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Tokumi, Yuta, Junki Hakamata, and Masataka Tokumaru. "Development of a Nutritional Management System for a Healthy Eating Habits Support System." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 2 (2013): 324–34. http://dx.doi.org/10.20965/jaciii.2013.p0324.
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In this study, we propose a Nutritional Management System (NMS) that optimizes nutritional balance using a tabu search. Contemporary recipe retrieval systems generally retrieve a recipe either by using a keyword or by recommending a popular recipe. However, these recipe retrieval systems yield the same retrieval results for different users, and thus, the results do not necessarily reflect an individual user’s tastes. In addition, the search results delivered by many recipe retrieval systems do not always describe the nutritional details of the recipes. Therefore, we developed a Healthy Eating Habits Support System for resolving these issues. This system is composed of an NMS and a Kansei Retrieval System (KRS). The NMS retrieves nutritionally balanced menus using a tabu search. The KRS learns a user’s taste preferences, and selects menus suitable for a user’s tastes from among the menus retrieved by the NMS. The KRS needs multiple nutritionally balanced menus to learn a user’s taste preferences. Thus, in this study, we simulated scenarios to examine whether the NMS can retrieve multiple nutritionally balanced menus in the long-term without duplication, using quasi recipe data and real recipe data. When we used quasi recipe data in a simulation with a very large number of recipes, the NMS could retrieve a sustained quantity of menus in the long-term. In addition, when we used real recipe data, the NMS could quickly retrieve menus over the long-term. Therefore, the effectiveness of the NMS was confirmed.
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Lu, Shijun, Ruru Deng, Yeheng Liang, Longhai Xiong, Xianjun Ai, and Yan Qin. "Remote Sensing Retrieval of Total Phosphorus in the Pearl River Channels Based on the GF-1 Remote Sensing Data." Remote Sensing 12, no. 9 (2020): 1420. http://dx.doi.org/10.3390/rs12091420.
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Total phosphorus (TP) concentration is one of the indicators for surface water quality evaluation. In this study, an indirect algorithm was proposed to retrieve TP concentration. This algorithm retrieves the TP concentration in urban waters based on Gaofen-1 (GF-1) remote sensing data. The algorithm uses the correlation between remote-sensing reflectance, optically significant constituents of water (chlorophyll, suspended sediment, and organic matter (excluding algae)), and TP to establish a retrieval model. First, the concentrations of optically active components are retrieved using a semi-analytical model. Second, the correlation between TP and optically active components is used to retrieve the TP concentration in waters. The GF-1 remote sensing data for 7 August 2015 were used to perform remote sensing retrieval of TP concentration in the Pearl River channels in Guangzhou, China. The results show that the TP concentration in most areas of the Front Channel, Western Channel, Guangzhou Channel, and the western part of the Back Channel was higher than 0.2 mg/L, while the TP concentration in the middle and eastern parts of the Back Channel was generally lower than 0.2 mg/L. The mean absolute percentage error of the retrieval is 24.18%. The experimental results show that the model is suitable for remote sensing retrieval of TP in urban waters in Guangzhou.
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Negi, H. S., and A. Kokhanovsky. "Retrieval of snow grain size and albedo of Western Himalayan snow cover using satellite data." Cryosphere Discussions 5, no. 1 (2011): 605–53. http://dx.doi.org/10.5194/tcd-5-605-2011.
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Abstract. In the present study we describe the retrievals of snow grain size and spectral albedo (plane and spherical albedo) for Western Himalayan snow cover using Hyperion sensor data. The asymptotic radiative transfer (ART) theory was explored for the snow retrievals. To make the methodology operational only five spectral bands (440, 500, 1050, 1240 and 1650 nm) of Hyperion were used for snow parameters retrieval. The bi-spectral method (440 nm in the visible and 1050/1240 nm in the NIR region) was used to retrieve snow grain size. Spectral albedos were retrieved using satellite reflectances and estimated grain size. A good agreement was observed between retrieved snow parameters and ground observed snow-meteorological conditions. The satellite retrieved grain sizes were compared with field spectroradiometer retrieved grain sizes and close results were found for Lower Himalayan snow. The wavelength 1240 nm was found to be more suitable compared to 1050 nm for grain size retrieval along the steep slopes. The methodology was able to retrieve the spatial variations in snow parameters in different parts of Western Himalaya which are due to snow climatic and terrain conditions of Himalaya. This methodology is of importance for operational snow cover and glacier monitoring in Himalayan region using space-borne and air-borne sensors.
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Negi, H. S., and A. Kokhanovsky. "Retrieval of snow grain size and albedo of western Himalayan snow cover using satellite data." Cryosphere 5, no. 4 (2011): 831–47. http://dx.doi.org/10.5194/tc-5-831-2011.
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Abstract. In the present study we describe the retrievals of snow grain size and spectral albedo (plane and spherical albedo) for western Himalayan snow cover using Hyperion sensor data. The asymptotic radiative transfer (ART) theory was explored for the snow retrievals. To make the methodology operational only five spectral bands (440, 500, 1050, 1240 and 1650 nm) of Hyperion were used for snow parameters retrieval. The bi-spectral method (440 nm in the visible and 1050/1240 nm in the NIR region) was used to retrieve snow grain size. Spectral albedos were retrieved using satellite reflectances and estimated grain size. A good agreement was observed between retrieved snow parameters and ground observed snow-meteorological conditions. The satellite retrieved grain sizes were compared with field spectroradiometer retrieved grain sizes and close results were found for lower Himalayan snow. The wavelength 1240 nm was found to be more suitable compared to 1050 nm for grain size retrieval along the steep slopes. The methodology was able to retrieve the spatial variations in snow parameters in different parts of western Himalaya which are due to snow climatic and terrain conditions of Himalaya. This methodology is of importance for operational snow cover and glacier monitoring in Himalayan region using space-borne and air-borne sensors.
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Taha, G., D. F. Rault, R. P. Loughman, A. E. Bourassa, and C. von Savigny. "SCIAMACHY stratospheric aerosol extinction profile retrieval." Atmospheric Measurement Techniques Discussions 3, no. 6 (2010): 5343–74. http://dx.doi.org/10.5194/amtd-3-5343-2010.
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Abstract. The Ozone Mapper and Profiler Suite Limp Profiler (OMPS/LP) algorithm is used to retrieve ozone and aerosol profiles using a series of 120 SCIAMACHY limb measurements collocated with SAGE II solar occultation events. The primary goal of the study is to ascertain the capability of the OMPS/LP retrieval algorithm to accurately retrieve the vertical distribution of stratospheric aerosol extinction coefficient so as to better account for aerosol effects in the ozone profiling retrieval process. Using simulated radiances, we show that the aerosol extinction coefficient can be retrieved from limb scatter measurements within 5% and a standard deviation better than 15%, which is more than sufficient to improve the OMPS/LP ozone products to be used as Environmental Data Records. We also illustrate the ability of SCIAMACHY limb measurements to retrieve stratospheric aerosol profiles with accuracy comparable to other instruments. The retrieved aerosol profiles agree with collocated SAGE II measurements on average to within 25%, with a standard deviation of 35%.
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Taha, G., D. F. Rault, R. P. Loughman, A. E. Bourassa, and C. von Savigny. "SCIAMACHY stratospheric aerosol extinction profile retrieval using the OMPS/LP algorithm." Atmospheric Measurement Techniques 4, no. 3 (2011): 547–56. http://dx.doi.org/10.5194/amt-4-547-2011.
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Abstract. The Ozone Mapper and Profiler Suite, Limp Profiler (OMPS/LP) algorithm is used to retrieve ozone concentration and aerosol extinction profiles using a series of 120 SCIAMACHY limb measurements collocated with SAGE II solar occultation events. The primary goal of the study is to ascertain the capability of the OMPS/LP retrieval algorithm to accurately retrieve the vertical distribution of stratospheric aerosol extinction coefficient so as to better account for aerosol effects in the ozone profiling retrieval process. Using simulated radiances, we show that the aerosol extinction coefficient can be retrieved from limb scatter measurements within 5% and a standard deviation better than 15%, which is more than sufficient to improve the OMPS/LP ozone products to be used as Environmental Data Records. We also illustrate the ability of SCIAMACHY limb measurements to retrieve stratospheric aerosol extinction profiles with accuracy comparable to other instruments. The retrieved aerosol extinction profiles agree with collocated SAGE II measurements on average to within 25%, with a standard deviation of 35%.
