Готові списки джерел за темами / CLOUDLET CLASSIFICATION

Добірка наукової літератури з теми "CLOUDLET CLASSIFICATION"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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Статті в журналах з теми "CLOUDLET CLASSIFICATION"

Ashima, Ashima, and Vikramjit Singh. "A NOVEL APPROACH OF JOB ALLOCATION USING MULTIPLE PARAMETERS IN IN CLOUD ENVIRONMENT." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 17, no. 1 (January 16, 2018): 7103–10. http://dx.doi.org/10.24297/ijct.v17i1.7004.

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Cloud computing is Internet ("cloud") based development and use of computer technology ("computing"). It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. This research deals with the balancing of work load in cloud environment. Load balancing is one of the essential factors to enhance the working performance of the cloud service provider. Grid computing utilizes the distributed heterogeneous resources in order to support complicated computing problems. Grid can be classified into two types: computing grid and data grid. We propose an improved load balancing algorithm for job scheduling in the Grid environment. Hence, in this research work, a multi-objective load balancing algorithm has been proposed to avoid deadlocks and to provide proper utilization of all the virtual machines (VMs) while processing the requests received from the users by VM classification. The capacity of virtual machine is computed based on multiple parameters like MIPS, RAM and bandwidth. Heterogeneous virtual machines of different MIPS and processing power in multiple data centers with different hosts have been created in cloud simulator. The VMâ€™s are divided into 2 clusters using K-Means clustering mechanism in terms of processor MIPS, memory and bandwidth. The cloudlets are divided into two categories like High QOS and Low QOS based on the instruction size. The cloudlet whose task size is greater than the threshold value will enter into High QOS and cloudlet whose task size is lesser than the threshold value will enter into Low QOS. Submit the job of the user to the datacenter broker. The job of the user is submitted to the broker and it will first find the suitable VM according to the requirements of the cloudlet and will match VM depending upon its availability. Multiple parameters have been evaluated like waiting time, turnaround time, execution time and processing cost. This modified algorithm has an edge over the original approach in which each cloudlet build their own individual result set and it is later on built into a complete solution.

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Kalesse-Los, Heike, Willi Schimmel, Edward Luke, and Patric Seifert. "Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network." Atmospheric Measurement Techniques 15, no. 2 (January 20, 2022): 279–95. http://dx.doi.org/10.5194/amt-15-279-2022.

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Abstract. Detection of liquid-containing cloud layers in thick mixed-phase clouds or multi-layer cloud situations from ground-based remote-sensing instruments still poses observational challenges, yet improvements are crucial since the existence of multi-layer liquid layers in mixed-phase cloud situations influences cloud radiative effects, cloud lifetime, and precipitation formation processes. Hydrometeor target classifications such as from Cloudnet that require a lidar signal for the classification of liquid are limited to the maximum height of lidar signal penetration and thus often lead to underestimations of liquid-containing cloud layers. Here we evaluate the Cloudnet liquid detection against the approach of Luke et al. (2010) which extracts morphological features in cloud-penetrating cloud radar Doppler spectra measurements in an artificial neural network (ANN) approach to classify liquid beyond full lidar signal attenuation based on the simulation of the two lidar parameters particle backscatter coefficient and particle depolarization ratio. We show that the ANN of Luke et al. (2010) which was trained under Arctic conditions can successfully be applied to observations at the mid-latitudes obtained during the 7-week-long ACCEPT field experiment in Cabauw, the Netherlands, in 2014. In a sensitivity study covering the whole duration of the ACCEPT campaign, different liquid-detection thresholds for ANN-predicted lidar variables are applied and evaluated against the Cloudnet target classification. Independent validation of the liquid mask from the standard Cloudnet target classification against the ANN-based technique is realized by comparisons to observations of microwave radiometer liquid-water path, ceilometer liquid-layer base altitude, and radiosonde relative humidity. In addition, a case-study comparison against the cloud feature mask detected by the space-borne lidar aboard the CALIPSO satellite is presented. Three conclusions were drawn from the investigation. First, it was found that the threshold selection criteria of liquid-related lidar backscatter and depolarization alone control the liquid detection considerably. Second, all threshold values used in the ANN framework were found to outperform the Cloudnet target classification for deep or multi-layer cloud situations where the lidar signal is fully attenuated within low liquid layers and the cloud radar is able to detect the microphysical fingerprint of liquid in higher cloud layers. Third, if lidar data are available, Cloudnet is at least as good as the ANN. The times when Cloudnet outperforms the ANN in liquid detections are often associated with situations where cloud dynamics smear the imprint of cloud microphysics on the radar Doppler spectra.

