scholarly journals A Sun Path Observation System Based on Augment Reality and Mobile Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Wernhuar Tarng ◽  
Kuo-Liang Ou ◽  
Yun-Chen Lu ◽  
Yi-Syuan Shih ◽  
Hsin-Hun Liou
Keyword(s):  
Situated Learning ◽  
Teaching Experiment ◽  
Observation System ◽  
Observation Data ◽  
The Sun ◽  
Astronomy Education ◽  
Teaching Aids ◽  
Augment Reality ◽  
Bad Weather ◽  
Observation Times

This study uses the augmented reality technology and sensor functions of GPS, electronic compass, and three-axis accelerometer on mobile devices to develop a Sun path observation system for applications in astronomy education. The orientation and elevation of the Sun can be calculated by the system according to the user’s location and local time to simulate the Sun path. When holding the mobile device toward the sky, the screen will show the virtual Sun at the same position as that of the real Sun. The user can record the Sun path and the data of observation date, time, longitude, and latitude using the celestial hemisphere and the pole shadow on the system. By setting different observation times and locations, it can be seen that the Sun path changes with seasons and latitudes. The system provides contextual awareness of the Sun path concepts, and it can convert the observation data into organized and meaningful astronomical knowledge to enable combination of situated learning with spatial cognition. The system can solve the problem of being not able to record the Sun path due to a bad weather or topographical restrictions, and therefore it is helpful for elementary students when conducting observations. A teaching experiment has been conducted to analyze the learning achievement of students after using the system, and the results show that it is more effective than traditional teaching aids. The questionnaire results also reveal that the system is easy to operate and useful in recording the Sun path data. Therefore, it is an effective tool for astronomy education in elementary schools.

scholarly journals Development of a Lunar-Phase Observation System Based on Augmented Reality and Mobile Learning Technologies

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Wernhuar Tarng ◽  
Yu-Sheng Lin ◽  
Chiu-Pin Lin ◽  
Kuo-Liang Ou
Keyword(s):  
Augmented Reality ◽  
Tall Buildings ◽  
Teaching Experiment ◽  
Learning Technologies ◽  
Observation System ◽  
Lunar Phase ◽  
The Moon ◽  
Lunar Phases ◽  
Phase Observation ◽  
Bad Weather

Observing the lunar phase requires long-term involvement, and it is often obstructed by bad weather or tall buildings. In this study, a lunar-phase observation system is developed using the augmented reality (AR) technology and the sensor functions of GPS, electronic compass, and 3-axis accelerometer on mobile devices to help students observe and record lunar phases easily. By holding the mobile device towards the moon in the sky, the screen will show the virtual moon at the position of the real moon. The system allows the user to record the lunar phase, including its azimuth/elevation angles and the observation date and time. In addition, the system can shorten the learning process by setting different dates and times for observation, so it can solve the problem of being unable to observe and record lunar phases due to a bad weather or the moon appearing late in the night. Therefore, it is an effective tool for astronomy education in elementary and high schools. A teaching experiment has been conducted to analyze the learning effectiveness of the system and the results show that it is effective in learning the lunar concepts. The questionnaire results reveal that students considered the system easy to operate and it is useful in locating the moon and recording the lunar data.

scholarly journals India

1998 ◽  
Vol 162 ◽  
pp. 32-34
Author(s):  
J. V. Narlikar ◽  
N.C. Rana
Keyword(s):  
Age Groups ◽  
The Sun ◽  
Space Technology ◽  
The Moon ◽  
School Teachers ◽  
Astronomy Education ◽  
Work Related ◽  
Recent Developments ◽  
Celestial Sphere ◽  
Modern Astronomy

A summary of work related to astronomy education carried out during the last three years in India is presented here. Since India is a huge country and many educational efforts are made by individuals alone, this report cannot be regarded as complete, but a specific sampling.India has more than 200 Universities, 8000 colleges, and about 100,000 schools, 33 planetaria, more than 100 museums and about 60 well known amateur astronomers’ clubs. Scores of dedicated astronomy oriented school teachers, act as nuclei of astronomy education for the general public and school children .The astronomical almanac, used in a typical household is in some way related to the stars in the sky and the movements of the Sun, the Moon and the planets. Traditionally, a rudimentary knowledge of the celestial sphere is common. The recent developments in space technology have brought a fascination and glamour to modern astronomy for all age groups, and this is noticeably reflected in the number of media coverages of astronomy.