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Yin, Xishuang, and Yi Feng. "Data Storage Architecture and Retrieval Based on Water Conservancy Data and Computer Technologies." International Journal of Agricultural and Environmental Information Systems 16, no. 1 (2025): 1–17. https://doi.org/10.4018/ijaeis.373123.
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The reasonable storage and retrieval of spatial data in rivers and lakes can promote the development of river and lake management and protection projects. In order to efficiently store and retrieve river and lake spatial data, this study adopts river and lake data and computer technology to design a data storage architecture and retrieval method based on river and lake spatial data types. The design of the structured data storage architecture adopts relational databases and document databases with spatial indexing characteristics. Use a geospatial data abstraction library to read and write raster image data from unstructured data, and use Elasticsearch to retrieve metadata. The test results show that the minimum latency of this architecture is 13ms, the average response time is 78ms, the maximum throughput is 14000 req/s, and the average failure rate is 0.106%. The designed architecture and database performance are excellent, providing technical support for efficient storage and retrieval of river and lake spatial data.
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Dhulavvagol, Praveen M., Akhilesh Gadagkar, K. J. Ateeth, Gururaj Hegade, Ritik Poonia, and S. G. Totad. "Optimised Image Storage and Retrieval on Hadoop." ITM Web of Conferences 53 (2023): 03001. http://dx.doi.org/10.1051/itmconf/20235303001.
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With the exponential growth of data, it is difficult to efficiently store and retrieve data using traditional methods. There is a need to optimize the storage and to efficiently retrieve relevant data matching the user query. Traditional methods lack optimized storage and to effectively retrieve data. To overcome these limitations, in this project, we propose a distributed architecture framework to optimize memory usage and to effectively retrieve relevant data using Content-Based Image Retrieval (CBIR). The experimental results show that the proposed model enhances storage performance and retrieval time by 20%.
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Du, Baolong, Dabin Ji, Jiancheng Shi, et al. "The Retrieval of Total Precipitable Water over Global Land Based on FY-3D/MWRI Data." Remote Sensing 12, no. 9 (2020): 1508. http://dx.doi.org/10.3390/rs12091508.
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Total precipitable water (TPW) is an important key factor in the global water cycle and climate change. The knowledge of TPW characteristics at spatial and temporal scales could help us to better understand our changing environment. Currently, many algorithms are available to retrieve TPW from optical and microwave sensors. There are still no available TPW data over land from FY-3D MWRI, which was launched by China in 2017. However, the TPW product over land is a key element for the retrieval of many ecological environment parameters. In this paper, an improved algorithm was developed to retrieve TPW over land from the brightness temperature of FY-3D MWRI. The major improvement is that surface emissivity, which is a key parameter in the retrieval of TPW in all-weather conditions, was developed and based on an improved algorithm according to the characteristics of FY-3D MWRI. The improvement includes two aspects, one is selection of appropriate ancillary data in estimating surface emissivity parameter Δε18.7/Δε23.8 in clear sky conditions, and the other is an improvement of the Δε18.7/Δε23.8 estimation function in cloudy conditions according to the band configuration of FY-3D MWRI. Finally, TPW retrieved was validated using TPW observation from the SuomiNet GPS and global distributed Radiosonde Observations (RAOB) networks. According to the validation, TPW retrieved using observations from FY-3D MWRI and ancillary data from Aqua MODIS had the best quality. The root mean square error (RMSE) and correlation coefficient between the retrieved TPW and observed TPW from RAOB were 5.47 and 0.94 mm, respectively.
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Fang, Ming, Bruce Albrecht, Eunsil Jung, Pavlos Kollias, Haflidi Jonsson, and Ivan PopStefanija. "Retrieval of Vertical Air Motion in Precipitating Clouds Using Mie Scattering and Comparison with In Situ Measurements." Journal of Applied Meteorology and Climatology 56, no. 3 (2017): 537–53. http://dx.doi.org/10.1175/jamc-d-16-0158.1.
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AbstractFor the first time, the Mie notch retrieval technique is applied to airborne cloud Doppler radar observations in warm precipitating clouds to retrieve the vertical air velocity profile above the aircraft. The retrieval algorithm prescribed here accounts for two major sources of bias: aircraft motion and horizontal wind. The retrieval methodology is evaluated using the aircraft in situ vertical air velocity measurements. The standard deviations of the residuals for the retrieved and in situ measured data for an 18-s time segment are 0.21 and 0.24 m s−1, respectively; the mean difference between the two is 0.01 m s−1. For the studied cases, the total theoretical uncertainty is less than 0.19 m s−1 and the actual retrieval uncertainty is about 0.1 m s−1. These results demonstrate that the Mie notch technique combined with the bias removal procedure described in this paper can successfully retrieve vertical air velocity from airborne radar observations with low spectral broadening due to Doppler fading, which enables new opportunities in cloud and precipitation research. A separate spectral peak due to returns from the cloud droplets is also observed in the same radar Doppler spectra and is also used to retrieve vertical air motion. The vertical air velocities retrieved using the two different methods agree well with each other, and the correlation coefficient is as high as 0.996, which indicates that the spectral peak due to cloud droplets might provide another way to retrieve vertical air velocity in clouds when the Mie notch is not detected but the cloud droplets’ spectral peak is discernable.
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Lei, Ming, Li Juan Gao, Qian Liu, and Jing Yuan Yin. "Research on AOD of Shandong Province Based on MODIS Data." Applied Mechanics and Materials 543-547 (March 2014): 2312–17. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2312.
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Based on transmission model 6S, this paper uses the moderate resolution imaging spectroradiometer (MODIS) data combined with a new aerosol business inversion algorithm, V5.2 of NASA ,to retrieve the atmospheric aerosols optical depth. We take MODIS data in Shandong province as the research data, select 8 groups of MODIS LIB data set combined with Sunny weather (Sunny and cloudless or thin cloud) and AERONET release detection date (NASA release the date of detection value), and validate the retrieved results with the aerosol optical depth (AOD) of the solar luminosity observations erected by AERONET. The results show that the V5.2 inversion algorithm results and observations presented the same change trend. The retrieved results are nearly close to observed value, and the V5.2 inversion algorithm has better application in aerosol optical thickness retrieval.
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Renno, Anas, Faisal Khateeb, Viviane Kazan, et al. "A single center experience with retrievable IVC filters." Vascular 23, no. 4 (2014): 350–57. http://dx.doi.org/10.1177/1708538114546713.
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Objective To evaluate retrievable IVC filters in our institution and assess their retrieval following a well-structured follow up program. Design Retrospective cohort study. Materials The medical records of patients implanted with retrievable IVC filters were reviewed. Methods All retrievable filter insertions between July 2007 and August 2011 at our institution were reviewed. Data was analyzed for age, gender, indication, complications, retrieval rate, and brand of filter inserted. Statistical analysis was done using SPSS software v19. Chi-square was used to compare discrete data and t-test for continuous data. P < 0.05 was significant. Results A total of 484 patients were reviewed of which 258 (53.1%) had a complete medical record. And 96 (37.2%) filters were placed as permanent at the time of insertion. An additional 40 (15.5%) filters were converted to permanent (total permanent filters 136; 52.7%). Death was reported in 26 (10%) patients and 96 (37.2%) out of the remaining 232 patients presented for potential retrieval. Also, 73 (28.2%) had an attempt to retrieve the filters, 69 (94.5%) were successful and 4 (5.4%) failed to retrieve. The remaining 23 (8.9%) patients declined retrieval. Filters studied include Celect (38%), Bard (31.4%), Option (26.2%), Tulip (4.1%), and Recovery (0.2%). Bard was more commonly used as a retrievable filter (80.9%). Retrieval on the first attempt was 90.4% ( n = 66) successful. Of the remaining seven filters, three were successfully retrieved on a second attempt, and four failed to retrieve due to filter tilt. The success rates of retrieval for Celect and Tulip were significantly lower than for Bard ( p = 0.04 and 0.023, respectively). Conclusion Our study showed that a variety of IVC filters can be retrieved successfully with minimal complication rates. In more than half of our patients, IVC filters were used as permanent. Failure of retrieval was most frequently due to filter tilting.
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Zhou, Y., X. Lu, Y. Huang, Z. Gao, and Y. Zheng. "NEW SOLAR-INDUCED CHLOROPHYLL FLUORESCENCE RETRIEVAL ALGORITHM BASED ON TANSAT SATELLITE DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 209–14. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-209-2020.
