scholarly journals Big Data Tracking and Automatic Measurement Technology for Unmanned Aerial Vehicle Trajectory Based on MEMS Sensor

2021 ◽  
Author(s):  
Jun Xing ◽  
Xinzhe Wang ◽  
Jie Dong
Keyword(s):  
Differential Equation ◽  
Big Data ◽  
Automatic Measurement ◽  
Magnetic Data ◽  
Discrete Equation ◽  
Measurement Technology ◽  
Measurement Information ◽  
Gradient Descent Method ◽  
Mems Sensor ◽  
Data Tracking

Abstract Due to the high cost and large error of traditional UAV big data tracking and automatic measurement technology, a method of big data tracking and automatic measurement for UAV trajectory based on MEMS sensor was put forward. The iterative learning control algorithm was used to estimate the repetitive disturbance and modeling error of system based on the simplified dynamics model of four-rotor helicopter and the optimal estimation characteristics of Kalman filter. The discrete equation of quadratic performance function in time domain was selected to compensate the estimated model error disturbance, and then the big data tracking was completed. Based on the data of gyroscope, the quaternion differential equation was established. The differential equation was solved by first-order Picard method, and a set of quaternion data was obtained. The gradient descent method was used to process the acceleration data and magnetic data, and thus to get the optimal quaternion. Finally, the measurement results were obtained by fusing the two quaternions with MEMS sensors. Simulation results prove that the proposed method can obtain the trajectory tracking data and measurement information of UAV accurately.

scholarly journals New Applications of a Kind of Infinitesimal-Operator Lie Algebra

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Honwah Tam ◽  
Yufeng Zhang ◽  
Xiangzhi Zhang
Keyword(s):  
Differential Equation ◽  
Lie Algebras ◽  
Transformation Group ◽  
Order Differential Equation ◽  
Lie Symmetry ◽  
Discrete Equation ◽  
Infinitesimal Operator ◽  
Lie Transformation ◽  
Symmetry Operators ◽  
New Applications

Applying some reduced Lie algebras of Lie symmetry operators of a Lie transformation group, we obtain an invariant of a second-order differential equation which can be generated by a Euler-Lagrange formulism. A corresponding discrete equation approximating it is given as well. Finally, we make use of the Lie algebras to generate some new integrable systems including (1+1) and (2+1) dimensions.

Euler’s differential equation and the identification of the magnetic point‐pole and point‐dipole sources

Geophysics ◽  
1984 ◽  
Vol 49 (9) ◽  
pp. 1549-1553 ◽  
Author(s):  
J. O. Barongo
Keyword(s):  
Magnetic Field ◽  
Differential Equation ◽  
Magnetic Anomalies ◽  
Field Vector ◽  
Magnetic Data ◽  
Dipole Source ◽  
Point Dipole ◽  
Main Subject ◽  
Depth Extent ◽  
Dipole Sources

The concept of point‐pole and point‐dipole in interpretation of magnetic data is often employed in the analysis of magnetic anomalies (or their derivatives) caused by geologic bodies whose geometric shapes approach those of (1) narrow prisms of infinite depth extent aligned, more or less, in the direction of the inducing earth’s magnetic field, and (2) spheres, respectively. The two geologic bodies are assumed to be magnetically polarized in the direction of the Earth’s total magnetic field vector (Figure 1). One problem that perhaps is not realized when interpretations are carried out on such anomalies, especially in regions of high magnetic latitudes (45–90 degrees), is that of being unable to differentiate an anomaly due to a point‐pole from that due to a point‐dipole source. The two anomalies look more or less alike at those latitudes (Figure 2). Hood (1971) presented a graphical procedure of determining depth to the top/center of the point pole/dipole in which he assumed prior knowledge of the anomaly type. While it is essential and mandatory to make an assumption such as this, it is very important to go a step further and carry out a test on the anomaly to check whether the assumption made is correct. The procedure to do this is the main subject of this note. I start off by first using some method that does not involve Euler’s differential equation to determine depth to the top/center of the suspected causative body. Then I employ the determined depth to identify the causative body from the graphical diagram of Hood (1971, Figure 26).

scholarly journals Reply to Martino’s comments on “The normal, the natural, and the harmonic”

2021 ◽  
Author(s):  
THEODORE MODIS
Keyword(s):  
Differential Equation ◽  
Discrete Equation

This is my reply to Martino's comments on my publication “The normal, the natural, and the harmonic.” In this reply I show that the chaos equation, also known as the logistic discrete equation, is the same as the discretized logistic differential equation.

scholarly journals PhilDB - The time series database with built-in change logging

2015 ◽  
Author(s):  
Andrew MacDonald
Keyword(s):  
Time Series ◽  
Big Data ◽  
Open Source ◽  
High Performance ◽  
Time Series Data ◽  
Handling Time ◽  
Series Data ◽  
Meta Data ◽  
Static Data ◽  
Data Tracking

