Research on Automatic Magnetic Deviation Compensation of Electronic Compass for Quadrotor

2013 ◽  
Vol 347-350 ◽  
pp. 995-999
Author(s):  
Wen Jian Xiao ◽  
Ping Lu
Keyword(s):  
Measurement Error ◽  
Real Time ◽  
Horizontal Plane ◽  
Magnetic Sensor ◽  
Experimental Results ◽  
Sensor Data ◽  
Automatic Compensation ◽  
Time Performance ◽  
Heading Angle ◽  
Electronic Compass

In magnetic heading measurement of quadrotor, it is necessary to compensate the magnetic deviation of the electronic magnetic compass. For the flight characteristics of quadrotor, a method using sampling data when the aircraft is in the air to achieve automatic compensation of the deviation has been proposed. Using elliptical hypothesis method one can only compensate deviation for quadrotor flying in horizontal plane by collecting the magnetic sensor data in different directions. When the quadrotor flying in any attitudes, using ellipsoid hypothesis method can obtain parameters used by magnetic deviation compensation. Using these parameters, the pitch and roll angles of quadrotor, the magnetic deviation could be compensated. Experimental results show that this method works well and has a good real time performance. The measurement error of heading angle could be less than 1 °.

Heading Estimation Based on Magnetic Markers for Intelligent Vehicles

2016 ◽  
Vol 138 (7) ◽  
Author(s):  
Yeun Sub Byun ◽  
Young Chol Kim
Keyword(s):  
Real Time ◽  
Autonomous Vehicle ◽  
Kinematic Model ◽  
Estimation Method ◽  
Intelligent Vehicles ◽  
Experimental Results ◽  
Main Concept ◽  
Heading Angle ◽  
Magnetic Markers ◽  
Heading Estimation

This paper presents a new real-time heading estimation method for an all-wheel steered single-articulated autonomous vehicle guided by a magnetic marker system. To achieve good guidance control for the vehicle, precise estimation of the position and heading angle during travel is necessary. The main concept of this study is to estimate the heading angle from the relative orientations of the magnetic markers and the vehicle motion. To achieve this, a kinematic model of the all-wheel steered vehicle is derived and combined with the motion of a magnetic ruler mounted near each axle underneath the vehicle. The position coordinates and polarities of the magnetic markers, which are provided a priori, are used to determine the vehicle position at every detection instance. A gyroscope is employed to assist real-time heading estimation at sample times when there are no marker detection data. The proposed method was tested on a real vehicle and evaluated by comparing the experimental results with those of the differential global positioning system (DGPS) in real-time kinematics (RTK) mode. Experimental results show that the proposed method exhibits good performance for heading estimation.

Sensitive Webpage Filter Based on Multiple Filtering

2012 ◽  
Vol 241-244 ◽  
pp. 2891-2896
Author(s):  
Dong Mei Wang ◽  
Ming Ma ◽  
Yan Sun
Keyword(s):  
Real Time ◽  
Face Detection ◽  
Text Detection ◽  
Experimental Results ◽  
Skin Detection ◽  
Time Requirement ◽  
The Real ◽  
Time Performance

In order to improve the accuracy and real-time performance of webpage filtering, a sensitive webpage filter based on multiple filtering was designed. Firstly, the URL is gained from IE browser’s address bar by BHO technology; Secondly, match the webpage text with sensitive vocabulary database using SMA algorithm; Finally, use the sensitive image detecting algorithm combing face detection, skin detection, skin text detection and classification to filter sensitive images in the webpage. The Simulation experimental results showed that the sensitive webpage filter can effectively intercept and filter sensitive webpages, meeting the accuracy and the real-time requirement of webpage filtering.