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Abstract. Solar-induced chlorophyll fluorescence (SIF) is an indicator of plant photosynthesis which could be detected by satellite. However，some existing algorithms are easily affected by the inaccuracy of satellite data which will causing deviation in the retrieval of SIF. To avoid "outliers" with inaccuracy affecting the retrieval results, a random sample consensus algorithm (RANSAC) was introduced to retrieve SIF in this paper. The results show that the chlorophyll fluorescence value obtained by this method is consistent with the OCO-2 SIF product (R2 = 0.81), and also consistent with the MODIS vegetation index (R2 = 0.87 with NDVI, R2 = 0.85 with EVI). Compared with the existing SIF products (OCO-2 SIF), the SIF retrieved in this paper was better in spatial details and outlier distribution.
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Amato, U., A. Antoniadis, I. De Feis, G. Masiello, M. Matricardi, and C. Serio. "Technical note: Functional sliced inverse regression to infer temperature, water vapour and ozone from IASI data." Atmospheric Chemistry and Physics Discussions 9, no. 2 (2009): 7589–613. http://dx.doi.org/10.5194/acpd-9-7589-2009.
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Abstract. A retrieval algorithm that uses a statistical strategy based on dimension reduction is proposed. The methodology and details of the implementation of the new algorithm are presented and discussed. The algorithm has been applied to high resolution spectra measured by the Infrared Atmospheric Sounding Interferometer instrument to retrieve atmospheric profiles of temperature, water vapour and ozone. The performance of the inversion strategy has been assessed by comparing the retrieved profiles to the ones obtained by co-locating in space and time profiles from the European Centre for Medium-Range Weather Forecasts analysis.
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Amato, U., A. Antoniadis, I. De Feis, G. Masiello, M. Matricardi, and C. Serio. "Technical Note: Functional sliced inverse regression to infer temperature, water vapour and ozone from IASI data." Atmospheric Chemistry and Physics 9, no. 14 (2009): 5321–30. http://dx.doi.org/10.5194/acp-9-5321-2009.
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Abstract. A retrieval algorithm that uses a statistical strategy based on dimension reduction is proposed. The methodology and details of the implementation of the new algorithm are presented and discussed. The algorithm has been applied to high resolution spectra measured by the Infrared Atmospheric Sounding Interferometer instrument to retrieve atmospheric profiles of temperature, water vapour and ozone. The performance of the inversion strategy has been assessed by comparing the retrieved profiles to the ones obtained by co-locating in space and time profiles from the European Centre for Medium-Range Weather Forecasts analysis.
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Zhang, Yuyuan, Yahao Liu, Yuan Kong, and Po Hu. "An Improved Method for Retrieving Subsurface Temperature Using the ConvLSTM Model in the Western Pacific Ocean." Journal of Marine Science and Engineering 12, no. 4 (2024): 620. http://dx.doi.org/10.3390/jmse12040620.
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In the era of marine big data, making full use of multi-source satellite observations to accurately retrieve and predict the temperature structure of the ocean subsurface layer is very significant in advancing the understanding of oceanic processes and their dynamics. Considering the time dependence and spatial correlation of marine characteristics, this study employed the convolutional long short-term memory (ConvLSTM) method to retrieve the subsurface temperature in the Western Pacific Ocean from several types of satellite observations. Furthermore, considering the temperature’s vertical distribution, the retrieved results for the upper layer were iteratively used in the calculation for the deeper layer as input data to improve the algorithm. The results show that the retrieved results for the 100 to 500 m depth temperature using the 50 m layer in the calculation resulted in higher accuracy than those retrieved from the standard ConvLSTM method. The largest improvement was in the calculation for the 100 m layer, where the thermocline was located. The results indicate that our improved ConvLSTM method can increase the accuracy of subsurface temperature retrieval without additional input data.
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Jasmin, Hilary. "Digital Object Identifiers (DOIs) Prove Highly Effective for Long-Term Data Availability in PLOS ONE." Evidence Based Library and Information Practice 18, no. 3 (2023): 72–74. http://dx.doi.org/10.18438/eblip30378.
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A Review of: Federer, L. M. (2022). Long-term availability of data associated with articles in PLOS ONE. PLOS ONE 17(8), Article e0272845. https://doi.org/10.1371/journal.pone.0272845 Objective – To retrieve a range of PLOS ONE data availability statements and quantify their ability to point to the study data efficiently and accurately. Research questions focused on availability over time, availability of URLs versus DOIs, the ability to locate resources using the data availability statement and availability based on data sharing method. Design – Observational study. Setting – PLOS ONE archive. Subjects – A corpus of 47,593 data availability statements from research articles in PLOS ONE between March 1, 2014, and May 31, 2016. Methods – Use of custom R scripts to retrieve 47,593 data availability statements; of these, 6,912 (14.5%) contained at least one URL or DOI. Once these links were extracted, R scripts were run to fetch the resources and record HTTP status codes to determine if the resource was discoverable. To address the potential for the DOI or URL to fetch but not actually contain the appropriate data, the researchers selected at random and manually retrieved the data for 350 URLs and 350 DOIs. Main Results – Of the unique URLs, 75% were able to be automatically retrieved by custom R scripts. In the manual sample of 350 URLs, which was used to test for accuracy of the URLs in containing the data, there was a 78% retrieval rate. Of the unique DOIs, 90% were able to be automatically retrieved by custom R scripts. The manual sample of 350 DOIs had a 98% retrieval rate. Conclusion – DOIs, especially those linked with a repository, had the highest rate of success in retrieving the data attached to the article. While URLs were better than no link at all, URLs are susceptible to content drift and need more management for long-term data availability.
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Tu, Jinsheng, Haohan Wei, Rui Zhang, et al. "GNSS-IR Snow Depth Retrieval from Multi-GNSS and Multi-Frequency Data." Remote Sensing 13, no. 21 (2021): 4311. http://dx.doi.org/10.3390/rs13214311.
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Global navigation satellite system interferometric reflectometry (GNSS-IR) represents an extra method to detect snow depth for climate research and water cycle managing. However, using a single frequency of GNSS-IR for snow depth retrieval is often found to be challenging when attempting to achieve a high spatial and temporal sensitivity. To evaluate both the capability of the GNSS-IR snow depth retrieved by the multi-GNSS system and multi-frequency from signal-to-noise ratio (SNR) data, the accuracy of snow depth retrieval by different frequency signals from the multi-GNSS system is analyzed, and a joint retrieval is carried out by combining the multi-GNSS system retrieval results. The SNR data of the global positioning system (GPS), global orbit navigation satellite system (GLONASS), Galileo satellite navigation system (Galileo), and BeiDou navigation satellite system (BDS) from the P387 station of the U.S. Plate Boundary Observatory (PBO) are analyzed. A Lomb–Scargle periodogram (LSP) spectrum analysis is used to compare the difference in reflector height between the snow-free and snow surfaces in order to retrieve the snow depth, which is compared with the PBO snow depth. First, the different frequency retrieval results of the multi-GNSS system are analyzed. Then, the retrieval accuracy of the different GNSS systems is analyzed through multi-frequency mean fusion. Finally, the joint retrieval accuracy of the multi-GNSS system is analyzed through mean fusion. The experimental shows that the retrieval results of different frequencies of the multi-GNSS system have a strong correlation with the PBO snow depth, and that the accuracy is better than 10 cm. The multi-frequency mean fusion of different GNSS systems can effectively improve the retrieval accuracy, which is better than 7 cm. The joint retrieval accuracy of the multi-GNSS system is further improved, with a correlation coefficient (R) between the retrieval snow depth and the PBO snow depth of 0.99, and the accuracy is better than 3 cm. Therefore, using multi-GNSS and multi-frequency data to retrieve the snow depth has a good accuracy and feasibility.
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Huang, Pengyu, Qiang Guo, Changpei Han, Chunming Zhang, Tianhang Yang, and Shuo Huang. "An Improved Method Combining ANN and 1D-Var for the Retrieval of Atmospheric Temperature Profiles from FY-4A/GIIRS Hyperspectral Data." Remote Sensing 13, no. 3 (2021): 481. http://dx.doi.org/10.3390/rs13030481.