The Development of Meteorological and Oceanographic Data Collection and Recording System Operating on Training Ship

2014 ◽  
Author(s):  
Michio Fujii ◽  
Mitsuru Hayashi ◽  
Misako Urakami ◽  
Nobukazu Wakabayashi
Keyword(s):  
Data Collection ◽  
Weather Forecasting ◽  
High Reliability ◽  
General Purpose ◽  
Observation System ◽  
Observation Data ◽  
Special Equipment ◽  
Merchant Ship ◽  
Marine Environmental ◽  
System Operating

The observation at sea for marine meteorology is achieved by weather reports from merchant ship’s crew every 3 or 6 hours, mainly. However, the number of available observation data is insufficient for weather forecasting and marine environmental simulation, compared with landside. Nowadays, the special data collection function is required if the automatic observation data collection system is installed on ship. But, it is difficult to install special equipment onto general merchant ship. Therefore, we develop a prototype marine observation system, which can be installed various types of ships easily without any special data collection function for improving data collection source and/or period of the observation at sea. In this paper, a) the configuration of high reliability and durability marine observation system by using general-purpose PC and general meteorological / oceanographic sensors, b) outlook of utilizing the data, which collected by this system, are described.

scholarly journals Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system

2014 ◽  
Vol 11 (13) ◽  
pp. 3547-3602 ◽  
Author(s):  
P. Ciais ◽  
A. J. Dolman ◽  
A. Bombelli ◽  
R. Duren ◽  
A. Peregon ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Cycle ◽  
Fossil Fuel ◽  
The Arctic ◽  
Observation System ◽  
Observation Data ◽  
Current State ◽  
Spatial Coverage

Abstract. A globally integrated carbon observation and analysis system is needed to improve the fundamental understanding of the global carbon cycle, to improve our ability to project future changes, and to verify the effectiveness of policies aiming to reduce greenhouse gas emissions and increase carbon sequestration. Building an integrated carbon observation system requires transformational advances from the existing sparse, exploratory framework towards a dense, robust, and sustained system in all components: anthropogenic emissions, the atmosphere, the ocean, and the terrestrial biosphere. The paper is addressed to scientists, policymakers, and funding agencies who need to have a global picture of the current state of the (diverse) carbon observations. We identify the current state of carbon observations, and the needs and notional requirements for a global integrated carbon observation system that can be built in the next decade. A key conclusion is the substantial expansion of the ground-based observation networks required to reach the high spatial resolution for CO2 and CH4 fluxes, and for carbon stocks for addressing policy-relevant objectives, and attributing flux changes to underlying processes in each region. In order to establish flux and stock diagnostics over areas such as the southern oceans, tropical forests, and the Arctic, in situ observations will have to be complemented with remote-sensing measurements. Remote sensing offers the advantage of dense spatial coverage and frequent revisit. A key challenge is to bring remote-sensing measurements to a level of long-term consistency and accuracy so that they can be efficiently combined in models to reduce uncertainties, in synergy with ground-based data. Bringing tight observational constraints on fossil fuel and land use change emissions will be the biggest challenge for deployment of a policy-relevant integrated carbon observation system. This will require in situ and remotely sensed data at much higher resolution and density than currently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO2 proxy measurements such as radiocarbon in CO2 and carbon-fuel combustion tracers. Additionally, a policy-relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. In addition, uncertainties for each observation data-stream should be assessed. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases interoperable, and on the calibration of each component of the system to agreed-upon international scales.