PhilDB is an open-source time series database. It supports storage of time series datasets that are dynamic, that is recording updates to existing values in a log as they occur. Recent open-source systems, such as InfluxDB and OpenTSDB, have been developed to indefinitely store long-period, high-resolution time series data. Unfortunately they require a large initial installation investment before use because they are designed to operate over a cluster of servers to achieve high-performance writing of static data in real time. In essence, they have a ‘big data’ approach to storage and access. Other open-source projects for handling time series data that don’t take the ‘big data’ approach are also relatively new and are complex or incomplete. None of these systems gracefully handle revision of existing data while tracking values that changed. Unlike ‘big data’ solutions, PhilDB has been designed for single machine deployment on commodity hardware, reducing the barrier to deployment. PhilDB eases loading of data for the user by utilising an intelligent data write method. It preserves existing values during updates and abstracts the update complexity required to achieve logging of data value changes. PhilDB improves accessing datasets by two methods. Firstly, it uses fast reads which make it practical to select data for analysis. Secondly, it uses simple read methods to minimise effort required to extract data. PhilDB takes a unique approach to meta-data tracking; optional attribute attachment. This facilitates scaling the complexities of storing a wide variety of data. That is, it allows time series data to be loaded as time series instances with minimal initial meta-data, yet additional attributes can be created and attached to differentiate the time series instances as a wider variety of data is needed. PhilDB was written in Python, leveraging existing libraries. This paper describes the general approach, architecture, and philosophy of the PhilDB software.

An Edge Device for Personal Big Data Tracking

2021 ◽  
Author(s):  
Haichao Huang ◽  
Yuqing Xie ◽  
Zhangchi Ying ◽  
Chang Yao ◽  
Peng Lu ◽  
...  
Keyword(s):  
Big Data ◽  
Data Tracking

The Water Level Automatic Measurement Technology Based on Image Processing

2013 ◽  
Vol 303-306 ◽  
pp. 621-626 ◽  
Author(s):  
Hui Ping Li ◽  
Wei Wang ◽  
Fu Chang Ma ◽  
Hong Le Liu ◽  
Tao Lv
Keyword(s):  
Water Level ◽  
Real Time ◽  
Automatic Measurement ◽  
Measurement Technology ◽  
Arm Processor ◽  
Straight Line ◽  
System Use ◽  
Characteristic Features ◽  
Combination Of Methods ◽  
Hydrological Monitoring

Design a real-time hydrological monitoring system. Using the ARM processor and video server combination of methods, realize the characteristic features of the next place machine extraction, and then through the GPRS connect PC . Also have the power supply, low power consumption, anti-interference characteristics. The system use Wince programming, to obtain the binary image, using morphological algorithm to remove useless characteristic features, then carries on the edge refinement, use the hough transform to extract the straight line equation of water level and the bank line to compute, can get out the distance between the two in the image, according to the actual coordinate can get the actual distance. The experimental results show that the system has a real time and efficiency, effectiveness, and other characteristics, get a good recognition effect.

scholarly journals Big Data Energy Consumption Monitoring Technology of Obese Individuals Based on MEMS Sensor

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yongjun Zhao ◽  
Juan Zhao ◽  
Liang Ding ◽  
Congcong Xie
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Inertial Sensor ◽  
Algorithm Design ◽  
Energy Detection ◽  
Obese Individual ◽  
Human Motion ◽  
Human Beings ◽  
Motion Recognition ◽  
Mems Sensor

The application of micro electro mechanical system (MEMS) is more and more extensive, involving military, medical, communication and other major fields. The progress of science and technology has brought cross era changes to human beings, but also brought troubles to human beings. Because machines can replace most people, which leads to a significant reduction in human exercise, many people have the symptoms of obesity. Therefore, how to effectively detect human exercise energy consumption is of great significance to improve obesity symptoms. The energy consumption detector takes stm32f103zet6 as the core processor and uses the inertial sensor mpu6050 to build a MEMS sensor system to monitor the daily motion state and gait of human body in real time. In the design of the big data algorithm, the adaptive peak detection and step, decision tree two-level classification of motion recognition big data algorithm are organically integrated, and then combined with the acceleration vector value of the motion energy detection big data algorithm, to process the collected motion data, including the acceleration signal, gyroscope and other data processing, and finally complete the feature extraction, get the final recognition and detection results. Through the data reference, we can know that the system can recognize different human motion states. Among them, it has 95% accuracy in the motion recognition of sitting, standing, walking, running, going up and down stairs and lying back, which is basically the same as the top detectors on the market. In the energy consumption detection, it also has 95% accuracy, which proves the correctness of the experimental big data algorithm design, and also improves the accuracy It is proved that the system has good performance and high practicability, and can provide a new idea for obese individual motion detection.

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