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In our study, a retrieval method of temperature profiles is proposed which combines an improved one-dimensional variational algorithm (1D-Var) and artificial neural network algorithm (ANN), using FY-4A/GIIRS (Geosynchronous Interferometric Infrared Sounder) infrared hyperspectral data. First, according to the characteristics of the FY-4A/GIIRS observation data using the conventional 1D-Var, we introduced channel blacklists and discarded the channels that have a large negative impact on retrieval, then used the information capacity method for channel selection and introduced a neural network to correct the satellite observation data. The improved 1D-Var effectively used the observation information of 1415 channels, reducing the impact of the error of the satellite observation and radiative transfer model, and realizing the improvement of retrieval accuracy. We subsequently used the improved 1D-Var and ANN algorithms to retrieve the temperature profiles, respectively, from the GIIRS data. The results showed that the accuracy when using ANN is better than using improved 1D-Var in situations where the pressure ranges from 800 hPa to 1000 hPa. Therefore, we combined the improved 1D-Var and ANN method to retrieve temperature profiles for different pressure levels, calculating the error by taking sounding data published by the University of Wyoming as the true values. The results show that the average error of the retrieved temperature profiles is smaller than 2 K when using our method, this method makes the accuracy of the retrieved temperature profiles superior to the accuracy of the GIIRS products from 10 hPa to 575 hPa. All in all, through the combination of the physical retrieval method and the machine learning retrieval method, this paper can certainly provide a reference for improving the accuracy of products.
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Satish, Bansal. "A REVIEW: IMAGE RETRIEVAL USING WEB MULTIMEDIA MINING." International Journal of Advances in Engineering & Scientific Research 1, no. 4 (2014): 113–17. https://doi.org/10.5281/zenodo.10720525.
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<strong>Abstract</strong> <em>Multimedia object include audio, video, Image, text etc. where image retrieve play very important role to find any kind of image information which use in business images, satellite images, medical images and so on. Web Multimedia mining is one kind of web mining which retrieving useful multimedia information from web. Image retrieval using web multimedia mining deals with the retrieval of useful image based on the user’s search requirement. This paper focus on web mining, multimedia mining which retrieve all kind of data or image information based on the search pattern of the user’s.</em> <strong><em>Keyword</em></strong><em>: Web Mining, Multimedia Image retrieval, Image Mining, Multimedia Data Mining.</em>
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Leinonen, Jussi, Matthew D. Lebsock, Simone Tanelli, et al. "Retrieval of snowflake microphysical properties from multifrequency radar observations." Atmospheric Measurement Techniques 11, no. 10 (2018): 5471–88. http://dx.doi.org/10.5194/amt-11-5471-2018.
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Abstract. We have developed an algorithm that retrieves the size, number concentration and density of falling snow from multifrequency radar observations. This work builds on previous studies that have indicated that three-frequency radars can provide information on snow density, potentially improving the accuracy of snow parameter estimates. The algorithm is based on a Bayesian framework, using lookup tables mapping the measurement space to the state space, which allows fast and robust retrieval. In the forward model, we calculate the radar reflectivities using recently published snow scattering databases. We demonstrate the algorithm using multifrequency airborne radar observations from the OLYMPEX–RADEX field campaign, comparing the retrieval results to hydrometeor identification using ground-based polarimetric radar and also to collocated in situ observations made using another aircraft. Using these data, we examine how the availability of multiple frequencies affects the retrieval accuracy, and we test the sensitivity of the algorithm to the prior assumptions. The results suggest that multifrequency radars are substantially better than single-frequency radars at retrieving snow microphysical properties. Meanwhile, triple-frequency radars can retrieve wider ranges of snow density than dual-frequency radars and better locate regions of high-density snow such as graupel, although these benefits are relatively modest compared to the difference in retrieval performance between dual- and single-frequency radars. We also examine the sensitivity of the retrieval results to the fixed a priori assumptions in the algorithm, showing that the multifrequency method can reliably retrieve snowflake size, while the retrieved number concentration and density are affected significantly by the assumptions.
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Wurl, D., R. G. Grainger, A. J. McDonald, and T. Deshler. "Optimal estimation retrieval of aerosol microphysical properties from SAGE II satellite observations in the lower stratosphere." Atmospheric Chemistry and Physics Discussions 9, no. 6 (2009): 23719–53. http://dx.doi.org/10.5194/acpd-9-23719-2009.
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Abstract. A new retrieval algorithm is presented, which is based on the Optimal Estimation (OE) approach and aimed to improve current estimates of aerosol microphysical properties under non-volcanic conditions. The new OE algorithm retrieves log-normal particle size distribution parameters and associated uncertainties from multi-wavelength aerosol extinction data at visible to near infrared wavelengths. The algorithm was tested on synthetic data and then applied to SAGE (Stratospheric Aerosol and Gas Experiment) II data measured in 1999 in the lower stratosphere between 10 and 35 km. Model validation based on synthetic data shows that the new algorithm is able to retrieve the particle size of typical background aerosols accurately and that the retrieved uncertainties are a good estimate of the true errors. Aerosol properties retrieved from measured SAGE II extinction data (recorded in 1999) using the OE approach were compared to Principal Component Analysis (PCA) results retrieved from the same SAGE II data set. The OE surface area and volume densities are observed to be larger than the PCA values by 20–50% and 10–40% whereas the OE effective radii tend to be smaller by about 10–40%. An examination of the OE algorithm biases with in situ data indicates that the new OE estimates are likely to be more realistic than the PCA results. Based on the results of this study we suggest that the new OE retrieval algorithm provides improved estimates of aerosol properties in the lower stratosphere under low aerosol loading conditions.
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Qi, Lin, Ronggao Liu, and Yang Liu. "Retrieval of Aerosol Single-Scattering Albedo from MODIS Data Using an Artificial Neural Network." Remote Sensing 14, no. 24 (2022): 6341. http://dx.doi.org/10.3390/rs14246341.
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Aerosol single-scattering albedo (SSA) is one of the largest sources of uncertainty in the evaluation of the aerosol radiative forcing effect. The SSA signal, coupled with aerosol optical depth (AOD) and surface reflectance in satellite images, is difficult to retrieve by the look-up table approach. In this study, we proposed an artificial neural network- (ANN) based approach that retrieves SSA over land based on MODIS (moderate resolution imaging spectroradiometer) visible (red band) reflectance variations among nearby pixels that have different surface reflectivities. Using the training dataset generated by the radiative transfer model, the ANN model was trained to establish the relationship among SSA, surface reflectance, and top of atmosphere (TOA) reflectance. Then, based on the trained ANN model, SSA can be retrieved using the surface and apparent reflectance of several heterogeneous pixels. According to sensitivity analysis, this method works well on nonuniform land surfaces with high AODs. The root mean square error (RMSE) of retrieved and measured SSA (from 28 sites of AErosol RObotic NETwork, AERONET) was 0.042, of which the results with an error less than 0.03 accounted for 51%. In addition, the SSA retrieval method was applied to several thick aerosol layer events over different areas (South Asia, South America, and North China Plain) and compared with the ozone monitoring instrument near-UV aerosol data product (OMAERUV). The comparison results of the images show that the retrieval method of visible wavelength proposed in this study has similar outcomes to those from the ultraviolet wavelengths in these regions. The retrieval algorithm we propose provides an effective way to produce an SSA product in visible wavelength and might help to better estimate the aerosol radiative and optical properties over high heterogeneous areas, which is important for the aerosol radiative impact estimate at a regional scale.
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Hamzeh, S., M. Hajeb, S. K. Alavipanah, and J. Verrelst. "RETRIEVAL OF SUGARCANE LEAF AREA INDEX FROM PRISMA HYPERSPECTRAL DATA." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (January 13, 2023): 271–77. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-271-2023.
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Abstract. The PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite of the Italian Space Agency, lunched in 2019, has provided a new generation source of hyperspectral data showing to have high potential in vegetation variable retrieval. In this study, the newly available PRISMA spectra were exploited to retrieve Leaf Area Index (LAI) of sugarcane using a new kind of Artificial Neural Networks (ANN) so-called Bayesian Regularized Artificial Neural Network (BRANN). The suggested BRANN retrieval model was implemented over a dataset collected during a field campaign in Amir Kabir Sugarcane Agro-Industrial zone, Khuzestan, Iran, in 2020. Principle Component Analysis (PCA) was utilized to reduce the dimensionality of PRISMA data cube. An accuracy assessment based on the bootstrapping procedure indicated RMSE of 0.67 m2/m2 for the LAI retrieval by applying the BRANN model. This study is a confirmation of the high performance of the BRANN method and high potential of PRISMA images to retrieve sugarcane LAI.
