scholarly journals Development of Liquid Capacity Measuring Algorithm Based on Data Integration from Multiple Sensors

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Kiwoong Park ◽  
Si-Kyoung Lee ◽  
Hyeon Cheol Kim
Keyword(s):  
Kalman Filter ◽  
Data Integration ◽  
Real Time ◽  
Rotation Angle ◽  
Sensor Data ◽  
Multiple Sensors ◽  
Complementary Filter ◽  
Level Sensor

This research proposes an algorithm using a process of integrating data from multiple sensors to measure the liquid capacity in real time regardless of the position of the liquid tank. The algorithm for measuring the capacity was created with a complementary filter using a Kalman filter in order to revise the level sensor data and IMU sensor data. The measuring precision of the proposed algorithm was assessed through repetitive experiments by varying the liquid capacity and the rotation angle of the liquid tank. The measurements of the capacity within the liquid tank were precise, even when the liquid tank was rotated. Using the proposed algorithm, one can obtain highly precise measurements, and it is affordable since an existing level sensor is used.

scholarly journals Deep Learning Based Data Fusion for Sensor Fault Diagnosis and Tolerance in Autonomous Vehicles

2020 ◽  
Author(s):  
Huihui Pan ◽  
Weichao Sun ◽  
Qiming Sun ◽  
Huijun Gao
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Environmental Perception ◽  
Sensor Data ◽  
Time Data ◽  
Multiple Sensors ◽  
Perception System

Abstract Environmental perception is one of the key technologies to realize autonomous vehicles. Autonomous vehicles are often equipped with multiple sensors to form a multi-source environmental perception system. Those sensors are very sensitive to light or background conditions, which will introduce a variety of global and local fault signals that bring great safety risks to autonomous driving system during long-term running. In this paper, a real-time data fusion network with fault diagnosis and fault tolerance mechanism is designed. By introducing prior features to realize the lightweight of the backbone network, the features of the input data can be extracted in real time accurately. Through the temporal and spatial correlation between sensor data, the sensor redundancy is utilized to diagnose the local and global condence of sensor data in real time, eliminate the fault data, and ensure the accuracy and reliability of data fusion. Experiments show that the network achieves the state-of-the-art results in speed and accuracy, and can accurately detect the location of the target when some sensors are out of focus or out of order.

scholarly journals A Stream Processing System for Multisource Heterogeneous Sensor Data

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Liang Hu ◽  
Rui Sun ◽  
Feng Wang ◽  
Xiuhong Fei ◽  
Kuo Zhao
Keyword(s):  
Data Integration ◽  
Data Processing ◽  
Real Time ◽  
Rapid Development ◽  
Streaming Data ◽  
Sensor Data ◽  
Low Latency ◽  
Time System ◽  
Real Time System ◽  
Set Up

With the rapid development of the Internet of Things (IoT), a variety of sensor data are generated around everyone’s life. New research perspective regarding the streaming sensor data processing of the IoT has been raised as a hot research topic that is precisely the theme of this paper. Our study serves to provide guidance regarding the practical aspects of the IoT. Such guidance is rarely mentioned in the current research in which the focus has been more on theory and less on issues describing how to set up a practical system. In our study, we employ numerous open source projects to establish a distributed real time system to process streaming data of the IoT. Two urgent issues have been solved in our study that are (1) multisource heterogeneous sensor data integration and (2) processing streaming sensor data in real time manner with low latency. Furthermore, we set up a real time system to process streaming heterogeneous sensor data from multiple sources with low latency. Our tests are performed using field test data derived from environmental monitoring sensor data collected from indoor environment for system validation. The results show that our proposed system is valid and efficient for multisource heterogeneous sensor data integration and streaming data processing in real time manner.

scholarly journals Real-time and Low-cost IoT based farming using raspberry Pi

2020 ◽  
Vol 17 (1) ◽  
pp. 197
Author(s):  
Md. Wahidur Rahman ◽  
Md. Elias Hossain ◽  
Rahabul Islam ◽  
Md. Harun Ar Rashid ◽  
Md. Nur A Alam ◽  
...  
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Ph Sensor ◽  
Sensor Data ◽  
Future Application ◽  
Raspberry Pi ◽  
Moisture Sensor ◽  
Security Issues ◽  
Temperature And Humidity ◽  
Level Sensor

<span>This paper reflects on the implementation of IoT enabled Farming, especially for the people needed a smart way of agriculture. This research focuses on real-time observation with efficient use of cheapest security system. The features of this research including i) Sensor data monitoring using soil moisture sensor which is responsible for measuring moisture of the filed, water level sensor which is liable for detecting flooded water, pH sensor which is accountable for measuring pH of the soil and Temperature and humidity sensor which is responsible for tracking out the present temperature and humidity in the atmosphere ii) Live monitoring of sensor’s value using cloud and a Dashboard iii) Security issues of the farming using Laser shield and IP-Camera through Wi-Fi which is conducted by android application. This paper also assures the analysis of the experimented data through various sensor’s value and gives a momentous way for future application. Result and discussion ensures the contribution in the field of Internet of things</span>

Integrated Navigation Models of a Mobile Fodder-Pushing Robot Based on a Standardized Cow Husbandry Environment