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Kalesse-Los, Heike, Anton Kötsche, Andreas Foth, Johannes Röttenbacher, Teresa Vogl, and Jonas Witthuhn. "The Virga-Sniffer – a new tool to identify precipitation evaporation using ground-based remote-sensing observations." Atmospheric Measurement Techniques 16, no. 6 (March 29, 2023): 1683–704. http://dx.doi.org/10.5194/amt-16-1683-2023.

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Abstract. Continuous long-term ground-based remote-sensing observations combined with vertically pointing cloud radar and ceilometer measurements are well suited for identifying precipitation evaporation fall streaks (so-called virga). Here we introduce the functionality and workflow of a new open-source tool, the Virga-Sniffer, which was developed within the framework of RV Meteor observations during the ElUcidating the RolE of Cloud–Circulation Coupling in ClimAte (EUREC4A) field experiment in January–February 2020 in the tropical western Atlantic. The Virga-Sniffer Python package is highly modular and configurable and can be applied to multilayer cloud situations. In the simplest approach, it detects virga from time–height fields of cloud radar reflectivity and time series of ceilometer cloud base height. In addition, optional parameters like lifting condensation level, a surface rain flag, and time–height fields of cloud radar mean Doppler velocity can be added to refine virga event identifications. The netCDF-output files consist of Boolean flags of virga and cloud detection, as well as base and top heights and depth for the detected clouds and virga. The sensitivity of the Virga-Sniffer results to different settings is explored (in the Appendix). The performance of the Virga-Sniffer was assessed by comparing its results to the CloudNet target classification resulting from using the CloudNet processing chain. A total of 86 % of pixels identified as virga correspond to CloudNet target classifications of precipitation. The remaining 14 % of virga pixels correspond to CloudNet target classifications of aerosols and insects (about 10 %), cloud droplets (about 2 %), or clear sky (2 %). Some discrepancies of the virga identification and the CloudNet target classification can be attributed to temporal smoothing that was applied. Additionally, it was found that CloudNet mostly classified aerosols and insects at virga edges, which points to a misclassification caused by CloudNet internal thresholds. For the RV Meteor observations in the downstream winter trades during EUREC4A, about 42 % of all detected clouds with bases below the trade inversion were found to produce precipitation that fully evaporates before reaching the ground. A proportion of 56 % of the detected virga originated from trade wind cumuli. Virga with depths less than 0.2 km most frequently occurred from shallow clouds with depths less than 0.5 km, while virga depths larger than 1 km were mainly associated with clouds of larger depths, ranging between 0.5 and 1 km. The presented results substantiate the importance of complete low-level precipitation evaporation in the downstream winter trades. Possible applications of the Virga-Sniffer within the framework of EUREC4A include detailed studies of precipitation evaporation with a focus on cold pools or cloud organization or distinguishing moist processes based on water vapor isotopic observations. However, we envision extended use of the Virga-Sniffer for other cloud regimes or scientific foci as well.

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Pîrloagă, Răzvan, Dragoş Ene, Mihai Boldeanu, Bogdan Antonescu, Ewan J. O’Connor, and Sabina Ştefan. "Ground-Based Measurements of Cloud Properties at the Bucharest–Măgurele Cloudnet Station: First Results." Atmosphere 13, no. 9 (September 6, 2022): 1445. http://dx.doi.org/10.3390/atmos13091445.

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Data collected over a period of 18 months (December 2019–May 2021) at the Bucharest–Măgurele Cloudnet station were analysed for the first time to determine the macrophysical and microphysical cloud properties over this site. A total number of 1,327,680 vertical profiles containing the target classification based on the Cloudnet algorithm were analysed, of which 1,077,858 profiles contained hydrometeors. The highest number of profiles with hydrometeors (>60%) was recorded in December 2020, with hydrometeors being observed mainly below 5 km. Above 5 km, the frequency of occurrence of hydrometeors was less than <20%. Based on the initial Cloudnet target classification, a cloud classification scheme was implemented. Clouds were more frequently observed during winter compared with other seasons (45% of all profiles). Ice clouds were the most frequent type of cloud (468,463 profiles) during the study period, followed by mixed phases (220,280 profiles) and mixed phased precipitable clouds (164,868 profiles). The geometrical thickness varied from a median value of 244 m for liquid clouds during summer to 3362 m for mix phased precipitable clouds during spring.

