Trajectory Construction for Autonomous Robot Movement based on Sensed Physical Parameters and Video Data

2021 ◽  
Author(s):  
Grigorij Rego ◽  
Nikita Bazhenov ◽  
Dmitry Korzun
Keyword(s):  
Autonomous Robot ◽  
Video Data ◽  
Physical Parameters

scholarly journals EVALUATION OF CELERITY AND VELOCITY FOR TSUNAMI PROPAGATION INTO RIVERS

2012 ◽  
Vol 1 (33) ◽  
pp. 12
Author(s):  
Min Roh ◽  
Hitoshi Tanaka ◽  
Mohammad Bagus Aditaywan ◽  
Akira Mano ◽  
Keiko Udo
Keyword(s):  
Flow Velocity ◽  
Image Data ◽  
Conservation Equation ◽  
Video Data ◽  
Video Image ◽  
Physical Parameters ◽  
Tsunami Propagation ◽  
The Pacific ◽  
Real Phenomenon ◽  
Tohoku Tsunami

Tsunami propagation into river is one of important real phenomenon. During this process, tsunami celerity and flow velocity are significant physical parameters to understand tsunami behaviors. However, the availability of observation was not sufficient in the 2011 off the Pacific Coast of Tohoku Tsunami, whereas several video data can be used to assess the physical parameters such as tsunami celerity and flow velocity. In this study, as a video image analysis method, Particle Image Velocimetry(PIV) and Particle Tracking Velocimetry(PTV) were used to estimate the tsunami flow velocity. Furthermore, the analysis result of video image data was verified by using the conservation equation. Tsunami physical parameter was successfully estimated by the comparison analysis.

Some Results of the Spectroscopic Study of Four Close Binary Systems of Early Spectral Types

1965 ◽  
Vol 5 ◽  
pp. 120-130
Author(s):  
T. S. Galkina
Keyword(s):  
Spectroscopic Study ◽  
Spectral Type ◽  
Spectroscopic Investigation ◽  
Binary Systems ◽  
Quantitative Information ◽  
Close Binary ◽  
Physical Parameters ◽  
Absorption And Emission ◽  
Close Binary Systems ◽  
Quantitative Estimates

It is necessary to have quantitative estimates of the intensity of lines (both absorption and emission) to obtain the physical parameters of the atmosphere of components.Some years ago at the Crimean observatory we began the spectroscopic investigation of close binary systems of the early spectral type with components WR, Of, O, B to try and obtain more quantitative information from the study of the spectra of the components.

A theory of contamination in electron microscopes by surface diffusion

1978 ◽  
Vol 36 (1) ◽  
pp. 116-117
Author(s):  
J.T. Fourie
Keyword(s):  
Electron Beam ◽  
Surface Diffusion ◽  
High Vacuum ◽  
Ultra High Vacuum ◽  
Physical Parameters ◽  
Carbon Compounds ◽  
Hydrocarbon Molecules ◽  
Electron Microscopes ◽  
Primary Electrons ◽  
Long Chain

Contamination in electron microscopes can be a serious problem in STEM or in situations where a number of high resolution micrographs are required of the same area in TEM. In modern instruments the environment around the specimen can be made free of the hydrocarbon molecules, which are responsible for contamination, by means of either ultra-high vacuum or cryo-pumping techniques. However, these techniques are not effective against hydrocarbon molecules adsorbed on the specimen surface before or during its introduction into the microscope. The present paper is concerned with a theory of how certain physical parameters can influence the surface diffusion of these adsorbed molecules into the electron beam where they are deposited in the form of long chain carbon compounds by interaction with the primary electrons.

Electron Microscopy in the Study of Natural Phytoplankton Assemblages

1985 ◽  
Vol 43 ◽  
pp. 620-623
Author(s):  
Linda Sicko-Goad
Keyword(s):  
Electron Microscopy ◽  
Aquatic Environment ◽  
Size Range ◽  
Physical Parameters ◽  
Specific Information ◽  
Phytoplankton Assemblages ◽  
Phytoplankton Productivity ◽  
Ecosystem Effects ◽  
Time Of Year ◽  
Species Specific

Although the use of electron microscopy and its varied methodologies is not usually associated with ecological studies, the types of species specific information that can be generated by these techniques are often quite useful in predicting long-term ecosystem effects. The utility of these techniques is especially apparent when one considers both the size range of particles found in the aquatic environment and the complexity of the phytoplankton assemblages.The size range and character of organisms found in the aquatic environment are dependent upon a variety of physical parameters that include sampling depth, location, and time of year. In the winter months, all the Laurentian Great Lakes are uniformly mixed and homothermous in the range of 1.1 to 1.7°C. During this time phytoplankton productivity is quite low.

A New Numerical Model for Electron-Probe Analysis at High Depth Resolution

1996 ◽  
Vol 54 ◽  
pp. 490-491
Author(s):  
P.-F. Staub ◽  
C. Bonnelle ◽  
F. Vergand ◽  
P. Jonnard
Keyword(s):  
Electron Probe ◽  
Surface Segregation ◽  
Depth Distribution ◽  
Computing Time ◽  
Depth Resolution ◽  
Physical Parameters ◽  
Electron Probe Analysis ◽  
Interface Phases ◽  
Excitation Conditions ◽  
High Depth

Characterizing dimensionally and chemically nanometric structures such as surface segregation or interface phases can be performed efficiently using electron probe (EP) techniques at very low excitation conditions, i.e. using small incident energies (0.5<E0<5 keV) and low incident overvoltages (1<U0<1.7). In such extreme conditions, classical analytical EP models are generally pushed to their validity limits in terms of accuracy and physical consistency, and Monte-Carlo simulations are not convenient solutions as routine tools, because of their cost in computing time. In this context, we have developed an intermediate procedure, called IntriX, in which the ionization depth distributions Φ(ρz) are numerically reconstructed by integration of basic macroscopic physical parameters describing the electron beam/matter interaction, all of them being available under pre-established analytical forms. IntriX’s procedure consists in dividing the ionization depth distribution into three separate contributions:

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

Unifying Calibration Parameters for Dielectric Moisture Meter

2006 ◽  
Vol 2 (1) ◽  
pp. 73-94 ◽  
Author(s):  
Péter Mészáros ◽  
David B. Funk
Keyword(s):  
The United States ◽  
Temperature Correction ◽  
Physical Parameters ◽  
Correction Coefficient ◽  
Prototype System ◽  
United States Department ◽  
Very High Frequency ◽  
Grain Moisture ◽  
University Of Budapest ◽  
Improved Accuracy

The Unified Grain Moisture Algorithm is capable of improved accuracy and allows the combination of many grain types into a single “unified calibration”. The purposes of this research were to establish processes for determining unifying parameters from the chemical and physical properties of grains. The data used in this research were obtained as part of the United States Department of Agriculture-Grain Inspection, Packers and Stockyards Administration's Annual Moisture Calibration Study. More than 5,000 grain samples were tested with a Hewlett-Packard 4291A Material/Impedance Analyzer. Temperature tests were done with a Very High Frequency prototype system at Corvinus University of Budapest. Typical chemical and physical parameters for each of the major grain types were obtained from the literature. Data were analyzed by multivariate chemometric methods. One of the most important unifying parameters (Slope) and the temperature correction coefficient were successfully modeled. The Offset and Translation unifying parameters were not modeled successfully, but these parameters can be estimated relatively easily through limited grain tests.

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