scholarly journals DESIGN AND EXPERIMENT OF A HIGH PAYLOAD FIXED WING VTOL UAV SYSTEM FOR EMERGENCY RESPONSE

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1715-1722
Author(s):  
H. Wu ◽  
Z. Wang ◽  
B. Ren ◽  
L. Wang ◽  
J. Zhang ◽  
...  
Keyword(s):  
Emergency Response ◽  
Inertial Navigation System ◽  
High Accuracy ◽  
Video Data ◽  
Multiple Sensors ◽  
Mosaic Image ◽  
Vtol Uav ◽  
Processing Software ◽  
Design And Practice ◽  
High Payload

Abstract. With the development of UAV technologies, the advantages of hybrid VTOL UAV have been realized and taken in emergency response. But, former hybrid VTOL UAV is lack of capacities on payload and endurance, which restrict the integration of multiple sensors. In this paper, a high payload fixed wing VTOL UAV, which has 20 kg payload and more than 3 hours endurance, is used to design a UAV system for emergency response. Multiple sensors including an optronics pod, PhaseOne IXM-100 camera, high accuracy inertial navigation system and three-axis stable head are integrated with it. Based on this, specific processing software is developed to process the video data and image which could meet the requirements of emergency response in different stages. Experiment results shown that the precision of mosaic image is about 10m and the precision of orthoimage is about 1m. This work could be reference for the design and practice of UAV system with multiple sensors.

Image-Based Versus Signal-Based Sensors for Machine Fault Detection and Isolation

2014 ◽  
Author(s):  
Heshan Fernando ◽  
Vedang Chauhan ◽  
Brian Surgenor
Keyword(s):  
Fault Detection ◽  
High Speed ◽  
Fault Isolation ◽  
Video Data ◽  
Fault Detection And Isolation ◽  
Single Camera ◽  
Multiple Faults ◽  
Multiple Sensors ◽  
Machine Fault ◽  
And Performance

This paper presents the results of a comparative study that investigated the use of image-based and signal-based sensors for fault detection and fault isolation of visually-cued faults on an automated assembly machine. The machine assembles 8 mm circular parts, from a bulk-supply, onto continuously moving carriers at a rate of over 100 assemblies per minute. Common faults on the machine include part jams and ejected parts that occur at different locations on the machine. Two sensor systems are installed on the machine for detecting and isolating these faults: an image-based system consisting of a single camera and a signal-based sensor system consisting of multiple greyscale sensors and limit switches. The requirements and performance of both systems are compared for detecting six faults on the assembly machine. It is found that both methods are able to effectively detect the faults but they differ greatly in terms of cost, ease of implementation, detection time and fault isolation capability. The conventional signal-based sensors are low in cost, simple to implement and require little computing power, but the installation is intrusive to the machine and readings from multiple sensors are required for faster fault detection and isolation. The more sophisticated image-based system requires an expensive, high-resolution, high-speed camera and significantly more processing power to detect the same faults; however, the system is not intrusive to the machine, fault isolation becomes a simpler problem with video data, and the single camera is able to detect multiple faults in its field of view.

Wireless Real-Time Telemetry Automatic System for Monitoring the Settlement of Structure and its Application

2012 ◽  
Vol 468-471 ◽  
pp. 1110-1113
Author(s):  
Guo Liang Zhang ◽  
Xue Min Zhang ◽  
Jie Li ◽  
Shu Ying Wang
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Automatic System ◽  
Intelligent System ◽  
High Accuracy ◽  
Normal Operation ◽  
Data Processing Software ◽  
Displacement Sensors ◽  
Processing Software ◽  
Time Acquisition

An intelligent system, which is real-time, remote, and automatic for monitoring the settlement of surface subsidence or building, was developed based on the principle of communicating vessels. The Hydrostatic leveling, displacement sensors, data acquisition and transmission, 3G wireless communications module, computer terminals, and data processing software were integrated in the system, which has the following functions: monitoring data real-time acquisition and transmission, automatic calculation and analysis of the settlement, and early warning by remote wireless mode. The system has been successfully applied in the Minzhi station project of Shenzhen Metro Line 5 to ensure the normal operation of existing railway. Meanwhile, the high accuracy and reliability of the system are also verified by the engineering applications. It can be applied to monitor the settlement of similar projects.

scholarly journals Intelligent Querying in Camera Networks for Efficient Target Tracking

2019 ◽  
Author(s):  
Anil Sharma
Keyword(s):  
Target Tracking ◽  
Decision Process ◽  
Visual Analytics ◽  
High Accuracy ◽  
Video Data ◽  
Camera Network ◽  
Markov Decision ◽  
Target Templates ◽  
The Individual ◽  
Identification Techniques

Visual analytics applications often rely on target tracking across a network of cameras for inference and prediction. A network of cameras generates immense amount of video data and processing it for tracking a target is highly computationally expensive. Related works typically use data association and visual re-identification techniques to match target templates across multiple cameras. In this thesis, I propose to formulate this scheduling problem as a Markov Decision Process (MDP) and present a reinforcement learning based solution to schedule cameras by selecting one where the target is most likely to appear next. The proposed approach can be learned directly from data and doesn't require any information of the camera network topology. NLPR MCT and DukeMTMC datasets are used to show that the proposed policy significantly reduces the number of frames to be processed for tracking and identifies the camera schedule with high accuracy as compared to the related approaches. Finally, I will be formulating an end-to-end pipeline for target tracking that will learn a policy to find the camera schedule and to track the target in the individual camera frames of the schedule.