Implementing a new data acquisition system for the advanced integrated atmospheric observation system KITcube

2020 ◽  
Author(s):  
Martin Kohler ◽  
Mahnaz Fekri ◽  
Andreas Wieser ◽  
Jan Handwerker
Keyword(s):  
Time Series ◽  
Data Acquisition ◽  
Real Time ◽  
Binary Data ◽  
Data Exchange ◽  
Data Access ◽  
Series Data ◽  
Observation System ◽  
Observation Data ◽  
File Server

<p>KITcube (Kalthoff et al, 2013) is a mobile advanced integrated observation system for the measurement of meteorological processes within a volume of 10x10x10 km<sup>3</sup>. A large variety of different instruments from in-situ sensors to scanning remote sensing devices are deployed during campaigns. The simultaneous operation and real time instrument control needed for maximum instrument synergy requires a real-time data management designed to cover the various user needs: Save data acquisition, fast loading, compressed storage, easy data access, monitoring and data exchange. Large volumes of data such as raw and semi-processed data of various data types, from simple ASCII time series to high frequency multi-dimensional binary data provide abundant information, but makes the integration and efficient management of such data volumes to a challenge.<br>Our data processing architecture is based on open source technologies and involves the following five sections: 1) Transferring: Data and metadata collected during a campaign are stored on a file server. 2) Populating the database: A relational database is used for time series data and a hybrid database model for very large, complex, unstructured data. 3) Quality control: Automated checks for data acceptance and data consistency. 4) Monitoring: Data visualization in a web-application. 5) Data exchange: Allows the exchange of observation data and metadata in specified data formats with external users.<br>The implemented data architecture and workflow is illustrated in this presentation using data from the MOSES project (http://moses.eskp.de/home).</p><p>References:</p><p><strong>KITcube - A mobile observation platform for convection studies deployed during HyMeX </strong>.<br>Kalthoff, N.; Adler, B.; Wieser, A.; Kohler, M.; Träumner, K.; Handwerker, J.; Corsmeier, U.; Khodayar, S.; Lambert, D.; Kopmann, A.; Kunka, N.; Dick, G.; Ramatschi, M.; Wickert, J.; Kottmeier, C.<br>2013. Meteorologische Zeitschrift, 22 (6), 633–647. doi:10.1127/0941-2948/2013/0542 </p>

Arctic Datasets as Part af PEEX International Collaboration

2020 ◽  
Author(s):  
Nuria Altimir ◽  
Alexander Mahura ◽  
Tuukka Petäjä ◽  
Hanna K Lappalainen ◽  
Alla Borisova ◽  
...  
Keyword(s):  
Arctic Region ◽  
End Users ◽  
Atmospheric Mercury ◽  
Collaboration Network ◽  
Observation System ◽  
Western Coast ◽  
Observation Data ◽  
Russian Arctic ◽  
Local Conditions

<p><strong>Keywords:</strong></p><p>Arctic datasets, research infrastructures, in-situ observations, PEEX e-Catalogue, INTAROS, iCUPE</p><p> </p><p> </p><p>The INAR is leading the Pan-Eurasian EXperiment (PEEX; www.atm.helsinki.fi/peex) initiative. The PEEX Research Infrastructure’s has 3 components: observation, data and modelling. Observations networks produce large volumes of raw data to be pre/processed/analysed and delivered in a form of datasets (or products) to research and stakeholders/end-users communities. Here, steps taken are discussed and include an overview (as PEEX-e-Catalogue) of measurement capacity of exiting stations and linkages to INTAROS (intaros.nersc.no) and iCUPE (www.atm.helsinki.fi/icupe).</p><p> </p><p><strong>In-Situ Atmospheric-Ecosystem Collaborating Stations</strong></p><p>Although more than 200 stations are presented in the PEEX regions of interest, but so far only about 60+ Russian stations have metadata information available. The station metadata enables to categorize stations in a systematic manner and to connect them to international observation networks, such as WMO-GAWP, CERN and perform standardization of data formats. As part of the INAR activities with Russian partners, an e-catalogue was published as a living document (to be updated as new stations will joinin the PEEX network). This catalogue (www.atm.helsinki.fi/peex/index.php/peex-russia-in-situ-stations-e-catalogue) introduces information on measurements and contacts of the Russian stations in the collaboration network, and promotes research collaboration and stations as partners of the collaboration network and to give wider visibility to the stations activities.</p><p> </p><p><strong>Integrated Arctic Observation System (INTAROS)</strong></p><p>For Arctic region, 11 stations were selected for the Atmospheric, Terrestrial and Cryospheric parts/themes. The updated metadata were obtained for these measurement stations located within the Russian Arctic territories. Metadata include basic information, physico-geographical and infrastructure description of the sites and details on atmosphere and ecosystem (soils–forest–lakes–urban–peatland–tundra) measurements. Measurements at these sites represent more local conditions of immediate surrounding environment and datasets (as time-series) are available under request. For SMEAR-I (Station for Measuring Atmosphere-Ecosystem Relations) station included in the INTAROS web-based catalogue (catalog-intaros.nersc.no/dataset), the measurement programme includes meteorological (wind speed and direction, air temperature and relative humidity), radiation (global, reflected, net), chemistry/aerosols (CO<sub>2</sub>, SO<sub>2</sub>, O<sub>3</sub>, NO<sub>x</sub>, etc.); ecosystem, photosynthesis, irradiance related measurements.</p><p> </p><p><strong>Integrative and Comprehensive Understanding on Polar Environments (iCUPE)</strong></p><p>More than 20 open access datasets as products for researchers, decision- and policy makers, stakeholders and end-users communities are produced. A list of expected datasets is presented at www.atm.helsinki.fi/icupe/index.php/datasets/list-of-datasets-as-deliverables. These datasets are promoted to larger science and public communities through so-called “teasers” (www.atm.helsinki.fi/icupe/index.php/submitted-datasets). For the Russian Arctic regions, these also include those from the iCUPE Russian collaborators: atmospheric mercury measurements at Amderma station; elemental and organic carbon over the north-western coast of the Kandalaksha Bay of the White Sea; micro-climatic features and Urban Heat Island intensity in cities of Arctic region; and others. Delivered datasets (www.atm.helsinki.fi/icupe/index.php/datasets/delivered-datasets) are directly linked (and downloadable) at website, and corresponding Read-Me files are available with detailed description and metadata information included. Selected datasets are also to be tested for pre/post-processing/analysis on several cloud-based online platforms.</p>