2020 ◽  
Vol 63 (2) ◽  
pp. 221-230
Author(s):  
Shenghui Yang ◽  
Shenghao Liang ◽  
Yongjun Zheng ◽  
Yu Tan ◽  
Zhang Xiao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Time Course ◽  
Satellite System ◽  
Integrated Navigation ◽  
Multiple Sensors ◽  
Motion Models ◽  
Outdoor Environments ◽  
Global Navigation Satellite ◽  
Smart Mobile

HighlighIntegrated navigation models for a two-wheel robot were specifically developed for a semi-enclosed environment.A combination of Kalman filter and fuzzy control system was developed with mathematical models.Real-time pose estimate and adjustment of perturbances due to feeding cows and fodder resistance were achieved.Abstract. As part of welfare feeding, standardized feeding is commonly used for cows in confined operations. Due to the strict facility requirements, smart mobile robots have been specifically developed to address these semi-enclosed environments. Their navigation is based on electromagnetic sensors with magnetic tapes, which does not easily allow route changes and other abilities afforded by the newer integrated sensors and Global Navigation Satellite System (GNSS) guidance packages available on large agricultural machinery in outdoor environments. This article proposes a system of integrated navigation using multiple sensors, which was used for a two-wheel-drive robot operating in the standardized environment of a cow husbandry facility. The developed system combined incremental encoders, ultrasonic sensors, and a gyroscope to determine parameters such as course angle and covered distance. A fuzzy self-adaption Kalman filter was applied to integrate these parameters and estimate the robot pose, so that the robot could achieve real-time course adjustment during operation. Experimental trials indicated that the real-world route was highly consistent with the set route. Moreover, the cross-track error was =0.10 m at a travel velocity of 0.2 m s-1, indicating that perturbances due to feeding cows and fodder resistance had little interference on the movement of the robot, and the models were robust and accurate. This novel integrated sensing system with a fuzzy self-adaption Kalman filter and derived models was able to guide real-time robot operations in a modern cow husbandry environment without the need for magnetic tapes. Keywords: Kalman filter, Integrated navigation, Motion models, Pose estimate, Welfare feeding.

scholarly journals Estimasi Sudut Rotasi Benda Kaku Berbasis IMU Menggunakan Kalman Filter

AVITEC ◽  
2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Lasmadi Lasmadi ◽  
Freddy Kurniawan ◽  
Muhammad Irfan Pamungkas
Keyword(s):  
Kalman Filter ◽  
Rotation Angle ◽  
Estimation Algorithm ◽  
Sensor Data ◽  
Estimation Model ◽  
Accuracy Rate ◽  
Improve Measurement ◽  
Gyroscope Sensor ◽  
Dynamics Models ◽  
Angle Of Rotation

Rotation angle estimates are often required and applied to the dynamics of spacecraft, UAVs, robots, underwater vehicles, and other systems before control. IMU is an electronic module that is used as an angle estimation tool but has noise that can reduce the accuracy of the estimation. This study aims to develop an estimation model for the angle of rotation of a rigid body based on the IMU-gyroscope sensor on a smartphone using a Kalman filter. The estimation model is developed in a simple dynamic equation of motion in state-space. Kalman filters are designed based on system dynamics models to reduce noise in sensor data and improve measurement estimation results. Simulations are carried out with software to investigate the accuracy of the developed estimation algorithm. Experiments were carried out on several smartphone rotations during the roll, pitch, and yaw. Then, the experimental data obtained is analyzed for accuracy by comparing the built-in algorithms on smartphones. Based on the experimental results, the accuracy rate of estimation angle is 94% before going through the Kalman filter and an accuracy level of above 98% after going through the Kalman filter for every rotation on the x-axis, y-axis, and z-axis.

scholarly journals Real-Time Inverse Estimation of Ocean Wave Spectra from Vessel-Motion Sensors Using Adaptive Kalman Filter

2019 ◽  
Vol 9 (14) ◽  
pp. 2797 ◽  
Author(s):  
HanSung Kim ◽  
HeonYong Kang ◽  
Moo-Hyun Kim
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
High Frequency ◽  
Practical Importance ◽  
Ocean Wave ◽  
Sensor Data ◽  
Wave Spectra ◽  
Inverse Estimation ◽  
High Frequency Waves ◽  
Vessel Motion

The real-time inverse estimation of the ocean wave spectrum and elevation from a vessel-motion sensor is of significant practical importance, but it is still in the developing stage. The Kalman-filter method has the advantages of real-time estimation, cost reduction, and easy installation than other methods. Reasonable estimation of high-frequency waves is important in view of covering various sea states. However, if the vessel is less responsive for high-frequency waves, amplified noise may occur and cause overestimation problem there. In this paper, a configuration of Kalman filter with applying the principle of Wiener filter is proposed to suppress those over-estimations. Over-estimation is significantly reduced at high frequencies when the method is applied, and reliable real-time wave spectra and elevations can be obtained. The simulated sensor data was used, but the proposed algorithm has been proved to perform well for various sea states and different vessels. In addition, the proposed Kalman-filter technique is robust when it is applied to time-varying sea states.

Sign in / Sign up

Export Citation Format

Share Document