Artículos de revistas: "Air Data System"

1

Lie, F. Adhika Pradipta y Demoz Gebre-Egziabher. "Synthetic Air Data System". Journal of Aircraft 50, n.º 4 (julio de 2013): 1234–49. http://dx.doi.org/10.2514/1.c032177.

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2

Shyam Mohan, N., M. Jayakumar, T. Sivamurugan, K. C. Finitha, S. B. Vidya, Jayanta Dhoaya, N. Remesh, M. Prasath, Shashi Krishna y Aisha Sidhique. "Flush Air Data Sensing System". Current Science 114, n.º 01 (10 de enero de 2018): 68. http://dx.doi.org/10.18520/cs/v114/i01/68-73.

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3

ZHOU, WeiJiang, GuoHui DOU, XiuXin DOU, WuYue LIU y GuangQiang CHEN. "Flush air data sensing system design for air breathing air-to-air missile". SCIENTIA SINICA Technologica 46, n.º 11 (28 de octubre de 2016): 1193–206. http://dx.doi.org/10.1360/n092016-00258.

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4

Benbassat, Danny y Levent Ileri. "Next Generation Air Transportation System: Data Communications". Proceedings of the Human Factors and Ergonomics Society Annual Meeting 54, n.º 1 (septiembre de 2010): 114–15. http://dx.doi.org/10.1177/154193121005400125.

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5

Freeman, Paul, Peter Seiler y Gary J. Balas. "Air data system fault modeling and detection". Control Engineering Practice 21, n.º 10 (octubre de 2013): 1290–301. http://dx.doi.org/10.1016/j.conengprac.2013.05.007.

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6

Schmidt, Walter, Ari-Matti Harri, Timo Nousiainen, Harri Hohti, Lasse Johansson, Olli Ojanperä, Erkki Viitala et al. "CITYZER observation network and data delivery system". Geoscientific Instrumentation, Methods and Data Systems 9, n.º 2 (13 de octubre de 2020): 397–406. http://dx.doi.org/10.5194/gi-9-397-2020.

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Abstract. CITYZER develops new digital services and products to support decision-making processes related to weather and air quality in cities. This includes, for example, early warnings and forecasts (0–24 h), which allow for avoiding weather-related accidents, mitigate human distress and costs from weather-related damage and bad air quality, and generally improve the resilience and safety of the society. The project takes advantage of the latest scientific know-how and directly exploits the expertise obtained from earlier projects. Central to the project is the Observation Network Manager NM10 developed by Vaisala, on which CITYZER defines and builds new commercial services and connects new sensor networks, for example, for air quality measurements, as well as the ENFUSER local-scale air quality modelling system developed by the Finnish Meteorological Institute, for real-time air quality forecasts and nowcasts.

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7

Liu, A. Gu Da Mu, Peng Yang, Wen Sheng Lv y Jie Liu. "Air Quality Data Management System Based on VB.NET". Advanced Materials Research 955-959 (junio de 2014): 1147–50. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.1147.

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In order to achieve the automatic management of air quality monitoring data, the air quality data management system has been developed using VB.NET platform and Oracle as background database. It is also combined with the situation of the air quality in Beijing and based on the air quality monitoring data. The paper analyzes the system from 6 aspects, including technology selection, system architecture, system data flow diagram, system development environment, system functions and system features. Finally this paper explains the significance of the system development and application prospects.

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8

Czechowski, Piotr, Artur Badyda y Grzegorz Majewski. "Data Mining System for Air Quality Monitoring Networks". Archives of Environmental Protection 39, n.º 4 (1 de diciembre de 2013): 123–47. http://dx.doi.org/10.2478/aep-2013-0041.

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Abstract The use of quantitative methods, including stochastic and exploratory techniques in environmental studies does not seem to be sufficient in practical aspects. There is no comprehensive analytical system dedicated to this issue, as well as research regarding this subject. The aim of this study is to present the Eco Data Miner system, its idea, construction and implementation possibility to the existing environmental information systems. The methodological emphasis was placed on the one-dimensional data quality assessment issue in terms of using the proposed QAAH1 method - using harmonic model and robust estimators beside the classical tests of outlier values with their iterative expansions. The results received demonstrate both the complementarity of proposed classical methods solution as well as the fact that they allow for extending the range of applications significantly. The practical usefulness is also highly significant due to the high effectiveness and numerical efficiency as well as simplicity of using this new tool.