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Ben Ayed, Alaidine, Ismaïl Biskri, and Jean-Guy Meunier. "An End-to-End Efficient Lucene-Based Framework of Document/Information Retrieval." International Journal of Information Retrieval Research 12, no. 1 (2022): 1–14. http://dx.doi.org/10.4018/ijirr.289950.
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In the context of big data and the 4.0 industrial revolution era, enhancing document/information retrieval frameworks efficiency to handle the ever‐growing volume of text data in an ever more digital world is a must. This article describes a double-stage system of document/information retrieval. First, a Lucene-based document retrieval tool is implemented, and a couple of query expansion techniques using a comparable corpus (Wikipedia) and word embeddings are proposed and tested. Second, a retention-fidelity summarization protocol is performed on top of the retrieved documents to create a short, accurate, and fluent extract of a longer retrieved single document (or a set of top retrieved documents). Obtained results show that using word embeddings is an excellent way to achieve higher precision rates and retrieve more accurate documents. Also, obtained summaries satisfy the retention and fidelity criteria of relevant summaries.
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Hao, Jianying, Yuhong Ren, Yuanying Zhang, et al. "Non-interferometric phase retrieval for collinear phase-modulated holographic data storage." Optical Review 27, no. 5 (2020): 419–26. http://dx.doi.org/10.1007/s10043-020-00611-x.
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Abstract Because of its simple optical system setup and robust noise tolerance, non-interferometric phase retrieval is an important technique for phase-modulated holographic data storage. Usually, the iterative algorithm of non-interferometry needs hundreds of iteration numbers to retrieve phase accurately, the data transfer rate decreases severely. Strong constraints such as adding embedded data into the phase data page can reduce the iteration numbers, but this method decreases the code rate severely. In this paper, we proposed the advanced non-interferometric phase retrieval method based on the collinear system. By encoding the reference beam of the collinear optical holographic storage system with embedded data, the storage space of the signal beam data page is completely released and the encoding rate is doubled. The embedded data can provide more modulation index including phase and amplitude to shorten iterations, so the data transfer rate is also increased. In the simulation, we recorded a four-level phase pattern and retrieved the phase correctly.
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Lv, Na, Yan Huang, Zhi Quan Feng, and Jing Liang Peng. "A Survey on Motion Capture Data Retrieval." Applied Mechanics and Materials 556-562 (May 2014): 2944–47. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2944.
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With the rapid development of motion capture technology, large motion capture databases are established. How to effectively retrieve the motions from huge amounts of motion data has become a hot topic in computer animation. In this paper, we give a survey on current motion capture data retrieval methods and point out some still existing problems at the end.
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Xue, Yixuan, Xiaolin Zhu, Zihao Wu, and Si-Bo Duan. "Retrieval of Land Surface Temperature over Mountainous Areas Using Fengyun-3D MERSI-II Data." Remote Sensing 15, no. 23 (2023): 5465. http://dx.doi.org/10.3390/rs15235465.
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Land surface temperature (LST) is an important physical quantity in the energy exchange of hydrothermal cycles between the land and near-surface atmosphere at regional and global scales. However, the traditional thermal infrared transfer equation (RTE) and LST retrieval algorithms are always based on the underlying assumptions of homogeneity and isotropy, which ignore the terrain effect influence of a heterogeneous topography. It can cause significant errors when traditional RTE and other algorithms are used to retrieve LST in such mountainous research. In this study, the mountainous thermal infrared transfer model considering terrain effect correction is used to retrieve the mountainous LST using FY-3D MERSI-II data, and the in situ site data are simultaneously utilized to evaluate the performance of the iterative single-channel algorithm. The elevation of this study region ranges from 500 m to 2200 m, whereas the minimum SVF can reach 0.75. Results show that the spatial distribution of the retrieved LST is similar to topographic features, and the LST has larger values in the lower valley and smaller values in the higher ridge. In addition, the overall bias and RMSE between the retrieved LSTs and five in situ stations are respectively −0.70 K and 2.64 K, which demonstrates this iterative single-channel algorithm performs well in taking into account the terrain effect influence. Accuracy of the LST estimation is meaningful for mountainous ecological environmental monitoring and global climate research. Such an adjacent terrain effect correction should be considered in future research on complex terrains, especially with high spatial resolution TIR data.
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Zhang, Yu-Ze, Xiao-Guang Jiang, Hua Wu, Ya-Zhen Jiang, Zhao-Xia Liu, and Cheng Huang. "Land Surface Temperature and Emissivity Separation from Cross-Track Infrared Sounder Data with Atmospheric Reanalysis Data and ISSTES Algorithm." Advances in Meteorology 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/7398312.
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The Cross-track Infrared Sounder (CrIS) is one of the most advanced hyperspectral instruments and has been used for various atmospheric applications such as atmospheric retrievals and weather forecast modeling. However, because of the specific design purpose of CrIS, little attention has been paid to retrieving land surface parameters from CrIS data. To take full advantage of the rich spectral information in CrIS data to improve the land surface retrievals, particularly the acquisition of a continuous Land Surface Emissivity (LSE) spectrum, this paper attempts to simultaneously retrieve a continuous LSE spectrum and the Land Surface Temperature (LST) from CrIS data with the atmospheric reanalysis data and the Iterative Spectrally Smooth Temperature and Emissivity Separation (ISSTES) algorithm. The results show that the accuracy of the retrieved LSEs and LST is comparable with the current land products. The overall differences of the LST and LSE retrievals are approximately 1.3 K and 1.48%, respectively. However, the LSEs in our study can be provided as a continuum spectrum instead of the single-channel values in traditional products. The retrieved LST and LSEs now can be better used to further analyze the surface properties or improve the retrieval of atmospheric parameters.
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Uma, R., and B. Latha. "An efficient voice based information retrieval using bag of words based indexing." International Journal of Engineering & Technology 7, no. 3.3 (2018): 622. http://dx.doi.org/10.14419/ijet.v7i2.33.14850.
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Data mining is one of the leading and drastically growing researches nowadays. One of the main areas in data mining is Information Retrieval (IR). Information retrieval is a broad job and it is finding information without any structured nature. Infor-mation retrieval retrieves the user required information from a large collection of data. The existing approaches yet to improve the accuracy in terms of relevant accuracy. In this paper, it is motivated to provide an Information Retrieval System (IRS) where it can retrieve information with high relevancy. The proposed IRS is specially designed for physically challenged people like blind people where the input and the output taken/given is voice. The functionality of proposed IRS consists of three stages such as: (i) Voice to Text input, (II). Pattern Matching, and (III). Text to Voice output.In order to improve the accuracy and relevancy the proposed IRS uses an indexing method called Bag of Words (BOW). BOW is like an index-table which can be referred to store, compare and retrieve the information speedily and accurately. Index-table utilization in IRS improves the accuracy with minimized computational complexity. The proposed IRS is simulated in DOTNET software and the results are compared with the existing system results in order to evaluate the performance.
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Friedrich, Martina Michaela, Claudia Rivera, Wolfgang Stremme, et al. "NO<sub>2</sub> vertical profiles and column densities from MAX-DOAS measurements in Mexico City." Atmospheric Measurement Techniques 12, no. 4 (2019): 2545–65. http://dx.doi.org/10.5194/amt-12-2545-2019.
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Abstract. We present a new numerical code, Mexican MAX-DOAS Fit (MMF), developed to retrieve profiles of different trace gases from the network of MAX-DOAS instruments operated in Mexico City. MMF uses differential slant column densities (dSCDs) retrieved with the QDOAS (Danckaert et al., 2013) software. The retrieval is comprised of two steps, an aerosol retrieval and a trace gas retrieval that uses the retrieved aerosol profile in the forward model for the trace gas. For forward model simulations, VLIDORT is used (e.g., Spurr et al., 2001; Spurr, 2006, 2013). Both steps use constrained least-square fitting, but the aerosol retrieval uses Tikhonov regularization and the trace gas retrieval optimal estimation. Aerosol optical depth and scattering properties from the AERONET database, averaged ceilometer data, WRF-Chem model data, and temperature and pressure sounding data are used for different steps in the retrieval chain. The MMF code was applied to retrieve NO2 profiles with 2 degrees of freedom (DOF = 2) from spectra of the MAX-DOAS instrument located at the Universidad Nacional Autónoma de México (UNAM) campus. We describe the full error analysis of the retrievals and include a sensitivity exercise to quantify the contribution of the uncertainties in the aerosol extinction profiles to the total error. A data set comprised of measurements from January 2015 to July 2016 was processed and the results compared to independent surface measurements. We concentrate on the analysis of four single days and additionally present diurnal and annual variabilities from averaging the 1.5 years of data. The total error, depending on the exact counting, is 14 %–20 % and this work provides new and relevant information about NO2 in the boundary layer of Mexico City.
