scholarly journals HS-SA-Based Precise Modeling of the Aircraft Fuel Center of Gravity Using Sensors Data

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2457 ◽  
Author(s):  
Xiaoming Guo ◽  
Jing Zhang ◽  
Lin Tie ◽  
Mingqiang Luo
Keyword(s):  
Data Storage ◽  
Heuristic Search ◽  
Large Data ◽  
Center Of Gravity ◽  
Sensor Data ◽  
Nonlinear Characteristics ◽  
Modeling Methods ◽  
Aircraft Fuel ◽  
Multidimensional Interpolation ◽  
Temporal Efficiency

The traditional modeling methods of aircraft fuel center of gravity (CG) based on sensor data have some disadvantages, such as large data storage requirements and low computational efficiency. In this article, a novel hybrid heuristic search-simulated annealing (HS-SA) algorithm is used to reduce the data storage requirements and improve the efficiency of the established models based on sensor data. First, a fuel CG model is established based on the multidimensional interpolation of flight sensors and fuel tank data, which can accurately reflect the nonlinear characteristics of the problem and reduce the data storage needs. Then, the calculation nodes are reasonably selected and optimized based on the proposed HS-SA algorithm to improve the precision of the model of the aircraft fuel CG. The established model of the fuel CG has obvious advantages over traditional methods in improving the temporal efficiency and meeting the storage requirements for sensor data in actual flights. Finally, detailed simulations are conducted based on more than 16,000 sets of sensor data, and the results demonstrate the effectiveness of the proposed HS-SA algorithm.

scholarly journals Feasibility study of using the RoboEarth cloud engine for rapid mapping and tracking with small unmanned aerial systems

2014 ◽  
Vol XL-1 ◽  
pp. 219-226 ◽  
Author(s):  
J. Li-Chee-Ming ◽  
C. Armenakis
Keyword(s):  
Data Storage ◽  
Large Data ◽  
Video Camera ◽  
Sensor Data ◽  
Single Frequency ◽  
Unmanned Aerial Systems ◽  
Indoor Environments ◽  
Processing Strategies ◽  
Outdoor Environments ◽  
Aerial Systems

This paper presents the ongoing development of a small unmanned aerial mapping system (sUAMS) that in the future will track its trajectory and perform 3D mapping in near-real time. As both mapping and tracking algorithms require powerful computational capabilities and large data storage facilities, we propose to use the RoboEarth Cloud Engine (RCE) to offload heavy computation and store data to secure computing environments in the cloud. While the RCE's capabilities have been demonstrated with terrestrial robots in indoor environments, this paper explores the feasibility of using the RCE in mapping and tracking applications in outdoor environments by small UAMS. <br><br> The experiments presented in this work assess the data processing strategies and evaluate the attainable tracking and mapping accuracies using the data obtained by the sUAMS. Testing was performed with an Aeryon Scout quadcopter. It flew over York University, up to approximately 40 metres above the ground. The quadcopter was equipped with a single-frequency GPS receiver providing positioning to about 3 meter accuracies, an AHRS (Attitude and Heading Reference System) estimating the attitude to about 3 degrees, and an FPV (First Person Viewing) camera. Video images captured from the onboard camera were processed using VisualSFM and SURE, which are being reformed as an Application-as-a-Service via the RCE. The 3D virtual building model of York University was used as a known environment to georeference the point cloud generated from the sUAMS' sensor data. The estimated position and orientation parameters of the video camera show increases in accuracy when compared to the sUAMS' autopilot solution, derived from the onboard GPS and AHRS. The paper presents the proposed approach and the results, along with their accuracies.

Design of FPGA-Based Missile-Borne Image Acquisition and Storage Recorder

2015 ◽  
Vol 713-715 ◽  
pp. 1448-1451
Author(s):  
Lin Lu ◽  
Yan Feng Zhang ◽  
Xiao Feng Li
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Storage System ◽  
Large Data ◽  
Engineering Practice ◽  
Image Storage ◽  
Video Images ◽  
Stable Performance ◽  
And Storage ◽  
Time Image

The high-altitude missile and other special application occasions have requirements on image storage system, such as small size, high storage speed, low temperature resistance, etc. Commonly used image storage system in the market cannot meet such requirement. In the paper, real-time image storage system solutions on missile based on FPGA should be proposed. The system mainly consists of acquisition module and memory reading module. The whole system adopts FPGA as main control chip for mainly completing real-time decoding and acquisition on one path of PAL format video images, reading and writing of NandFlash chipset, erasure, bad block management and so on. The solution has passed various environmental tests with stable performance, large data storage capacity and easy expansion, which has been used in engineering practice.