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Kulie, Mark S., Lisa Milani, Norman B. Wood, Samantha A. Tushaus, Ralf Bennartz, and Tristan S. L’Ecuyer. "A Shallow Cumuliform Snowfall Census Using Spaceborne Radar." Journal of Hydrometeorology 17, no. 4 (April 1, 2016): 1261–79. http://dx.doi.org/10.1175/jhm-d-15-0123.1.

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Abstract The first observationally based near-global shallow cumuliform snowfall census is undertaken using multiyear CloudSat Cloud Profiling Radar observations. CloudSat snowfall observations and snowfall rate estimates from the CloudSat 2C-Snow Water Content and Snowfall Rate (2C-SNOW-PROFILE) product are partitioned between shallow cumuliform and nimbostratus cloud structures by utilizing coincident cloud category classifications from the CloudSat 2B-Cloud Scenario Classification (2B-CLDCLASS) product. Shallow cumuliform (nimbostratus) snowfall events comprise about 36% (59%) of snowfall events in the CloudSat snowfall dataset. The remaining 5% of snowfall events are distributed between other categories. Distinct oceanic versus continental trends exist between the two major snowfall categories, as shallow cumuliform snow-producing clouds occur predominantly over the oceans. Regional differences are also noted in the partitioned dataset, with over-ocean regions near Greenland, the far North Atlantic Ocean, the Barents Sea, the western Pacific Ocean, the southern Bering Sea, and the Southern Hemispheric pan-oceanic region containing distinct shallow snowfall occurrence maxima exceeding 60%. Certain Northern Hemispheric continental regions also experience frequent shallow cumuliform snowfall events (e.g., inland Russia), as well as some mountainous regions. CloudSat-generated snowfall rates are also partitioned between the two major snowfall categories to illustrate the importance of shallow snow-producing cloud structures to the average annual snowfall. While shallow cumuliform snowfall produces over 50% of the annual estimated surface snowfall flux regionally, about 18% (82%) of global snowfall is attributed to shallow (nimbostratus) snowfall. This foundational spaceborne snowfall study will be utilized for follow-on evaluative studies with independent model, reanalysis, and ground-based observational datasets to characterize respective dataset biases and to better quantify CloudSat snowfall detection and quantitative snowfall estimate uncertainties.

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Zhang, Jinglin, Pu Liu, Feng Zhang, and Qianqian Song. "CloudNet: Ground‐Based Cloud Classification With Deep Convolutional Neural Network." Geophysical Research Letters 45, no. 16 (August 28, 2018): 8665–72. http://dx.doi.org/10.1029/2018gl077787.

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Liu, Cheng-Chien, Yu-Cheng Zhang, Pei-Yin Chen, Chien-Chih Lai, Yi-Hsin Chen, Ji-Hong Cheng, and Ming-Hsun Ko. "Clouds Classification from Sentinel-2 Imagery with Deep Residual Learning and Semantic Image Segmentation." Remote Sensing 11, no. 2 (January 10, 2019): 119. http://dx.doi.org/10.3390/rs11020119.

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Detecting changes in land use and land cover (LULC) from space has long been the main goal of satellite remote sensing (RS), yet the existing and available algorithms for cloud classification are not reliable enough to attain this goal in an automated fashion. Clouds are very strong optical signals that dominate the results of change detection if they are not removed completely from imagery. As various architectures of deep learning (DL) have been proposed and advanced quickly, their potential in perceptual tasks has been widely accepted and successfully applied to many fields. A comprehensive survey of DL in RS has been reviewed, and the RS community has been suggested to be leading researchers in DL. Based on deep residual learning, semantic image segmentation, and the concept of atrous convolution, we propose a new DL architecture, named CloudNet, with an enhanced capability of feature extraction for classifying cloud and haze from Sentinel-2 imagery, with the intention of supporting automatic change detection in LULC. To ensure the quality of the training dataset, scene classification maps of Taiwan processed by Sen2cor were visually examined and edited, resulting in a total of 12,769 sub-images with a standard size of 224 × 224 pixels, cut from the Sen2cor-corrected images and compiled in a trainset. The data augmentation technique enabled CloudNet to have stable cirrus identification capability without extensive training data. Compared to the traditional method and other DL methods, CloudNet had higher accuracy in cloud and haze classification, as well as better performance in cirrus cloud recognition. CloudNet will be incorporated into the Open Access Satellite Image Service to facilitate change detection by using Sentinel-2 imagery on a regular and automatic basis.