scholarly journals Classification of Video Observation Data for Volcanic Activity Monitoring Using Computer Vision and Modern Neural NetWorks (on Klyuchevskoy Volcano Example)

2021 ◽  
Vol 13 (23) ◽  
pp. 4747
Author(s):  
Sergey Korolev ◽  
Aleksei Sorokin ◽  
Igor Urmanov ◽  
Aleksandr Kamaev ◽  
Olga Girina
Keyword(s):  
Neural Networks ◽  
High Efficiency ◽  
Activity Monitoring ◽  
Learning Technologies ◽  
Video Observation ◽  
Video Frame ◽  
Observation System ◽  
Observation Data ◽  
Area Of Interest ◽  
Observation Systems

Currently, video observation systems are actively used for volcano activity monitoring. Video cameras allow us to remotely assess the state of a dangerous natural object and to detect thermal anomalies if technical capabilities are available. However, continuous use of visible band cameras instead of special tools (for example, thermal cameras), produces large number of images, that require the application of special algorithms both for preliminary filtering out the images with area of interest hidden due to weather or illumination conditions, and for volcano activity detection. Existing algorithms use preselected regions of interest in the frame for analysis. This region could be changed occasionally to observe events in a specific area of the volcano. It is a problem to set it in advance and keep it up to date, especially for an observation network with multiple cameras. The accumulated perennial archives of images with documented eruptions allow us to use modern deep learning technologies for whole frame analysis to solve the specified task. The article presents the development of algorithms to classify volcano images produced by video observation systems. The focus is on developing the algorithms to create a labelled dataset from an unstructured archive using existing and authors proposed techniques. The developed solution was tested using the archive of the video observation system for the volcanoes of Kamchatka, in particular the observation data for the Klyuchevskoy volcano. The tests show the high efficiency of the use of convolutional neural networks in volcano image classification, and the accuracy of classification achieved 91%. The resulting dataset consisting of 15,000 images and labelled in three classes of scenes is the first dataset of this kind of Kamchatka volcanoes. It can be used to develop systems for monitoring other stratovolcanoes that occupy most of the video frame.