scholarly journals DESIGN OF GEODETIC NETWORKS BASED ON OUTLIER IDENTIFICATIONCRITERIA: AN EXAMPLE APPLIED TO THE LEVELING NETWORK

2018 ◽  
Vol 24 (2) ◽  
pp. 152-170 ◽  
Author(s):  
Vinicius Francisco Rofatto ◽  
Marcelo Tomio Matsuoka ◽  
Ivandro Klein
Keyword(s):  
Numerical Simulation ◽  
Data Storage ◽  
Quality Criterion ◽  
Large Data ◽  
Simulation Method ◽  
Testing Procedure ◽  
Data Snooping ◽  
Internal Reliability ◽  
Geodetic Networks ◽  
Power Of The Test

Abstract: We present a numerical simulation method for designing geodetic networks. The quality criterion considered is based on the power of the test of data snooping testing procedure. This criterion expresses the probability of the data snooping to identify correctly an outlier. In general, the power of the test is defined theoretically. However, with the advent of the fast computers and large data storage systems, it can be estimated using numerical simulation. Here, the number of experiments in which the data snooping procedure identifies the outlier correctly is counted using Monte Carlos simulations. If the network configuration does not meet the reliability criterion at some part, then it can be improved by adding required observation to the surveying plan. The method does not use real observations. Thus, it depends on the geometrical configuration of the network; the uncertainty of the observations; and the size of outlier. The proposed method is demonstrated by practical application of one simulated leveling network. Results showed the needs of five additional observations between adjacent stations. The addition of these new observations improved the internal reliability of approximately 18%. Therefore, the final designed network must be able to identify and resist against the undetectable outliers - according to the probability levels.

scholarly journals A review: evolution of big data in developing country

2019 ◽  
Vol 3 (1) ◽  
pp. 30-37
Author(s):  
Bayu Prasetyo ◽  
Faiz Syaikhoni Aziz ◽  
Kamil Faqih ◽  
Wahyu Primadi ◽  
Roni Herdianto ◽  
...  
Keyword(s):  
Developing Countries ◽  
Big Data ◽  
Developing Country ◽  
Data Storage ◽  
Human Life ◽  
Large Data ◽  
Big Data Technology

The development of technology from year to year is increasingly rapid and diverse. All systems that exist in human life began to be designed with technology that requires large data storage. Big Data technology began to be developed to accommodate very large data volumes, rapid data changes, and very varied. Developing countries are starting to use Big Data a lot in developing their systems, such as healthcare, agriculture, building, transportation, and various other fields. In this paper, it explains the development of Big Data applied to the sectors previously mentioned in developing countries and also the challenges faced by developing countries in the process of developing their systems.

scholarly journals Bagbunker - Tool for Large Data Storage, Analysis, Viewing and Testing

2016 ◽  
Author(s):  
Dejan Pangercic ◽  
Daniel Marco ◽  
Florian Friesdorf ◽  
Marko Durkovic
Keyword(s):  
Data Storage ◽  
Large Data

Building Australia’s Scalable Drone Cloud

2021 ◽  
Author(s):  
Jens Klump ◽  
Tim Brown ◽  
Rohan Clarke ◽  
Robert Glasgow ◽  
Steve Micklethwaite ◽  
...  
Keyword(s):  
Data Management ◽  
Best Practice ◽  
Management Practices ◽  
Agricultural Research ◽  
Terrestrial Ecosystem ◽  
Large Data ◽  
Sensor Data ◽  
Research Network ◽  
Ecosystem Research ◽  
Plant Phenomics