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Behrangi, Ali, Terry Kubar, and Bjorn Lambrigtsen. "Phenomenological Description of Tropical Clouds Using CloudSat Cloud Classification." Monthly Weather Review 140, no. 10 (October 1, 2012): 3235–49. http://dx.doi.org/10.1175/mwr-d-11-00247.1.

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Abstract Two years of tropical oceanic cloud observations are analyzed using the operational CloudSat cloud classification product and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar. Relationships are examined between cloud types, sea surface temperature (SST), and location during the CloudSat early morning and afternoon overpasses. Based on CloudSat and combined lidar–radar products, the maximum and minimum cloud fractions occur at SSTs near 303 and 299 K, respectively, corresponding to deep convective/detrained cloud populations and the transition from shallow to deep convection. For SSTs below approximately 301 K, low clouds (stratiform and stratocumulus) are dominant (cloud fraction between 0.15 and 0.37) whereas high clouds are dominant for SSTs greater than about 301 K (cloud fraction between 0.18 and 0.28). Consistent with previous studies, most tropical low clouds are associated with lower SSTs, with a strong inverse linear relationship between low cloud frequency and SST. For all cloud types except nimbostratus, stratus, and stratocumulus, a sharp increase in frequency of occurrence is observed for SSTs between 299 and 300.5 K, deduced as the onset of deeper convection. Peak fractions of high, deep convective, altostratus, and altocumulus clouds occur at SSTs close to 303 K, while cumulus clouds, which have lower tops, show a smooth cloud fractional peak about 2° cooler. Deep convective and other high cloud types decrease sharply above SSTs of 303 K, in accordance with previous work suggesting a narrow window of tropical deep convection. Finally, significant cloud frequency differences exist between CloudSat early morning and afternoon overpasses, suggesting a diurnal cycle of some cloud types, particularly stratocumulus, high, and deep convective clouds.

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Schimmel, Willi, Heike Kalesse-Los, Maximilian Maahn, Teresa Vogl, Andreas Foth, Pablo Saavedra Garfias, and Patric Seifert. "Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks." Atmospheric Measurement Techniques 15, no. 18 (September 21, 2022): 5343–66. http://dx.doi.org/10.5194/amt-15-5343-2022.

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Abstract. In mixed-phase clouds, the variable mass ratio between liquid water and ice as well as the spatial distribution within the cloud plays an important role in cloud lifetime, precipitation processes, and the radiation budget. Data sets of vertically pointing Doppler cloud radars and lidars provide insights into cloud properties at high temporal and spatial resolution. Cloud radars are able to penetrate multiple liquid layers and can potentially be used to expand the identification of cloud phase to the entire vertical column beyond the lidar signal attenuation height, by exploiting morphological features in cloud radar Doppler spectra that relate to the existence of supercooled liquid. We present VOODOO (reVealing supercOOled liquiD beyOnd lidar attenuatiOn), a retrieval based on deep convolutional neural networks (CNNs) mapping radar Doppler spectra to the probability of the presence of cloud droplets (CD). The training of the CNN was realized using the Cloudnet processing suite as supervisor. Once trained, VOODOO yields the probability for CD directly at Cloudnet grid resolution. Long-term predictions of 18 months in total from two mid-latitudinal locations, i.e., Punta Arenas, Chile (53.1∘ S, 70.9∘ W), in the Southern Hemisphere and Leipzig, Germany (51.3∘ N, 12.4∘ E), in the Northern Hemisphere, are evaluated. Temporal and spatial agreement in cloud-droplet-bearing pixels is found for the Cloudnet classification to the VOODOO prediction. Two suitable case studies were selected, where stratiform, multi-layer, and deep mixed-phase clouds were observed. Performance analysis of VOODOO via classification-evaluating metrics reveals precision > 0.7, recall ≈ 0.7, and accuracy ≈ 0.8. Additionally, independent measurements of liquid water path (LWP) retrieved by a collocated microwave radiometer (MWR) are correlated to the adiabatic LWP, which is estimated using the temporal and spatial locations of cloud droplets from VOODOO and Cloudnet in connection with a cloud parcel model. This comparison resulted in stronger correlation for VOODOO (≈ 0.45) compared to Cloudnet (≈ 0.22) and indicates the availability of VOODOO to identify CD beyond lidar attenuation. Furthermore, the long-term statistics for 18 months of observations are presented, analyzing the performance as a function of MWR–LWP and confirming VOODOO's ability to identify cloud droplets reliably for clouds with LWP > 100 g m−2. The influence of turbulence on the predictive performance of VOODOO was also analyzed and found to be minor. A synergy of the novel approach VOODOO and Cloudnet would complement each other perfectly and is planned to be incorporated into the Cloudnet algorithm chain in the near future.