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9

Jing, You Yin, Qian Qian Liu y Jiang Jiang Wang. "System Optimization and Exergy Analysis of Air Conditioning System for Data Center". Applied Mechanics and Materials 291-294 (febrero de 2013): 1857–62. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.1857.

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As the air conditioning energy consumption of the date center has increasingly become the focus of attention, reformation for the energy consumption and exploration for the suitable combination model of the air conditioning system have significance in energy saving. This paper makes a program design for the air-conditioning system of a data center in Beijing, makes an analysis of the system operation mode and uses the concept of exergy efficiency to optimize the air conditioning system in order to achieve maximum efficiency.

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10

Johnston, Ian A., Peter A. Jacobs y Takayuki Shimoda. "Flush Air Data System Calibration Using Numerical Simulation". Journal of Spacecraft and Rockets 35, n.º 6 (noviembre de 1998): 812–20. http://dx.doi.org/10.2514/2.3404.

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11

Zhang, Qing Feng. "Optimization Design of the Flush Air Data System". Applied Mechanics and Materials 533 (febrero de 2014): 312–15. http://dx.doi.org/10.4028/www.scientific.net/amm.533.312.

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A new configuration of multi sensor flush air data system was proposed and the detecting principles of the flush air data system was discussed. A large number of redundant information pick up from multi sensors embedded in aircraft data system put forward the requirements of information optimization. Characteristic value of air data defined in this chapter meet the active control system of aircraft redundancy requirements of total pressure, static pressure, angle of attack and sideslip angle of atmospheric data, was the foundation of the multi sensor information fusion system.

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12

Jurado, Juan D. y Clark C. McGehee. "Complete Online Algorithm for Air Data System Calibration". Journal of Aircraft 56, n.º 2 (marzo de 2019): 517–28. http://dx.doi.org/10.2514/1.c034964.

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13

Efremova, E. S. "A simulator of the aircraft air data system". Russian Aeronautics (Iz VUZ) 59, n.º 2 (abril de 2016): 243–48. http://dx.doi.org/10.3103/s1068799816020148.

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14

Chen, Yong-Liang, Zi-Qiang Qin, Yao Li, Hai-Bo Wang, Sheryar Muhammad y Hai-Qing Si. "Optimization of modular data center air conditioning system". International Journal of Modern Physics B 34, n.º 14n16 (3 de junio de 2020): 2040123. http://dx.doi.org/10.1142/s0217979220401232.

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In high-density data center, energy consumption is increasing dramatically. For reducing the energy consumption, CFD software, Fluent 15.0, is used to simulate the flow and temperature field distribution with [Formula: see text] turbulence model and fluid–solid coupling method. Fans on the back of racks are simplified as walls with a certain pressure jump. Severs are treated as solid heat sources and porous media. Simulation results reveal that the temperature distribution on the back of racks is not uniform when air conditioners are arranged face-to-face, and local high temperature points emerge near the side wall of air conditioners. Factors affecting cooling efficiency, such as location of air conditioners, speed of inlets, distance of racks, etc., need to be improved. Geometric model is optimized by using a diagonal rack arrangement and drilling holes on the side wall. Based on this, four different cases with various hot aisle distance are proposed. Single and double modular data center are both simulated. Results of new model are better than those of baseline model.

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15

Krishna, H. S. y K. P. Singh. "Computation Cuwes for Air Data System (Short Communication)". Defence Science Journal 52, n.º 1 (1 de enero de 2002): 65–71. http://dx.doi.org/10.14429/dsj.52.2150.

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16

McClary, C. R. "A fault-tolerant air data/inertial reference system". IEEE Aerospace and Electronic Systems Magazine 7, n.º 5 (mayo de 1992): 19–23. http://dx.doi.org/10.1109/62.257088.