RESEARCH ON APPLICATION OF NEURAL NETWORKS FOR RESTORING DAMAGED VIDEO FILES OF AVI AND MP4 FORMATS

2021 ◽  
Vol 55 (3) ◽  
pp. 25-32
Author(s):  
GERASKIN ALEKSEY S. ◽  
◽  
UKOLOV RODION V. ◽  
Keyword(s):  
Neural Network ◽  
Video Compression ◽  
High Accuracy ◽  
Video Data ◽  
Modern World ◽  
Special Role ◽  
Compression Algorithms ◽  
Video File ◽  
Software Product ◽  
Research On Application

In the modern world, video files play a special role. The development of video compression algorithms and the growth of the Internet’s capabilities make it possible to transfer video files. Damage inevitably occurs when transferring video files. Accordingly, the question arises about restoring a damaged file and obtaining information from it. The article discusses the most commonly used video file extensions AVI, MP4. As a result of the study, it was revealed that the most common damage is in the headers, which leads to errors when opening files by players, data is damaged less often. Data corruption leads to the fact that a certain fragment of the video file is either played with errors, distortions, or is skipped. The article discusses the possibility of recovering damaged video file using the removal of undistorted data and proposes an algorithm for analyzing frames using a neural network. As part of the algorithm, a neural network is used to identify damaged frames in video data. The algorithm was implemented as a software product. For the first stage of checking the efficiency of the algorithm, deliberate distortions of one frame were made for each video file under study. As a result of experimental verification of the developed algorithm, it was revealed that it provides high accuracy in detecting distorted frame sequences.

scholarly journals Study on Initial Gravity Map Matching Technique Based on Triangle Constraint Model

2015 ◽  
Vol 69 (2) ◽  
pp. 353-372 ◽  
Author(s):  
Zhu Zhuangsheng ◽  
Guo Yiyang ◽  
Yang Zhenli
Keyword(s):  
Inertial Navigation System ◽  
High Accuracy ◽  
Map Matching ◽  
Initial Error ◽  
Matching Algorithm ◽  
Matching Technique ◽  
Contour Point ◽  
Constraint Model ◽  
Short Time ◽  
Gravity Map

In this paper, a gravity map-matching algorithm is proposed based on a triangle constraint model. A high-accuracy triangle constraint model is constructed by using a short time and high-accuracy-featured inertial navigation system. In this paper, the principle of the gravity map-matching algorithm based on the triangle constraint model and a triangle matching parameter-parsing method are first introduced in detail. It is verified by test that the method is sensitive to the initial error value. By comparison to the commonly used Iterative Closest Contour Point (ICCP) and Sandia Inertial Terrain Aided Navigation (SITAN) algorithms respectively, the results show that this method is perfect in real-time performance and reliability, and its advantages are more obvious especially with a large initial error.

Validation of an Agent-based Microscopic Pedestrian Simulation Model at the Pedestrian Walkway of Brooklyn Bridge

2018 ◽  
Vol 2672 (35) ◽  
pp. 33-45 ◽  
Author(s):  
Mohamed Hussein ◽  
Tarek Sayed
Keyword(s):  
Simulation Model ◽  
High Accuracy ◽  
Video Data ◽  
Model Parameters ◽  
Actual Behavior ◽  
Agent Based ◽  
Optimum Model ◽  
Using Data ◽  
Brooklyn Bridge ◽  
Pedestrian Simulation

The objective of this study is to validate a recently developed agent-based pedestrian simulation model, using data collected at the pedestrian walkway of Brooklyn Bridge. Video data were collected at the walkway and the trajectories of 294 pedestrians were extracted using computer vision. A genetic algorithm was applied to identify the optimum model parameters that minimize the error between the simulated and the actual trajectories of the calibration dataset. The simulation model was then applied to reproduce the trajectories of 214 pedestrians, considered for validation. The validation results showed that the model was capable of producing pedestrian trajectories with high accuracy, as the average location error between actual and simulated trajectories was for 0.32 m, while the average speed error was 0.06 m/s. Macroscopic results of the model were assessed by comparing the density–speed relationship in both actual data and the simulation. Finally, the accuracy of the model in reproducing the actual behavior of pedestrians during different interactions was evaluated. Results showed that the model was capable of handling these interactions with high accuracy, ranged between 79% and 100%.

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