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Куликов, М. Ю., M. V. Belikovich, A. G. Chubarov, S. O. Dementyeva, and A. M. Feigin. "Retrieval of Nighttime Distributions of Mesosphere–Lower Thermosphere Characteristics from Satellite Data." Известия Российской академии наук. Физика атмосферы и океана 60, no. 1 (2024): 81–94. http://dx.doi.org/10.31857/s0002351524010086.
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The database of SABER/TIMED satellite campaign includes the distributions of nighttime O, H and some other characteristics of mesosphere – lower thermosphere region which are retrieved from the measurements of OH* volume emission rate (near 2 μm), temperature and ozone. In the core of the retrieval procedure lies the assumption about photochemical equilibrium of nighttime ozone and airglow model that considers two excited states of OH (levels ν = 9, 8). In this work, a modified OH* model (with the rate constants updated according to contemporary publications) is used to retrieve O, H, OH, HO2 and the chemical heating rate at 80–100 km altitudes from to SABER/TIMED measurements in 2002–2021. It was found that the use of new parameters in the retrieval procedure leads to significant (up to 2 times or more) changes in the resulting spatial distributions of O, H and chemical heating rate, while the corresponding changes in OH and HO2 distributions are minor.
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Rezvani, Shahla, Nader Naghshineh, and Ahmad Khalilijafarabad. "Identification of LSA Data Retrieval Method and Temporal Graph for Document Retrieval." Tehnički glasnik 19, no. 1 (2025): 9–16. https://doi.org/10.31803/tg-20230715000112.
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The field of expert finding has seen a large number of approaches proposed both in universities and in industries, using a variety of new techniques in relevant data fields. This study tends to identify information retrieval method of latent semantic analysis and temporal graph for document retrieval. In this study, citation occurrence and author occurrence are independent variables and scales of expert author finding are dependent variables. The method used to evaluate judgment of document and author relevance in the test set formation phase is more similar to survey methods. Library method is used to study theoretical foundations and judge literature. This study has three populations: a) test set documents; b) people who make queries and judge relevance of retrieved documents; c) people who judge relevance of the retrieved experts. To measure judgments of document relevance, a method similar to peer tests is used. Among the retrieved results, repeated results are placed to determine accuracy and reliability of the judge. The degree of correlation obtained in this method is very high (0.98), indicating the reliability of the results. Regarding the results of the current study on application of latent semantic indexing (LSA) information retrieval model, which was ultimately used to retrieve expert authors, the performance of LSA-based retrieval model outperformed the baseline model. This was evident from the obtained metrics, including precision at the top 5 results (p@5) with a value of 0.895, mean average precision (MAP) of 0.839, and mean reciprocal rank (MRR) of 0.909. The improved retrieval performance can be attributed to the superior performance of the dimension reduction method compared to keyword matching.
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Zakšek, K., M. Hort, J. Zaletelj, and B. Langmann. "Monitoring volcanic ash cloud top height through simultaneous retrieval of optical data from polar orbiting and geostationary satellites." Atmospheric Chemistry and Physics Discussions 12, no. 9 (2012): 25617–56. http://dx.doi.org/10.5194/acpd-12-25617-2012.
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Abstract. Volcanic ash cloud top height (ACTH) can be monitored on the global level using satellite remote sensing. Here we propose a photogrammetric method based on the parallax between data retrieved from geostationary and polar orbiting satellites to overcome some limitations of the existing methods of ACTH retrieval. SEVIRI HRV band and MODIS band 1 are a good choice because of their high resolution. The procedure works well if the data from both satellites are retrieved nearly simultaneously. MODIS does not retrieve the data at exactly the same time as SEVIRI. To compensate for advection we use two sequential SEVIRI images (one before and one after the MODIS retrieval) and interpolate the cloud position from SEVIRI data to the time of MODIS retrieval. The proposed method was tested for the case of the Eyjafjallajökull eruption in April 2010. The parallax between MODIS and SEVIRI data can reach over 30 km which implies ACTH of more than 12 km in the beginning of the eruption. In the end of April eruption ACTH of 3–4 km is observed. The accuracy of ACTH was estimated to be 0.6 km.
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Zakšek, K., M. Hort, J. Zaletelj, and B. Langmann. "Monitoring volcanic ash cloud top height through simultaneous retrieval of optical data from polar orbiting and geostationary satellites." Atmospheric Chemistry and Physics 13, no. 5 (2013): 2589–606. http://dx.doi.org/10.5194/acp-13-2589-2013.
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Abstract. Volcanic ash cloud-top height (ACTH) can be monitored on the global level using satellite remote sensing. Here we propose a photogrammetric method based on the parallax between data retrieved from geostationary and polar orbiting satellites to overcome some limitations of the existing methods of ACTH retrieval. SEVIRI HRV band and MODIS band 1 are a good choice because of their high resolution. The procedure works well if the data from both satellites are retrieved nearly simultaneously. MODIS does not retrieve the data at exactly the same time as SEVIRI. To compensate for advection we use two sequential SEVIRI images (one before and one after the MODIS retrieval) and interpolate the cloud position from SEVIRI data to the time of MODIS retrieval. The proposed method was tested for the case of the Eyjafjallajökull eruption in April 2010. The parallax between MODIS and SEVIRI data can reach 30 km, which implies an ACTH of approximately 12 km at the beginning of the eruption. At the end of April eruption an ACTH of 3–4 km is observed. The accuracy of ACTH was estimated to be 0.6 km.
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Ahmed, Shaikh Asharfali, and Megha Singh. "IMPROVED FUZZY SEARCHINGTECHNOLOGY." COMPUSOFT: An International Journal of Advanced Computer Technology 04, no. 07 (2015): 1928–30. https://doi.org/10.5281/zenodo.14786680.
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In web based applications information retrieval is gaining more popular. There are many advances of searching such as instant fuzzy search. Even if there are few typing errors in words the system will retrieve relevant data. Fuzzy search retrieves relevant data containing words which are similar to keywords. A main computational challenge in this paradigm is the high speed requirement, i.e., each query needs to be answered within milliseconds to achieve an instant response. Fuzzy search is implemented using Edit"s Distance method.
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Gupta, Rajeev, and Virender Singh. "COMPARATIVE ANALYSIS OF IMAGE RETRIEVAL TECHNIQUES IN CYBERSPACE." International Journal of Students' Research in Technology & Management 8, no. 1 (2020): 01–10. http://dx.doi.org/10.18510/ijsrtm.2020.811.
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Purpose: With the popularity and remarkable usage of digital images in various domains, the existing image retrieval techniques need to be enhanced. The content-based image retrieval is playing a vital role to retrieve the requested data from the database available in cyberspace. CBIR from cyberspace is a popular and interesting research area nowadays for a better outcome. The searching and downloading of the requested images accurately based on meta-data from the cyberspace by using CBIR techniques is a challenging task. The purpose of this study is to explore the various image retrieval techniques for retrieving the data available in cyberspace. 
 Methodology: Whenever a user wishes to retrieve an image from the web, using present search engines, a bunch of images is retrieved based on a user query. But, most of the resultant images are unrelated to the user query. Here, the user puts their text-based query in the web-based search engine and compute the related images and retrieval time.
 Main Findings: This study compares the accuracy and retrieval-time of the requested image. After the detailed analysis, the main finding is none of the used web-search engines viz. Flickr, Pixabay, Shutterstock, Bing, Everypixel, retrieved the accurate related images based on the entered query. 
 Implications: This study is discussing and performs a comparative analysis of various content-based image retrieval techniques from cyberspace.
 Novelty of Study: Research community has been making efforts towards efficient retrieval of useful images from the web but this problem has not been solved and it still prevails as an open research challenge. This study makes some efforts to resolve this research challenge and perform a comparative analysis of the outcome of various web-search engines.