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Casey, S. P. F., E. J. Fetzer, and B. H. Kahn. "Revised identification of tropical oceanic cumulus congestus as viewed by CloudSat." Atmospheric Chemistry and Physics Discussions 11, no. 5 (May 17, 2011): 14883–902. http://dx.doi.org/10.5194/acpd-11-14883-2011.

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Abstract. Congestus cloud convective features are examined in one year of tropical oceanic cloud observations from the CloudSat/CALIPSO instruments. Two types of convective clouds (cumulus and deep convective, based on classification profiles from radar), and associated differences in radar reflectivity and radar/lidar cloud-top height are considered. Congestus convective features are defined as contiguous convective clouds with heights between 3 and 9 km. A majority of congestus convective features satisfy one of three criteria used in previous studies: (1) CloudSat and CALIPSO cloud-top heights less than 1 km apart; (2) CloudSat 0 dBZ echo-top height less than 1 km from CloudSat cloud-top height, and (3) CloudSat 10 dBZ echo-top height less than 2 km from CloudSat cloud-top height. However, less than half of congestus convective features satisfy all three of these requirements. This implies that previous methods used to identify congestus clouds may be biased towards more vigorous convection, missing more than half of observed congestus and significantly misrepresenting the deduced relationship between congestus clouds and their surroundings.

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Більше джерел

Дисертації з теми "CLOUDLET CLASSIFICATION"

Nam, Christine C. W., and Johannes Quaas. "Evaluation of clouds and precipitation in the ECHAM5 general circulation model using CALIPSO and CloudSat satellite data." American Meteorological Society, 2012. https://ul.qucosa.de/id/qucosa%3A13468.

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Observations from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat satellites are used to evaluate clouds and precipitation in the ECHAM5 general circulation model. Active lidar and radar instruments on board CALIPSO and CloudSat allow the vertical distribution of clouds and their optical properties to be studied on a global scale. To evaluate the clouds modeled by ECHAM5 with CALIPSO and CloudSat, the lidar and radar satellite simulators of the Cloud Feedback Model Intercomparison Project’s Observation Simulator Package are used. Comparison of ECHAM5 with CALIPSO and CloudSat found large-scale features resolved by the model, such as the Hadley circulation, are captured well. The lidar simulator demonstrated ECHAM5 overestimates the amount of high-level clouds, particularly optically thin clouds. High-altitude clouds in ECHAM5 consistently produced greater lidar scattering ratios compared with CALIPSO. Consequently, the lidar signal in ECHAM5 frequently attenuated high in the atmosphere. The large scattering ratios were due to an underestimation of effective ice crystal radii in ECHAM5. Doubling the effective ice crystal radii improved the scattering ratios and frequency of attenuation. Additionally, doubling the effective ice crystal radii improved the detection of ECHAM5’s highest-level clouds by the radar simulator, in better agreement with CloudSat. ECHAM5 was also shown to significantly underestimate midlevel clouds and (sub)tropical low-level clouds. The low-level clouds produced were consistently perceived by the lidar simulator as too optically thick. The radar simulator demonstrated ECHAM5 overestimates the frequency of precipitation, yet underestimates its intensity compared with CloudSat observations. These findings imply compensating mechanisms inECHAM5 balance out the radiative imbalance caused by incorrect optical properties of clouds and consistently large hydrometeors in the atmosphere.