Design of a Cabled Seafloor Observatory for Marine Ecological Environment Monitoring

2021 ◽  
Vol 55 (2) ◽  
pp. 17-24
Author(s):  
Chao Li ◽  
Yan Li ◽  
Rui Zhu ◽  
Yu-ze Song ◽  
Lei Yang
Keyword(s):  
Current System ◽  
Ecological Environment ◽  
Observation System ◽  
In Situ Observation ◽  
Observation Data ◽  
Environment Monitoring ◽  
State Monitoring ◽  
Seafloor Observation ◽  
Shore Station

Abstract Cabled seafloor in-situ observation systems have drawn much attention in recent years for their capability of facilitating long-term all-weather deep-sea data-intense marine observations. The Penglai in-situ seafloor observation system for ecological environment monitoring is proposed in this paper. The current system consists of an on-shore station, a primary node, and two secondary nodes, but more nodes can be hosted due to its scalability. A looped backbone network connects the on-shore station and primary nodes. Each primary node can host up to four secondary nodes, and each secondary node can host up to eight different sensors. Marine observation data and system work state data are collected and backed up by the on-shore station in a real-time manner. Users can access the ocean observation data via a web page interface. The proposed system has been deployed for more than half a year and will continue to work after that. The field experiment showed that the proposed system worked smoothly in system state monitoring and marine data acquisition. A large amount of oceanographic data with videos has been achieved for future studies.

scholarly journals Socio-hydrologic data assimilation: Analyzing human-flood interactions by model-data integration

2020 ◽  
Author(s):  
Yohei Sawada ◽  
Risa Hanazaki
Keyword(s):  
Data Assimilation ◽  
Data Integration ◽  
Flood Risk ◽  
Particle Filtering ◽  
Risk Model ◽  
Observation System ◽  
Observation Data ◽  
Sequential Data ◽  
Model Data ◽  
Sequential Data Assimilation

Abstract. In socio-hydrology, human-water interactions are simulated by mathematical models. Although the integration of these socio-hydrologic models and observation data is necessary to improve the understanding of the human-water interactions, the methodological development of the model-data integration in socio-hydrology is in its infancy. Here we propose to apply sequential data assimilation, which has been widely used in geoscience, to a socio-hydrological model. We developed particle filtering for a widely adopted flood risk model and performed an idealized observation system simulation experiment to demonstrate the potential of the sequential data assimilation in socio-hydrology. In this experiment, the flood risk model's parameters, the input forcing data, and empirical social data were assumed to be somewhat imperfect. We tested if data assimilation can contribute to accurately reconstructing the historical human-flood interactions by integrating these imperfect models and imperfect and sparsely distributed data. Our results highlight that it is important to sequentially constrain both state variables and parameters when the input forcing is uncertain. Our proposed method can accurately estimate the model's unknown parameters even if the true model parameter temporally varies. The small amount of empirical data can significantly improve the simulation skill of the flood risk model. Therefore, sequential data assimilation is useful to reconstruct historical socio-hydrological processes by the synergistic effect of models and data.

scholarly journals Application of Random Forest Algorithm for Merging Multiple Satellite Precipitation Products across South Korea

2021 ◽  
Vol 13 (20) ◽  
pp. 4033
Author(s):  
Giang V. Nguyen ◽  
Xuan-Hien Le ◽  
Linh Nguyen Van ◽  
Sungho Jung ◽  
Minho Yeon ◽  
...  
Keyword(s):  
Random Forest ◽  
South Korea ◽  
Learning Algorithm ◽  
Water Cycle ◽  
Spatiotemporal Variability ◽  
Observation System ◽  
Observation Data ◽  
Satellite Precipitation ◽  
Crucial Component ◽  
Grid Based

Precipitation is a crucial component of the water cycle and plays a key role in hydrological processes. Recently, satellite-based precipitation products (SPPs) have provided grid-based precipitation with spatiotemporal variability. However, SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution of these products is still relatively coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation based on a combination of rainfall observation data with multiple SPPs for the period of 2003–2017 across South Korea. A Random Forest (RF) machine-learning algorithm model was applied for producing a new merged precipitation product. In addition, several statistical linear merging methods have been adopted to compare with the results achieved from the RF model. To investigate the efficiency of RF, rainfall data from 64 observed Automated Synoptic Observation System (ASOS) installations were collected to analyze the accuracy of products through several continuous as well as categorical indicators. The new precipitation values produced by the merging procedure generally not only report higher accuracy than a single satellite rainfall product but also indicate that RF is more effective than the statistical merging method. Thus, the achievements from this study point out that the RF model might be applied for merging multiple satellite precipitation products, especially in sparse region areas.

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