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Shen, Jun Hong, Ching Ta Lu, and Ming Shen Jian. "Neighbor-Index Method for Continuous Window Queries over Wireless Data Broadcast." Applied Mechanics and Materials 284-287 (January 2013): 3295–99. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3295.
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A continuous window query is an important class of spatial queries for location-based services. It retrieves spatial objects in a fixed window region of every point on a line segment and indicates the valid segments of them. In this paper, we focus on continuous window queries in wireless data broadcast systems. Since the query result of the continuous window queries has the spatial locality, providing neighbor information of spatial objects can guide clients to efficiently retrieve related objects. Therefore, we propose a neighbor-index method to efficiently support the continuous window queries in wireless data broadcast systems. The proposed method interleaves the neighbor information between spatial objects to guide mobile clients to quickly retrieve the answered objects and save the power consumption of the mobile devices. Experimental results show that our method outperforms the distributed indexing.
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Hassan, Musa Dauda. "Data Curation: A Perspective of Information Retrieval to Society." International Journal of Research and Scientific Innovation XII, no. III (2025): 563–65. https://doi.org/10.51244/ijrsi.2025.12030040.
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In today’s data-driven world, the ability to effectively manage, organize, and retrieve digital information is critical to societal advancement. The huge and expanding volume of digital content demands effective data curation procedures to assure accessibility, usefulness, and dependability. Information retrieval systems play an important part in this process by organizing data to facilitate knowledge sharing, decision-making, and innovation. This article investigates the importance of data curation, its impact on information retrieval, and its broader consequences for society.
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Du, Baoyu, Kebiao Mao, Sayed M. Bateni, et al. "A Novel Fully Coupled Physical–Statistical–Deep Learning Method for Retrieving Near-Surface Air Temperature from Multisource Data." Remote Sensing 14, no. 22 (2022): 5812. http://dx.doi.org/10.3390/rs14225812.
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Retrieval of near-surface air temperature (NSAT) from remote sensing data is often ill-posed because of insufficient observational information. Many factors influence the NSAT, which can lead to the instability of the accuracy of traditional algorithms. To overcome this problem, in this study, a fully coupled framework was developed to robustly retrieve NSAT from thermal remote sensing data, integrating physical, statistical, and deep learning methods (PS-DL). Based on physical derivation, the optimal combinations of remote sensing bands were chosen for building the inversion equations to retrieve NSAT, and deep learning was used to optimize the calculations. Multisource data (physical model simulations, remote sensing data, and assimilation products) were used to establish the training and test databases. The NSAT retrieval accuracy was enhanced using the land surface temperature (LST) and land surface emissivity (LSE) as prior knowledge. The highest mean absolute error (MAE) and root-mean-square error (RMSE) of the retrieved NSAT data were 0.78 K and 0.89 K, respectively. In a cross-validation against the China Meteorological Forcing Dataset (CMFD), the MAE and RMSE were 1.00 K and 1.29 K, respectively. The actual inversion MAE and RMSE for the optimal band combination were 1.21 K and 1.33 K, respectively. The proposed method effectively overcomes the limitations of traditional methods as the inversion accuracy is enhanced by adding the information of atmospheric water vapor and more bands, and the applicability (portability) of the algorithm is enhanced using LST and LSE as prior knowledge. This model can become a general inversion paradigm for geophysical parameter retrieval, which is of milestone significance because of its accuracy and the ability to allow deep learning for physical interpretation.
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Sun, L., R. Li, and H. Yu. "A NEW ALGORITHM FOR THE AOD INVERSION FROM NOAA/AVHRR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1587–88. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1587-2018.
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The advanced very high resolution radiometer (AVHRR) data from the National Oceanic and Atmospheric Administration satellite is one of the earliest data applied in aerosol research. The dense dark vegetation (DDV) algorithm is a popular method for the present land aerosol retrieval. One of the most crucial steps in the DDV algorithm with AVHRR data is estimating the land surface reflectance (LSR). However, LSR cannot be easily estimated because of the lack of a 2.13&amp;thinsp;μm band. In this article, the moderate resolution imaging spectroradiometer (MODIS) vegetation index product (MYD13) is introduced to support the estimation of AVHRR LSR. The relationship between MODIS NDVI and the AVHRR LSR of the visible band is analysed to retrieve aerosol optical depth (AOD) from AVHRR data. Retrieval experiments are carried out in mid-eastern America. The AOD data from AErosol RObotic NETwork (AERONET) measurements are used to evaluate the aerosol retrieval from AVHRR data, the results indicate that about 74&amp;thinsp;% of the retrieved AOD are within the expected error range of &amp;plusmn;(0.05&amp;thinsp;+&amp;thinsp;0.2), and a cross comparison of the AOD retrieval results with the MODIS aerosol product (MYD04) shows that the AOD datasets have a similar spatial distribution.
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Kyrölä, E., J. Tamminen, V. Sofieva, et al. "Retrieval of atmospheric parameters from GOMOS data." Atmospheric Chemistry and Physics Discussions 10, no. 4 (2010): 10145–217. http://dx.doi.org/10.5194/acpd-10-10145-2010.
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Abstract. The Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument on board the European Space Agency's ENVISAT satellite measures attenuation of stellar light in occultation geometry. Daytime measurements also record scattered solar light from the atmosphere. The wavelength regions are the ultraviolet-visible band 248–690 nm and two infrared bands at 755–774 nm and at 926–954 nm. From UV-Visible and IR spectra the vertical profiles of O3, NO2, NO3, H2O, O2 and aerosols can be retrieved. In addition there are two 1 kHz photometers at blue 473–527 nm and red 646–698 nm. Photometer data are used to correct spectrometer measurements for scintillations and to retrieve high resolution temperature profiles as well as gravity wave and turbulence parameters. Measurements cover altitude region 5–150 km. Atmospherically valid data are obtained in 15–100 km. In this paper we present an overview of the GOMOS retrieval algorithms for stellar occultation measurements. The low signal-to-noise ratio and the refractive effects due to the point source nature of stars have been important drivers in the development of GOMOS retrieval algorithms. We present first the Level 1b algorithms that are used to correct instrument related disturbances in the spectrometer and photometer measurements The Level 2 algorithms deal with the retrieval of vertical profiles of atmospheric gaseous constituents, aerosols and high resolution temperature. We divide the presentation into correction for refractive effects, high resolution temperature retrieval and spectral/vertical inversion. The paper also includes discussion about the GOMOS algorithm development, expected improvements, access to GOMOS data and alternative retrieval approaches.
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Kyrölä, E., J. Tamminen, V. Sofieva, et al. "Retrieval of atmospheric parameters from GOMOS data." Atmospheric Chemistry and Physics 10, no. 23 (2010): 11881–903. http://dx.doi.org/10.5194/acp-10-11881-2010.
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Abstract. The Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument on board the European Space Agency's ENVISAT satellite measures attenuation of stellar light in occultation geometry. Daytime measurements also record scattered solar light from the atmosphere. The wavelength regions are the ultraviolet-visible band 248–690 nm and two infrared bands at 755–774 nm and at 926–954 nm. From UV-Visible and IR spectra the vertical profiles of O3, NO2, NO3, H2O, O2 and aerosols can be retrieved. In addition there are two 1 kHz photometers at blue 473–527 nm and red 646–698 nm. Photometer data are used to correct spectrometer measurements for scintillations and to retrieve high resolution temperature profiles as well as gravity wave and turbulence parameters. Measurements cover altitude region 5–150 km. Atmospherically valid data are obtained in 15–100 km. In this paper we present an overview of the GOMOS retrieval algorithms for stellar occultation measurements. The low signal-to-noise ratio and the refractive effects due to the point source nature of stars have been important drivers in the development of GOMOS retrieval algorithms. We present first the Level 1b algorithms that are used to correct instrument related disturbances in the spectrometer and photometer measurements The Level 2 algorithms deal with the retrieval of vertical profiles of atmospheric gaseous constituents, aerosols and high resolution temperature. We divide the presentation into correction for refractive effects, high resolution temperature retrieval and spectral/vertical inversion. The paper also includes discussion about the GOMOS algorithm development, expected improvements, access to GOMOS data and alternative retrieval approaches.
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Fu, Zihao, Lidong Bing, and Wai Lam. "Open Domain Event Text Generation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7748–55. http://dx.doi.org/10.1609/aaai.v34i05.6278.