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Nam, Christine C. W., Johannes Quaas, Roel Neggers, Drian Colombe Siegenthaler-Le, and Francesco Isotta. "Evaluation of boundary layer cloud parameterizations in the ECHAM5 general circulation model using CALIPSO and CloudSat satellite data." American Geophysical Union (AGU), 2014. https://ul.qucosa.de/id/qucosa%3A13458.

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Three different boundary layer cloud models are incorporated into the ECHAM5 general circulation model (GCM) and compared to CloudSat and CALIPSO satellite observations. The first boundary layer model builds upon the standard Tiedtke (1989) parameterization for shallow convection with an adapted convective trigger; the second is a bulk parameterization of the effects of transient shallow cumulus clouds; and lastly the Dual Mass Flux (DMF) scheme adjusted to better represent shallow convection. The three schemes improved (Sub)Tropical oceanic low-level cloud cover, however, the fraction of low-level cloud cover remains underestimated compared to CALIPSO observations. The representation of precipitation was improved by all schemes as they reduced the frequency of light intensity events <0.01 mm d-1, which were found to dominate the radar reflectivity histograms as well as be the greatest source of differences between ECHAM5 and CloudSat radar reflectivity histograms. For both lidar and radar diagnostics, the differences amongst the schemes are smaller than the differences compared to observations. While the DMF approach remains experimental, as its top-of-atmosphere radiative balance has not been retuned, it shows the most promise in producing nonprecipitating boundary layer clouds. With its internally consistent boundary layer scheme that uses the same bimodal joint distribution with a diffusive and an updraft component for clouds and turbulent transport, the ECHAM5_DMF produces the most realistic boundary layer depth as indicated by the cloud field. In addition, it reduced the frequency of large-scale precipitation intensities of <0.01 mm d-1 the greatest.

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Simmel, Martin, Robert Reilein, Gudula Rünger, and Gerd Tetzlaff. "Parallele Strategien für ein spektrales Wolkenmodul in einem 3-dimensionalen Mesoskalenmodell." Wissenschaftliche Mitteilungen des Leipziger Instituts für Meteorologie ; 12 = Meteorologische Arbeiten aus Leipzig ; 4 (1999), S. 217-224, 1999. https://ul.qucosa.de/id/qucosa%3A15135.

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A spectral cloud model is developed for a 3-dimensional mesoscale model considering only the microphysical conversion processes of the warm cloud. Because of the expected computation requirements, which are strongly increased in relation to the bulk-parameterization, we develop concepts for the parallelization of the module, explain their applicability and present first results.
Für ein 3-dimensionales Mesoskalenmodell wird ein spektrales Wolkenmodul entwickelt, das zunächst nur die mikrophysikalischen Umwandlungsprozesse der warmen Wolke berücksichtigt. Aufgrund des zu erwartenden, im Vergleich zur bulk-Parametrisierung stark erhöhten Rechenzeitbedarfs entwickeln wir Konzepte zur Parallelisierung des Moduls, erläutern deren Anwendbarkeit und stellen erste Ergebnisse vor.

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Частини книг з теми "CLOUDLET CLASSIFICATION"

Benabbes, S., and S. M. Hemam. "An Approach Based on (Tasks-VMs) Classification and MCDA for Dynamic Load Balancing in the CloudIoT." In Lecture Notes in Networks and Systems, 387–96. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37207-1_41.

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Nasrollahi, Nasrin. "Reducing False Rain in Satellite Precipitation Products Using Cloudsat Cloud Classification Maps and Modis Multi-spectral Images." In Springer Theses, 21–32. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12081-2_4.

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Тези доповідей конференцій з теми "CLOUDLET CLASSIFICATION"

Serra, Wendel, Warley Junior, Isaac Barros, Hugo Kuribayashi, and João Carmona. "Monitoring and Smart Decision Architecture for DRONE-FOG Integrated Environment." In Simpósio Brasileiro de Computação Ubíqua e Pervasiva. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/sbcup.2021.16008.

