Research on Gesture Speed Estimation Model in 3D Interactive Interface

2015 ◽  
Vol 713-715 ◽  
pp. 1847-1850
Author(s):  
Zhong Zhu Huang ◽  
Zhi Quan Feng ◽  
Na Na He ◽  
Xue Wen Yang
Keyword(s):  
Estimation Method ◽  
Interactive System ◽  
Speed Estimation ◽  
Behavioral Models ◽  
Estimation Model ◽  
User Intention ◽  
Data Glove ◽  
Hand Motion ◽  
Interactive Interface ◽  
Interactive Task

Gesture has different speed in the process of movement. To reflect the different speed of the user, this paper presents a gesture speed estimation method. Firstly, we use data glove and camera to establish the relation between the variation of gesture contour and that of gesture speed. Secondly, we build the gesture speed estimation model by stages. Finally, we get the real-time speed of hand motion through this model and complete interactive task. The main innovation of this paper is that we reveal the relation between the gesture contour and speed to lay the foundation for further capture the interaction of user intention. Experimental results indicate that the time cost of our method decreased by 31% compared with freehand tracking based on behavioral models and the 3D interactive system based on our model is of high the user experience.

Estimation of Forest Biomass Energy Resources Based on Bottom-up Approach in China

2015 ◽  
Vol 8 (1) ◽  
pp. 272-275
Author(s):  
Lan Zhang ◽  
Dan Yu ◽  
Caihong Zhang ◽  
Weidong Zhang
Keyword(s):  
Forest Biomass ◽  
Estimation Method ◽  
Energy Resource ◽  
Biomass Energy ◽  
Energy Resources ◽  
Theoretical Ground ◽  
Estimation Model ◽  
Bottom Up ◽  
Rational Development ◽  
Calculation Results

Currently, the forest biomass energy development is at an initial stage and the estimation method for the forest biomass energy resource reserve is to be unified and refined although there is a great value and potential in the development and utilization of forest biomass energy in China. Based on the existing studies, the present paper analyzes the origins and types of forest biomass energy resources in the perspective of sustainable forestry management, constructs the estimation model using a bottom-up approach, and estimates the total existing forest biomass energy resource reserve in China based on the data of the 7th Forest Resource Survey. The estimation method and the calculation results provide the important theoretical ground for promoting the rational development of forest biomass energy in China.

scholarly journals Estimation of resistance force at steady-state sinkage for cylindrical wheel-typed lunar/planetary exploration rovers with function of push–pull locomotion

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Daisuke Fujiwara ◽  
Naoki Tsujikawa ◽  
Tetsuya Oshima ◽  
Kojiro Iizuka
Keyword(s):  
Steady State ◽  
Estimation Method ◽  
Planetary Exploration ◽  
Experimental Result ◽  
Resistance Force ◽  
Estimation Model ◽  
Loose Soil ◽  
Key Factor ◽  
The Relationship ◽  
Resistance Forces

Abstract Planetary exploration rovers have required a high traveling performance to overcome obstacles such as loose soil and rocks. Push-pull locomotion rovers is a unique scheme, like an inchworm, and it has high traveling performance on loose soil. Push-pull locomotion uses the resistance force by keeping a locked-wheel related to the ground, whereas the conventional rotational traveling uses the shear force from loose soil. The locked-wheel is a key factor for traveling in the push-pull scheme. Understanding the sinking behavior and its resistance force is useful information for estimating the rover’s performance. Previous studies have reported the soil motion under the locked-wheel, the traction, and the traveling behavior of the rover. These studies were, however, limited to the investigation of the resistance force and amount of sinkage for the particular condition depending on the rover. Additionally, the locked-wheel sinks into the soil until it obtains the required force for supporting the other wheels’ motion. How the amount of sinkage and resistance forces are generated at different wheel sizes and mass of an individual wheel has remained unclear, and its estimation method hasn’t existed. This study, therefore, addresses the relationship between the sinkage and its resistance force, and we analyze and consider this relationship via the towing experiment and theoretical consideration. The results revealed that the sinkage reached a steady-state value and depended on the contact area and mass of each wheel, and the maximum resistance force also depends on this sinkage. Additionally, the estimation model did not capture the same trend as the experimental results when the wheel width changed, whereas, the model captured a relatively the same trend as the experimental result when the wheel mass and diameter changed.