&lt;p&gt;Remotely Piloted Aircraft (RPA), commonly known as drones, provide sensing capabilities that address the critical scale-gap between ground- and satellite-based observations. Their versatility allows researchers to deliver near-real-time information for society.&lt;/p&gt;&lt;p&gt;Key to delivering RPA information is the capacity to enable researchers to systematically collect, process, manage and share RPA-borne sensor data. Importantly, this should allow vertical integration across scales and horizontal integration across different RPA deployments. However, as an emerging technology, the best practice and standards are still developing and the large data volumes collected during RPA missions can be challenging.&lt;/p&gt;&lt;p&gt;Australia&amp;#8217;s Scalable Drone Cloud (ASDC) aims to coordinate and standardise how scientists from across earth, environmental and agricultural research manage, process and analyse data collected by RPA-borne sensors, by establishing best practices in managing 3D-geospatial data and aligned with the FAIR data principles.&lt;/p&gt;&lt;p&gt;The ASDC is building a cloud-native platform for research drone data management and analytics, driven by exemplar data management practices, data-processing pipelines, and search and discovery of drone data. The aim of the platform is to integrate sensing capabilities with easy-to-use storage, processing, visualisation and data analysis tools (including computer vision / deep learning techniques) to establish a national ecosystem for drone data management.&lt;/p&gt;&lt;p&gt;The ASDC is a partnership of the Monash Drone Discovery Platform, CSIRO and key National Collaborative Research Infrastructure (NCRIS) capabilities including the Australian Research Data Commons (ARDC), Australian Plant Phenomics Facility (APPF), Terrestrial Ecosystem Research Network (TERN), and AuScope.&lt;/p&gt;&lt;p&gt;This presentation outlines the roadmap and first proof-of-concept implementation of the ASDC.&lt;/p&gt;

Storage Systems for Data Warehousing

2011 ◽  
pp. 1859-1864 ◽  
Author(s):  
Alexander Thomasian
Keyword(s):  
Data Storage ◽  
Concurrency Control ◽  
Storage Systems ◽  
Large Data ◽  
Cost Effective ◽  
Background Information ◽  
Disk Storage ◽  
Large Databases ◽  
Database Size ◽  
Time Required

Data storage requirements have consistently increased over time. According to the latest WinterCorp survey (http://www/WinterCorp.com), “The size of the world’s largest databases has tripled every two years since 2001.” With database size in excess of 1 terabyte, there is a clear need for storage systems that are both cost effective and highly reliable. Historically, large databases are implemented on mainframe systems. These systems are large and expensive to purchase and maintain. In recent years, large data warehouse applications are being deployed on Linux and Windows hosts, as replacements for the existing mainframe systems. These systems are significantly less expensive to purchase while requiring less resources to run and maintain. With large databases it is less feasible, and less cost effective, to use tapes for backup and restore. The time required to copy terabytes of data from a database to a serial medium (streaming tape) is measured in hours, which would significantly degrade performance and decreases availability. Alternatives to serial backup include local replication, mirroring, or geoplexing of data. The increasing demands of larger databases must be met by less expensive disk storage systems, which are yet highly reliable and less susceptible to data loss. This article is organized into five sections. The first section provides background information that serves to introduce the concepts of disk arrays. The following three sections detail the concepts used to build complex storage systems. The focus of these sections is to detail: (i) Redundant Arrays of Independent Disks (RAID) arrays; (ii) multilevel RAID (MRAID); (iii) concurrency control and storage transactions. The conclusion contains a brief survey of modular storage prototypes.

Data Storage, Retrieval and Management

2010 ◽  
pp. 111-140
Author(s):  
Valentin Cristea ◽  
Ciprian Dobre ◽  
Corina Stratan ◽  
Florin Pop
Keyword(s):  
Data Storage ◽  
Resource Sharing ◽  
High Performance ◽  
Large Scale ◽  
Workflow Management ◽  
Large Data ◽  
Data Retrieval ◽  
Distributed Data Storage ◽  
Processing Power ◽  
Data Transfers

The latest advances in network and distributedsystem technologies now allow integration of a vast variety of services with almost unlimited processing power, using large amounts of data. Sharing of resources is often viewed as the key goal for distributed systems, and in this context the sharing of stored data appears as the most important aspect of distributed resource sharing. Scientific applications are the first to take advantage of such environments as the requirements of current and future high performance computing experiments are pressing, in terms of even higher volumes of issued data to be stored and managed. While these new environments reveal huge opportunities for large-scale distributed data storage and management, they also raise important technical challenges, which need to be addressed. The ability to support persistent storage of data on behalf of users, the consistent distribution of up-to-date data, the reliable replication of fast changing datasets or the efficient management of large data transfers are just some of these new challenges. In this chapter we discuss how the existing distributed computing infrastructure is adequate for supporting the required data storage and management functionalities. We highlight the issues raised from storing data over large distributed environments and discuss the recent research efforts dealing with challenges of data retrieval, replication and fast data transfers. Interaction of data management with other data sensitive, emerging technologies as the workflow management is also addressed.

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