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17

Ramírez, Hermes, Javier Jiménez-Cabas y Antonio Bula. "Experimental data for an air-conditioning system identification". Data in Brief 25 (agosto de 2019): 104316. http://dx.doi.org/10.1016/j.dib.2019.104316.

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18

Mocker, Hans W. y T. J. Wagener. "Laser Doppler optical air-data system: feasibility demonstration and systems specifications". Applied Optics 33, n.º 27 (20 de septiembre de 1994): 6457. http://dx.doi.org/10.1364/ao.33.006457.

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19

Zhang, Feng Ying y Yun Hua Gao. "Study on Green Gas Data Detection System". Advanced Materials Research 722 (julio de 2013): 322–27. http://dx.doi.org/10.4028/www.scientific.net/amr.722.322.

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This paper describes a wireless gas data detection system, based on CC1010 MCU as the core controller design measurement circuit of indoor air quality. IF in the air pollutants such as smokes and doors, TGS gas sensor sensitive to perceived pollutants transferred to the receiving device through the wireless function after CC1010 MCU treatment analysis. The system has the function of wireless data transmission, small size, high sensitivity, good stability, low cost, long life and other advantages of the peripheral circuits required little devices can be widely used in enclosed spaces such as indoor, automobile air pollution detection.

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20

Lu, Chen, Rong-Bing Li, Jian-Ye Liu y Ting-Wan Lei. "Air Data Estimation by Fusing Navigation System and Flight Control System". Journal of Navigation 71, n.º 5 (30 de abril de 2018): 1231–46. http://dx.doi.org/10.1017/s037346331800022x.

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A novel synthetic air data estimation method without using air data sensors is presented, and the method only relies on the information from the Navigation System (NS) and Flight Control System (FCS). The aircraft's aerodynamic model is also required to make a connection between the FCS control parameters and the NS measurements. The airspeed, angle of attack and sideslip, angular velocity and wind speed are defined as state vectors, and state equations are established through the aircraft's aerodynamic model and dynamics. Linear velocity and angular velocity provided by the navigation system are considered as the measurement vector. To deal with variable wind fields, a novel Initialised Three-step Extended Kalman Filter (ITEKF), which considers the wind speed as an unknown input, is developed to track the variation of wind speed. Simulation results based on a Generic Hypersonic Vehicle (GHV) model are presented and compared with an existing method. Factors affecting the method's accuracy include the navigation system accuracy and the aerodynamic model error, are also discussed.

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21

Kharchenko, Volodymur, Ivan Ostroumov, Nataliia Kuzmenko y Arkadiy Larionov. "Airplane positioning using airborne collision avoidance system data". E3S Web of Conferences 164 (2020): 03050. http://dx.doi.org/10.1051/e3sconf/202016403050.

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Mid-air collision is an important problem of modern air transport system. Development of Airborne Collision Avoidance System (ACAS) is grounded on safety criteria in order to support air traffic capacity and equipment. In research, we study the possibility and performance of airplane positioning by data accumulated in ACAS. ACAS surveillance equipment supports accurate range and poor angular measurements of airspace user’s data. An approach is based on a fusion of surveillance data with airplanes locations obtained from decoded Automatic Depended Surveillance-Broadcast messages. Performance of positioning approach is considered in more detail. An error of positioning in the horizontal plane is estimated in relation with uncertainty of airspace users’ location. Numerical demonstration with a live air traffic data indicates poor positioning accuracy in comparison with primary positioning system on-board of airplane and accuracy dependence from geometry and capacity of air traffic.

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22

Li, Min, Yun Wang, Zheng Qian Feng y Wang Li. "Research on Intelligent Air Conditioning System of Data Center". Applied Mechanics and Materials 602-605 (agosto de 2014): 928–32. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.928.

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By studying the energy-saving technologies of air-conditioning system in data centers, we designed a intelligent air conditioning system, improved the cooling efficiency of air conditioning system through a reasonable set of hot and cold aisles, reduced the running time of HVAC by using the intelligent heat exchange system, an provided a reference for energy saving research of air conditioning system of data centers.