Estimating Traffic Volume on Minor Roads at Rural Stop-Controlled Intersections using Deep Learning

2019 ◽  
Vol 2673 (4) ◽  
pp. 108-116 ◽  
Author(s):  
Mostafa H. Tawfeek ◽  
Karim El-Basyouny
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Safety Management ◽  
Estimation Method ◽  
Estimation Model ◽  
The Neural Network ◽  
Annual Average Daily Traffic

Safety Performance Functions (SPFs) are regression models used to predict the expected number of collisions as a function of various traffic and geometric characteristics. One of the integral components in developing SPFs is the availability of accurate exposure factors, that is, annual average daily traffic (AADT). However, AADTs are not often available for minor roads at rural intersections. This study aims to develop a robust AADT estimation model using a deep neural network. A total of 1,350 rural four-legged, stop-controlled intersections from the Province of Alberta, Canada, were used to train the neural network. The results of the deep neural network model were compared with the traditional estimation method, which uses linear regression. The results indicated that the deep neural network model improved the estimation of minor roads’ AADT by 35% when compared with the traditional method. Furthermore, SPFs developed using linear regression resulted in models with statistically insignificant AADTs on minor roads. Conversely, the SPF developed using the neural network provided a better fit to the data with both AADTs on minor and major roads being statistically significant variables. The findings indicated that the proposed model could enhance the predictive power of the SPF and therefore improve the decision-making process since SPFs are used in all parts of the safety management process.

Speed estimation method for ultrahigh-speed induction machine utilizing rotor slot harmonics appearing in neutral point voltage

2011 ◽  
Vol 176 (3) ◽  
pp. 59-68
Author(s):  
Koichi Nishibata ◽  
Muneaki Ishida ◽  
Shinji Doki ◽  
Takashi Masuzawa ◽  
Masami Fujitsuna
Keyword(s):  
Induction Machine ◽  
Estimation Method ◽  
Neutral Point ◽  
Speed Estimation

Speed Estimation Method for Sensor Less Control of AC Motor Drives Based on Stator Field Orientation

2011 ◽  
Vol 130-134 ◽  
pp. 1361-1364
Author(s):  
Shou Qiang Chen ◽  
Fan Li ◽  
Yu Long
Keyword(s):  
Direct Torque Control ◽  
Estimation Method ◽  
Orientation Angle ◽  
Torque Control ◽  
Speed Estimation ◽  
Field Orientation ◽  
Orientation Control ◽  
Ac Motor ◽  
Field Orientation Control ◽  
Estimation Scheme

Based on stator-field-orientation control combining with both direct torque control (DTC) and rotor-field-orientation control a speed estimation scheme is presented in this paper. Two key problems are discussed in detail. First is to use phase operation to decide stator-field-orientation angle θs and then the discrete-tracking-differential (DTD) technique is employed to high quality stator current. The experiments show that accurate speed estimation can be obtained by the method.

scholarly journals A Novel Wind Speed Estimation Based on the Integration of an Artificial Neural Network and a Particle Filter Using BeiDou GEO Reflectometry

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3350 ◽  
Author(s):  
Kittipong Kasantikul ◽  
Dongkai Yang ◽  
Qiang Wang ◽  
Aung Lwin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Particle Filter ◽  
Maritime Transportation ◽  
Global Navigation Satellite Systems ◽  
Speed Estimation ◽  
Estimation Model ◽  
Main Concept ◽  
Artificial Neural

Oceanographic remote sensing, which is based on the sensitivity of reflected signals from the Global Navigation Satellite Systems (GNSS), so-called GNSS-Reflectometry (GNSS-R), is very useful for the observation of ocean wind speed. Wind speed estimation over the ocean is the core factor in maritime transportation management and the study of climate change. The main concept of the GNSS-R technique is using the different times between the reflected and the direct signals to measure the wind speed and wind direction. Accordingly, this research proposes a novel technique for wind speed estimation involving the integration of an artificial neural network and the particle filter based on a theoretical model. Moreover, particle swarm optimization was applied to find the optimal weight and bias of the artificial neural network, in order to improve the accuracy of the estimation result. The observation dataset of the reflected signal information from BeiDou Geostationary Earth Orbit (GEO) satellite number 4 was used as an input for the estimation model. The data consisted of two phases with I and Q components. Two periods of BeiDou data were selected, the first period was from 3 to 8 August 2013 and the second period was from 12 to 14 August 2013, which corresponded to events from the typhoon Utor. The in situ wind speed measurement collected from the buoy station was used to validate the results. A coastal experiment was conducted at the Yangjiang site located in the South China Sea. The results show the ability of the proposed technique to estimate wind speed with a root mean square error of approximately 1.9 m/s.

The DMT Power Line Channel Sparse Bayesian Regression Estimation Based on Communication System

2014 ◽  
Vol 960-961 ◽  
pp. 1308-1311
Author(s):  
Yi Pei Huang ◽  
Ya Jun Han ◽  
Bao Fan Chen
Keyword(s):  
Channel Estimation ◽  
Bayesian Learning ◽  
Estimation Method ◽  
Power Line ◽  
Previous Method ◽  
Bayesian Regression ◽  
Estimation Model ◽  
Power Line Communications ◽  
Learning Framework ◽  
Performance Curves

This paper introduces the power line communications channel estimation method based on sparse Bayesian regression, it is through the use of Bayesian learning framework that provides a sparse model in the presence of noise accurate channel estimation model. Improved channel estimation using the power line for the system to consider the frequency domain equalization (FREQ) transmitter and receiver, the bit error rate and comparing the two methods for generating various channel estimation techniques, and (BER) performance curves simulation the results show that the performance of the method is better than the previous method of least squares technique.

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