Real-time digital simulator for distributed systems

SIMULATION ◽  
2021 ◽  
pp. 003754972098686
Author(s):  
Jinchao Chen ◽  
Chenglie Du ◽  
Pengcheng Han ◽  
Xiaoyan Du
Keyword(s):  
Distributed Systems ◽  
Real Time ◽  
Experimental Results ◽  
Data Driven ◽  
New Approach ◽  
Support Tool ◽  
Time Performance ◽  
Simulator Design ◽  
Data Buses ◽  
Real Time Digital Simulator

Simulation has been widely adopted as a support tool for the validation and experimentation of distributed systems. It allows different devices and applications to be evaluated and analyzed without requiring the actual presence of those machines. Although the simulation plays an important role in investigating and evaluating the behaviors of devices, it results in a serious simulator building problem as the distributed systems become more and more complicated and dynamically data driven. Most of the existing simulators are designed and developed to target a specific type of application, lacking the capabilities to be a configurable and standardized tool for researchers. To solve the adaptability and reusability problems of simulators, this paper proposes a new approach to design and implement a configurable real-time digital simulator for hardware devices that are connected via data buses in distributed systems. First, the proposed simulator uses a logic automaton to simulate the activities of a real device, and generates the incentive data for tested equipment according to the predefined XML-based files. Then with a virtual bus, the simulator can receive, handle, and send data in various network environments, improving the flexibility and adaptability of a simulator design. Experimental results show that the proposed simulator has a high real-time performance, and can meet the increasing requirements of modern simulations of distributed systems.

scholarly journals Adaptive-Neuro-Fuzzy-Based Information Fusion for the Attitude Prediction of TBMs

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 61
Author(s):  
Boning He ◽  
Guoli Zhu ◽  
Lei Han ◽  
Dailin Zhang
Keyword(s):  
Measurement Error ◽  
Real Time ◽  
Information Fusion ◽  
High Performance ◽  
Fuzzy Inference ◽  
Experimental Results ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Rate Information ◽  
Attitude Prediction

In a tunneling boring machine (TBM), to obtain the attitude in real time is very important for a driver. However, the current laser targeting system has a large delay before obtaining the attitude. So, an adaptive-neuro-fuzzy-based information fusion method is proposed to predict the attitude of a laser targeting system in real time. In the proposed method, a dual-rate information fusion is used to fuse the information of a laser targeting system and a two-axis inclinometer, and then obtain roll and pitch angles with a higher rate and provide a smoother attitude prediction. Considering that a measurement error exists, the adaptive neuro-fuzzy inference system (ANFIS) is proposed to model the measurement error, and then the ANFIS-based model is combined with the dual-rate information fusion to achieve high performance. Experimental results show the ANFIS-based information fusion can provide higher real-time performance and accuracy of the attitude prediction. Experimental results also verify that the ANFIS-based information fusion can solve the problem of the laser targeting system losing signals.

scholarly journals Machine-learning-based children’s pathological gait classification with low-cost gait-recognition system

2021 ◽  
Author(s):  
Linghui Xu ◽  
Jiansong Chen ◽  
Fei Wang ◽  
Yuting Chen ◽  
Wei Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Plantar Pressure ◽  
Recognition Accuracy ◽  
Low Cost ◽  
Gait Recognition ◽  
Recognition System ◽  
Experimental Results ◽  
Time Performance ◽  
Pathological Gait