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23

Nanseki, Teruaki, Kaoru Maeyama y Shigehiro Honda. "FAPS-DB: Farm Planning Support System Integrated with Agro-technology Systems Data Base". Agricultural Information Research 16, n.º 2 (2007): 66–80. http://dx.doi.org/10.3173/air.16.66.

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24

Yamada, Kazushige. "Situation of Meteorological Data and Its Intelligence System". Agricultural Information Research 2, n.º 1 (1993): 1–15. http://dx.doi.org/10.3173/air.2.1.

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25

Ji, Lei, Gabriel B. Senay y James P. Verdin. "Evaluation of the Global Land Data Assimilation System (GLDAS) Air Temperature Data Products". Journal of Hydrometeorology 16, n.º 6 (17 de noviembre de 2015): 2463–80. http://dx.doi.org/10.1175/jhm-d-14-0230.1.

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Abstract There is a high demand for agrohydrologic models to use gridded near-surface air temperature data as the model input for estimating regional and global water budgets and cycles. The Global Land Data Assimilation System (GLDAS) developed by combining simulation models with observations provides a long-term gridded meteorological dataset at the global scale. However, the GLDAS air temperature products have not been comprehensively evaluated, although the accuracy of the products was assessed in limited areas. In this study, the daily 0.25° resolution GLDAS air temperature data are compared with two reference datasets: 1) 1-km-resolution gridded Daymet data (2002 and 2010) for the conterminous United States and 2) global meteorological observations (2000–11) archived from the Global Historical Climatology Network (GHCN). The comparison of the GLDAS datasets with the GHCN datasets, including 13 511 weather stations, indicates a fairly high accuracy of the GLDAS data for daily temperature. The quality of the GLDAS air temperature data, however, is not always consistent in different regions of the world; for example, some areas in Africa and South America show relatively low accuracy. Spatial and temporal analyses reveal a high agreement between GLDAS and Daymet daily air temperature datasets, although spatial details in high mountainous areas are not sufficiently estimated by the GLDAS data. The evaluation of the GLDAS data demonstrates that the air temperature estimates are generally accurate, but caution should be taken when the data are used in mountainous areas or places with sparse weather stations.

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26

Gao, Yang, Jinxiang Liu, Xiaolei Yuan, Kai Zhang, Yujia Yang y Yu Wang. "Air-conditioning system with underfloor air distribution integrated solar chimney in data center". Procedia Engineering 205 (2017): 3420–27. http://dx.doi.org/10.1016/j.proeng.2017.09.852.

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27

Rajasekhara Naidu, G. y N. Venkat ram. "Urban Climate Monitoring System with IoT Data Analytics". International Journal of Engineering & Technology 7, n.º 2.20 (18 de abril de 2018): 5. http://dx.doi.org/10.14419/ijet.v7i2.20.11732.

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Climate monitoring was one of the important thing that should be monitored thoroughly and it plays an important role in every person’s life and his surroundings. Due to the increase of factories, vehicles there are so many drastically changes in the climate like increasing of temperature, humidity and the quality of air. In this paper we are designing a climate monitoring system that which detects temperature, humidity, and monitoring the air-quality. Coming to air quality we are mainly concentrating on detecting carbon-monoxide and some pollutants. The information can be checked with gadgets like PC, versatile, tablet through Thingspeak information examination.

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28

Shirani, Arsalan, Alexander Merzkirch, Jennifer Roesler, Stephan Leyer, Frank Scholzen y Stefan Maas. "Field monitoring data on a residential exhaust air heat pump system (air-to-air heat pump)". Data in Brief 38 (octubre de 2021): 107386. http://dx.doi.org/10.1016/j.dib.2021.107386.

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29

Cunningham, J. A., W. C. Rochelle, I. Norman, P. C. Ting y J. J. Gallegos. "Shuttle Entry Air Data System preflight testing and analysis". Journal of Spacecraft and Rockets 24, n.º 1 (enero de 1987): 33–39. http://dx.doi.org/10.2514/3.25869.

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30

Mutlu, Taner y Chingiz Hajiyev. "An Integrated Air Data / GPS Navigation System for Helicopters". Positioning 02, n.º 02 (2011): 103–11. http://dx.doi.org/10.4236/pos.2011.22010.

