is proposed using foot-mounted inertial sensors to estimate position and attitude. The simulation data are generated using spline functions, where the estimated position and attitude are used as control points. The attitude is represented using B-spline quaternion and the position is represented by eighth-order algebraic splines. The simulation data can be generated using inertial sensors (accelerometer and gyroscope) without using any additional sensors. Through indoor experiments, two scenarios were examined include 2D walking path (rectangular) and 3D walking path (corridor and stairs) for simulation data generation. The proposed simulation data is used to evaluate the estimation performance with different parameters such as different noise levels and sampling periods.

Sign in / Sign up

Export Citation Format

Share Document