Abstract Background: Pathological gaits of children may lead to terrible diseases, such as osteoarthritis or scoliosis. By monitoring the gait pattern of a child, proper therapeutic measures can be recommended to avoid the terrible consequence. However, low-cost systems for pathological gait recognition of children automatically have not been on market yet. Our goal was to design a low-cost gait-recognition system for children with only pressure information.Methods: In this study, we design a pathological gait-recognition system (PGRS) with an 8 × 8 pressure-sensor array. An intelligent gait-recognition method (IGRM) based on machine learning and pure plantar pressure information is also proposed in static and dynamic sections to realize high accuracy and good real-time performance. To verifying the recognition effect, a total of seventeen children were recruited in the experiments wearing PGRS to recognize three pathological gaits (toe in, toe out, and flat) and normal gait. Children are asked to walk naturally on level ground in the dynamic section or stand naturally and comfortably in the static section. The evaluation of the performance of recognition results included stratified 10-fold cross-validation with recall, precision, and a time cost as metrics.Results: The experimental results show that all of the IGRMs have been identified with a practically applicable degree of average accuracy either in the dynamic or static section. Experimental results indicate that the IGRM has 92.41% and 97.79% recognition accuracy respectively in the static and dynamic sections. And we find methods in the static section have less recognition accuracy due to the unnatural gesture of children when standing.Conclusions: In this study, a low-cost PGRS has been verified and realize feasibility, highly average precision, and good real-time performance of gait recognition. The experimental results reveal the potential for the computer supervision of non-pathological and pathological gaits in the plantar-pressure patterns of children and for providing feedback in the application of gait-abnormality rectification.

Improved SURF Descriptor Based on Triangle Partition

2013 ◽  
Vol 718-720 ◽  
pp. 2296-2301 ◽  
Author(s):  
Zeng Luan ◽  
Zhai You ◽  
Xiong Wei
Keyword(s):  
Real Time ◽  
Image Matching ◽  
Weighting Function ◽  
Boundary Effect ◽  
Haar Wavelet ◽  
Local Region ◽  
Experimental Results ◽  
Time Performance ◽  
Wavelet Response ◽  
Better Than

In order to improve the robustness and real time performance of SURF based image matching algorithms, a new descriptor is proposed. We compute the new descriptor in a rectangle local region (the side set to 20s). Firstly, the local region is divided into 8 equal triangle subregion. Secondly, local region location grid is rotated to align its dominate orientation to a canonical direction. The keypoint dominate orientation and its orthogonalorientation is defined as the x and y directions of the descriptors local coordinate system.Thirdly, compute the Haar wavelet response in x and y directions within the keypoint local region. In order to reduce the boundary effect and outer noise, Haar wavelet response in the same Grid of different triangle is both assigned to each triangle in different weight, and then a gaussian weighting function is used. Compute the histogram of Haar wavelet response and absolute Haar wavelet response, so each triangle subregion constitutes a vector with 4 dimensions. Finally, a descriptor with 32 dimensions is constituted and the descriptor is normalized to achieve illumination invariance. The experimental results show that the performance of the new descriptor is even better than SURF descriptor.

scholarly journals Smart Pack: Online Autonomous Object-Packing System Using RGB-D Sensor Data

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4448
Author(s):  
Young-Dae Hong ◽  
Young-Joo Kim ◽  
Ki-Baek Lee
Keyword(s):  
Cost Function ◽  
Real Time ◽  
Online Optimization ◽  
Experimental Results ◽  
Sensor Data ◽  
Optimal Position ◽  
Optimization Criteria ◽  
Time Calculation ◽  
The Cost ◽  
Packing System

This paper proposes a novel online object-packing system which can measure the dimensions of every incoming object and calculate its desired position in a given container. Existing object-packing systems have the limitations of requiring the exact information of objects in advance or assuming them as boxes. Thus, this paper is mainly focused on the following two points: (1) Real-time calculation of the dimensions and orientation of an object; (2) Online optimization of the object’s position in a container. The dimensions and orientation of the object are obtained using an RGB-D sensor when the object is picked by a manipulator and moved over a certain position. The optimal position of the object is calculated by recognizing the container’s available space using another RGB-D sensor and minimizing the cost function that is formulated by the available space information and the optimization criteria inspired by the way people place things. The experimental results show that the proposed system successfully places the incoming various shaped objects in their proper positions.