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31

Nebula, Francesco, Roberto Palumbo, Gianfranco Morani y Federico Corraro. "Virtual Air Data System Architecture for Space Reentry Applications". Journal of Spacecraft and Rockets 46, n.º 4 (julio de 2009): 818–28. http://dx.doi.org/10.2514/1.42485.

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32

Smart, Anthony E. "Velocity sensor for an airborne optical air data system". Journal of Aircraft 28, n.º 3 (marzo de 1991): 163–64. http://dx.doi.org/10.2514/3.46007.

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33

Schleutker, Thorn, Ali Gülhan, Bart Van Hove y Özgür Karatekin. "ExoMars Flush Air Data System: Experimental and Numerical Investigation". Journal of Spacecraft and Rockets 56, n.º 4 (julio de 2019): 971–82. http://dx.doi.org/10.2514/1.a34185.

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34

Ishii, Yoshikazu. "Performance Monitoring of Air Conditioning System using Inconsistent Data". IEEJ Transactions on Electronics, Information and Systems 136, n.º 9 (2016): 1411–17. http://dx.doi.org/10.1541/ieejeiss.136.1411.

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35

Miura, Akira, Hiroyuki Morikawa y Moriyuki Mizumachi. "Air traffic control data tables for conflict alert system". Electronics and Communications in Japan (Part I: Communications) 79, n.º 6 (1996): 101–13. http://dx.doi.org/10.1002/ecja.4410790611.

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36

Fravolini, Mario L., Giuseppe Del Core, Umberto Papa, Paolo Valigi y Marcello R. Napolitano. "Data-Driven Schemes for Robust Fault Detection of Air Data System Sensors". IEEE Transactions on Control Systems Technology 27, n.º 1 (enero de 2019): 234–48. http://dx.doi.org/10.1109/tcst.2017.2758345.

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37

Maurya, Ekta Saheblal. "Liquid Cooling System and Air Cooling System in Data Center: A Comparison". International Journal for Research in Applied Science and Engineering Technology 8, n.º 11 (30 de noviembre de 2020): 548–50. http://dx.doi.org/10.22214/ijraset.2020.32226.

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38

Muchmore, Douglas B., Bernard Silverman, Amparo De La Peña y Janet Tobian. "The AIR® Inhaled Insulin System: System Components and Pharmacokinetic/Glucodynamic Data". Diabetes Technology & Therapeutics 9, s1 (junio de 2007): S—41—S—47. http://dx.doi.org/10.1089/dia.2007.0218.

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39

Iguchi, Nobukazu, Takuma Yokomae, Shouji Mizobuchi y Sonoyo Mukai. "Sensor Data Exchange System Based on Hybrid P2P Technology". Agricultural Information Research 20, n.º 2 (2011): 39–52. http://dx.doi.org/10.3173/air.20.39.

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40

Imabayashi, Souichirou y Tomohiko Yoshida. "A Simple Data Management System of Rice Breeding Records". Agricultural Information Research 6, n.º 1 (1997): 1–8. http://dx.doi.org/10.3173/air.6.1.

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41

Priyadumkol, Jetsadaporn y Chawalit Kittichaikarn. "A Study of Air Flow through Perforated Tile for Air Conditioning System in Data Center". Applied Mechanics and Materials 249-250 (diciembre de 2012): 126–31. http://dx.doi.org/10.4028/www.scientific.net/amm.249-250.126.

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The power trend of using server systems in data center is continuously increasing. Cooling system consumed 38% of total energy usage which is a significant contribution in the energy consumption. As a result, the efficient energy usage in data center is concerned.Normally a raised-floor is widely used in data center cooling system which delivers cool air through perforated tiles to a front of server racks. However it is usually found that this cool air cannot effectively remove a heat dissipation at the top of server racks. Therefore, the environmental condition in data center must be designed strictly to avoid disruption that caused by overheat.This paper gives some guidelines to help in the better design. Commercial Computational Fluid Dynamics (CFD) program was used to analyze the air flow from raised-floor air conditioning system. A tetrahedral of 1.8 million meshes with k- turbulence model were used to obtain the air flow velocity and temperature distributions in the room. The model was validated by comparing simulation results with actual measurements. As a result, dimensionless parameters in the form of supply heat index(SHI), for understanding the optimization of relative airflow distribution to the heat load of server rack was found. It shows that these parameters provide an effective tool to the improvement of energy efficiency in raised floor data center.