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Text generation tasks aim at generating human-readable text from different kinds of data. Normally, the generated text only contains the information included in the data and its application is thus restricted to some limited scenarios. In this paper, we extend the task to an open domain event text generation scenario with an entity chain as its skeleton. Specifically, given an entity chain containing several related event entities, the model should retrieve from a trustworthy repository (e.g. Wikipedia) the detailed information of these entities and generate a description text based on the retrieved sentences. We build a new dataset called WikiEvent1 that provides 34K pairs of entity chain and its corresponding description sentences. To solve the problem, we propose a wiki augmented generator framework that contains an encoder, a retriever, and a decoder. The encoder encodes the entity chain into a hidden space while the decoder decodes from the hidden space and generates description text. The retriever retrieves relevant text from a trustworthy repository which provides more information for generation. To alleviate the overfitting problem, we propose a novel random drop component that randomly deletes words from the retrieved sentences making our model more robust for handling long input sentences. We apply the proposed model on the WikiEvent dataset and compare it with a few baselines. The experimental results show that our carefully-designed architecture does help generate better event text, and extensive analysis further uncovers the characteristics of the proposed task.
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Rani, Shama, and Jaiteg Singh. "Getting Bulk Data Through Google: An empirical study." Journal of Technology Management for Growing Economies 7, no. 2 (2016): 39–48. https://doi.org/10.15415/jtmge.2016.72006.
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To store the information in a database is one of the major tasks. The efficient storage of data is important for future use. Information retrieval is a method of gathering information related to input queries from the various sources or stored databases. To retrieve the information, a search engine plays an important role. A web search engine creates an index to match queries. The quality of information is improved with the help of search engine. For retrieving the information, a search engine comprises some modules such as query processor, a searching and matching function, document processor and page rank capability. This paper focuses on the retrieval of web documents against input queries and stores them in to database. A Google search API can be used to fetch the results. It analyses the data by processing through these modules and downloads the content available in different formats.
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Nwagwu, Honour Chika. "Visualising Inconsistency and Incompleteness in RDF Gene Expression Data using FCA." International Journal of Conceptual Structures and Smart Applications 2, no. 1 (2014): 68–82. http://dx.doi.org/10.4018/ijcssa.2014010105.
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The integration of data from different data sources can result to the existence of inconsistent or incomplete data (IID). IID can undermine the validity of information retrieved from an integrated dataset. There is therefore a need to identify these anomalies. This work presents SPARQL queries that retrieve from an EMAGE dataset, information which are inconsistent or incomplete. Also, it will be shown how Formal Concept Analysis (FCA) tools notably FcaBedrock and Concept Explorer can be applied to identify and visualise IID existing in these retrieved information. Although, instances of IID can exist in most data formats, the investigation is focused on RDF dataset.
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Grzegorski, M., M. Wenig, U. Platt, P. Stammes, N. Fournier, and T. Wagner. "The Heidelberg iterative cloud retrieval utilities (HICRU) and its application to GOME data." Atmospheric Chemistry and Physics Discussions 6, no. 2 (2006): 1637–78. http://dx.doi.org/10.5194/acpd-6-1637-2006.
Full textAbstract:
Abstract. Information about clouds, in particular the accurate identification of cloud free pixels, is crucial for the retrieval of tropospheric vertical column densities from space. The Heidelberg Iterative Cloud Retrieval Utilities (HICRU) retrieve effective cloud fraction using spectra of two instruments designed for trace gas retrievals from space: The Global Ozone Monitoring Experiment (GOME) on the European Remote Sensing Satellite (ERS-2) and the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) on ENVISAT. HICRU applies the widely used threshold method to the so-called Polarization Monitoring Devices (PMDs) with higher spatial resolution compared to the channels used for trace gas retrievals. Cloud retrieval and in particular the identification of cloud free pixels is improved by HICRU through a sophisticated, iterative retrieval of the thresholds which takes their dependency on different instrumental and geometrical parameters into account. The lower thresholds, which represent the surface albedo and strongly affect the results of the algorithm, are retrieved accurately through a four stage classification scheme using image sequence analysis. The design and the results of the algorithm applied to GOME data are described and compared to several other cloud algorithms for GOME. The differences to other cloud algorithms are discussed with respect to the particular characteristics of the algorithms.
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Grzegorski, M., M. Wenig, U. Platt, P. Stammes, N. Fournier, and T. Wagner. "The Heidelberg iterative cloud retrieval utilities (HICRU) and its application to GOME data." Atmospheric Chemistry and Physics 6, no. 12 (2006): 4461–76. http://dx.doi.org/10.5194/acp-6-4461-2006.
Full textAbstract:
Abstract. Information about clouds, in particular the accurate identification of cloud free pixels, is crucial for the retrieval of tropospheric vertical column densities from space. The Heidelberg Iterative Cloud Retrieval Utilities (HICRU) retrieve effective cloud fraction using spectra of two instruments designed for trace gas retrievals from space: The Global Ozone Monitoring Experiment (GOME) on the European Remote Sensing Satellite (ERS-2) and the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) on ENVISAT. HICRU applies the widely used threshold method to the so-called Polarization Monitoring Devices (PMDs) with higher spatial resolution compared to the channels used for trace gas retrievals. Cloud retrieval and in particular the identification of cloud free pixels is improved by HICRU through a sophisticated, iterative retrieval of the thresholds which takes their dependency on different instrumental and geometrical parameters into account. The lower thresholds, which represent the surface albedo and strongly affect the results of the algorithm, are retrieved accurately through a four stage classification scheme using image sequence analysis. The design and the results of the algorithm applied to GOME data are described and compared to several other cloud algorithms for GOME. The differences to other cloud algorithms are discussed with respect to the particular characteristics of the algorithms.
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Abdulla P., Shaik, and Abdul Razak T. "Retrieval-Based Inception V3-Net Algorithm and Invariant Data Classification using Enhanced Deep Belief Networks for Content-Based Image Retrieval." Scientific Temper 15, spl-1 (2025): 415–24. https://doi.org/10.58414/scientifictemper.2024.15.spl.48.
Full textAbstract:
In the present scenario, Content-Based Image Retrieval (CBIR) performs a constantly changing function that makes use gain knowledge from images. Moreover, it is also the dynamic sector of research and was recently rewarded due to the drastic increase in the performance of digital images. To retrieve images from the massive dataset, experts utilize Content Based Image Retrieval. This approach automatically indexes and retrieves images depending upon the contents of the image, and the developing techniques for mining images are based on the CBIR systems. Based on the visual characteristics of the input image, object pattern, texture, color, shape, layout, and position classifications are applied, and indexing is carried out. When issues arise during feature extraction, deep learning approaches help to resolve them. A method called RIV3-NET, which stands for Retrieval-Based Inception V3, was used to classify the features. Classifying image invariant data using Enhanced Deep Belief Networks (EDBN) is necessary to decrease noise and improve displacement with smoothness. The simulation outcomes demonstrate the improved picture retrieval and parametric analysis.
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Mary, A., M. Dumont, J. P. Dedieu, et al. "Retrieval of snow Specific Surface Area (SSA) from MODIS data in mountainous regions." Cryosphere Discussions 6, no. 3 (2012): 1915–61. http://dx.doi.org/10.5194/tcd-6-1915-2012.
Full textAbstract:
Abstract. This study describes a method to retrieve snow specific surface area (SSA) from satellite radiance reasurements in mountainous terrain. It aims at comparing different retrieval methods and at addressing topographic corrections of reflectance, namely slope and aspect of terrain and multiple reflections on neighbouring slopes. We use an iterative algorithm to compute reflectance from radiance of the MODerate resolution Imaging Spectrometer (MODIS) with a comprehensive correction of local illumination with regards to topography. The retrieved SSA is compared to the results of the snowpack model Crocus, fed by driving data from the SAFRAN meteorological analysis, over a large domain in the French Alps. We compared SSA retrievals with and without topographic or anisotropy correction, and with a spherical or non-spherical snow reflectance model. The topographic correction enables SSA to be retrieved in better agreement with those from SAFRAN-Crocus. The root mean square deviation is 10.0 m2 kg−1 and the bias is −0.6 m2 kg−1, over 3829 pixels representing seven different dates and snow conditions. The standard deviation of MODIS retrieved data, larger than the one of SAFRAN-Crocus estimates, is responsible for half this RMSD. It is due to the topographic classes used by SAFRAN-Crocus. In addition, MODIS retrieved data show SSA gradients with elevation and solar exposition, physically consistent and in good agreement with SAFRAN-Crocus.