A Smart Wiper System Based on Feedforward-Feedback Composite Controller

2012 ◽  
Vol 472-475 ◽  
pp. 1446-1450
Author(s):  
Xian Zhong Ren ◽  
Mei Fu Luo ◽  
Ya Nan Qiu
Keyword(s):  
Real Time ◽  
Software Design ◽  
Good Accuracy ◽  
Experimental Results ◽  
Time Performance ◽  
Key Factor ◽  
Feedback Algorithm

The velocity of the wiper arms is a key factor which can influence the clarity of the windshield and the comfort of the driver’s view. So how to control the velocity of the wiper arms is important. To solve the problem, a method is elaborated based on feedforward-feedback composite controller. It introduces the hardware and software design based on the feedforward-feedback algorithm, controlling the velocity of the wiper arms by adjusting the duty of PWM. The experimental results indicate that the smart wiper system is reliable, and has good accuracy and real-time performance.

scholarly journals A Signature Identification Method Based on Strength and Strokes Direction

2011 ◽  
Vol 2-3 ◽  
pp. 223-227
Author(s):  
Hai Long Huang ◽  
Hong Wang ◽  
Li Ji
Keyword(s):  
Real Time ◽  
Mathematical Morphology ◽  
Experimental Results ◽  
Time Performance ◽  
New Methods ◽  
Identification Method ◽  
Available Information

It was much more complex and difficult for off-line signature identification attributable to the limitation of available information. To solve the problem, a signature identification method based on strength and strokes direction was proposed. The signature image acquired was gray-scaled and filtered at the stage of preprocess; then the image was two-valued with different threshold based on strength feature, the regions which grayscale was less than threshold were retained; the strokes which possess distinctive directional feature were extracted by using mathematical morphology and combining different scales/directions structure element based on strokes direction feature; at last judgement was maked for sample in accordance with corresponding feature. Experimental results showed the proposed method can enhance accurate rate effectively, improve real-time performance, which was a try beneficial to apply new methods for off-line signature identification.

scholarly journals Machine-learning-based children’s pathological gait classification with low-cost gait-recognition system

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Linghui Xu ◽  
Jiansong Chen ◽  
Fei Wang ◽  
Yuting Chen ◽  
Wei Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Plantar Pressure ◽  
Recognition Accuracy ◽  
Low Cost ◽  
Gait Recognition ◽  
Recognition System ◽  
Experimental Results ◽  
Time Performance ◽  
Pathological Gait

Abstract Background Pathological gaits of children may lead to terrible diseases, such as osteoarthritis or scoliosis. By monitoring the gait pattern of a child, proper therapeutic measures can be recommended to avoid the terrible consequence. However, low-cost systems for pathological gait recognition of children automatically have not been on market yet. Our goal was to design a low-cost gait-recognition system for children with only pressure information. Methods In this study, we design a pathological gait-recognition system (PGRS) with an 8 × 8 pressure-sensor array. An intelligent gait-recognition method (IGRM) based on machine learning and pure plantar pressure information is also proposed in static and dynamic sections to realize high accuracy and good real-time performance. To verifying the recognition effect, a total of 17 children were recruited in the experiments wearing PGRS to recognize three pathological gaits (toe-in, toe-out, and flat) and normal gait. Children are asked to walk naturally on level ground in the dynamic section or stand naturally and comfortably in the static section. The evaluation of the performance of recognition results included stratified tenfold cross-validation with recall, precision, and a time cost as metrics. Results The experimental results show that all of the IGRMs have been identified with a practically applicable degree of average accuracy either in the dynamic or static section. Experimental results indicate that the IGRM has 92.41% and 97.79% intra-subject recognition accuracy, and 85.78% and 78.81% inter-subject recognition accuracy, respectively, in the static and dynamic sections. And we find methods in the static section have less recognition accuracy due to the unnatural gesture of children when standing. Conclusions In this study, a low-cost PGRS has been verified and realize feasibility, highly average precision, and good real-time performance of gait recognition. The experimental results reveal the potential for the computer supervision of non-pathological and pathological gaits in the plantar-pressure patterns of children and for providing feedback in the application of gait-abnormality rectification.

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