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42

Shio, Kohki y Takemi Machida. "Analysis of FAX Data and Problems on Personal Data Inputting System by Using a Facsimile". Agricultural Information Research 4, n.º 1 (1995): 1–17. http://dx.doi.org/10.3173/air.4.1.

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43

Samy, Ihab, Ian Postlethwaite, Da-Wei Gu y John Green. "Neural-Network-Based Flush Air Data Sensing System Demonstrated on a Mini Air Vehicle". Journal of Aircraft 47, n.º 1 (enero de 2010): 18–31. http://dx.doi.org/10.2514/1.44157.

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44

Won, Dae-Yeon, Seonghun Yun, Hongju Lee, Jin-Sung Hong, Sun-Yu Hwang, Heung-Sik Lim y Taekyeum Kim. "Design and Integration of a Dual Redundancy Air Data System for Unmanned Air Vehicles". Journal of the Korea Institute of Military Science and Technology 23, n.º 6 (5 de diciembre de 2020): 639–49. http://dx.doi.org/10.9766/kimst.2020.23.6.639.

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45

Zhan, Ye, Li Ming Chang, Jun Li y Ming Shu Jiao. "Study on Flush Air Data Sensing Technology". Advanced Materials Research 962-965 (junio de 2014): 2766–69. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.2766.

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Accurate measurement of atmospheric data is important for the control and navigation of aircraft. With the development of Aerospace Technology, the traditional probe air data sensing system has been could not meet the requirements of advanced flight. For this problem, the flush air data sensing system (FADS) has been proposed. The paper listed and compared some FADS algorithms and summarizes the characteristics and using conditions of several algorithm based on introducing the FADS measuring principle and compositions.

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46

Szafrański, Bolesław, Mirosław Zieja, Jarosław Wójcik y Krzysztof Murawski. "Data analysis of information system TURAWA in the aspect of data mining methods". Computer Science and Mathematical Modelling, n.º 7/2018 (18 de julio de 2018): 45–52. http://dx.doi.org/10.5604/01.3001.0012.2004.

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The article is devoted to the analysis of data coming from the operation process and collected in computer system TURAWA, which focuses on supporting the management of flight safety in the Polish Air Force. The Armed Forces are equipped with a system, which collects and processes data concerning the whole air crew, all performed flights and all aircraft. The increasing opportunities in obtaining data and the continuous development of data mining methods allow to extract information never been known before, which, together with conclusions obtained from the data analysis, will help to improve the level of flight safety.

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47

Park, Seonghyun y Janghoo Seo. "Economic Evaluation of Air-side Economizer System for Data Center". Korean Journal of Air-Conditioning and Refrigeration Engineering 26, n.º 4 (10 de abril de 2014): 145–50. http://dx.doi.org/10.6110/kjacr.2014.26.4.145.

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48

Xie, Chenlei, Qiansheng Fang, Xiaolong Wang y Yang Li. "Research on wireless data acquisition system of air-conditioning compressor". JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT 24, n.º 2 (9 de abril de 2010): 195–99. http://dx.doi.org/10.3724/sp.j.1187.2010.00195.

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49

Ellsworth, Joel C. y Stephen A. Whitmore. "Simulation of a Flush Air-Data System for Transatmospheric Vehicles". Journal of Spacecraft and Rockets 45, n.º 4 (julio de 2008): 716–32. http://dx.doi.org/10.2514/1.33541.

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50

Karlgaard, Christopher D. y Mark Schoenenberger. "Planetary Probe Entry Atmosphere Estimation Using Synthetic Air Data System". Journal of Spacecraft and Rockets 55, n.º 3 (mayo de 2018): 599–610. http://dx.doi.org/10.2514/1.a34115.

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Artículos de revistas sobre el tema "Air Data System"

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