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Due to the limited computing resources of drones, it is difficult to handle computation-intensive tasks locally, hence, fog-based computation offloading has been widely adopted. The effectiveness of an offloading operation, however, is determined by its ability to infer where the execution of code/data represents less computational effort for the drone, so that, by deciding where to offload correctly, the device benefits. Thus, this paper proposes MonDroneFog, a novel fog-based architecture that supports image offloading, as well as monitoring and storing the performance metrics related to the drone, wireless network, and cloudlet. It takes advantage of the main machine-learning algorithms to provide offloading decisions with high levels of accuracy, F1, and G-mean. We evaluate the main classification algorithms under our database and the results show that Multi-Layer Perceptron (MLP) and Logistic Regression classifiers achieve 99.64% and 99.20% accuracy, respectively. Under these conditions, MonDrone-Fog works well in dense forests when weather conditions are favorable and can be useful as a support system for SAR missions by providing a shorter runtime for image operations.

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Piraisoody, Gajen, Changcheng Huang, Biswajit Nandy, and Nabil Seddigh. "Classification of applications in HTTP tunnels." In 2013 IEEE 2nd International Conference on Cloud Networking (CloudNet). IEEE, 2013. http://dx.doi.org/10.1109/cloudnet.2013.6710559.

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ElKenawy, Ahmed S., and Sherif G. Aly. "An Enhanced Cloud-Native Deep Learning Pipeline for Network Traffic Classification." In 2022 IEEE 11th International Conference on Cloud Networking (CloudNet). IEEE, 2022. http://dx.doi.org/10.1109/cloudnet55617.2022.9978774.

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Ding, Leah, Roberto Corizzo, Colin Bellinger, Nancy Ching, Spencer Login, Rodrigo Yepez-Lopez, Jie Gong, and Dong L. Wu. "Imbalanced Multi-layer Cloud Classification with Advanced Baseline Imager (ABI) and CloudSat/CALIPSO Data." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020783.

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Xu, Yi, Jie Hu, Zhiqiao Gao, and Jinpeng Chen. "UCL-AST: Active Self-Training with Uncertainty-Aware Clouded Logits for Few-Shot Text Classification." In 2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2022. http://dx.doi.org/10.1109/ictai56018.2022.00210.

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Cowan, Matthew, Joseph Lieberman, Jacob Cimbalista, and Bryan Schlake. "Electronic Freight Car Inspection Recording and Application of Internet-of-Things (IoT) and Machine-to-Machine (M2M) Frameworks." In 2018 Joint Rail Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/jrc2018-6192.

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Freight railroad classification yards have been compared to large-scale manufacturing plants, with inbound trains as the inputs and outbound trains as the outputs. Railcars often take up to 24 hours to be processed through a railyard due to the need for manual inbound inspection, car classification, manual outbound inspection, and other intermediate processes. Much of the inspection and repair process has historically been completed manually with handwritten documents. Until recently, car inspections were rarely documented unless repairs were required. Currently, when a defect is detected in the yard, the railcar inspector must complete a “bad order” form that is adhered to each side of the car. This process may take up to ten minutes per bad order. To reduce labor costs and improve efficiency, asset management technology and Internet-of-Things (IoT) frameworks can now be developed to reduce labor time needed to record bad orders, increase inspection visibility, and provide the opportunity to implement analytics and cognitive insights to optimize worker productivity and facilitate condition-based maintenance. The goal of this project is to develop a low-cost prototype electronic freight car inspection tracking system for small-scale (short line and regional) railroad companies. This system allows car inspectors to record mechanical inspection data using a ruggedized mobile platform (e.g. tablet or smartphone). This data may then be used to improve inspection quality and efficiency as well as reduce inspection redundancy. Data collection will involve two approaches. The first approach is the development of an Android-based mobile application to electronically record and store inspection data using a smartphone or rugged tablet. This automates the entire bad order form process by connecting to IBM’s Bluemix Cloudant NoSQL database. It allows for the information to be accessed by railroad mechanical managers or car owners, anywhere and at any time. The second approach is a web-based Machine-to-Machine (M2M) system using Bluetooth low energy (BLE) and beacon technology to store car inspection data on a secure website and/or a cloudant database. This approach introduces the freight car inspection process to the “physical web,” and it will offer numerous additional capabilities that are not possible with the current radio frequency identification device (RFID) system used for freight car tracking. By connecting railcars to the physical web, railcar specifications and inspection data can be updated in real-time and be made universally available. At the end of this paper, an evaluation and assessment is made of both the benefits and drawbacks of each of these approaches. The evaluation suggests that although some railroads may immediately benefit from these technological solutions, others may be better off with the current manual method until IoT and M2M become more universally accepted within the railroad industry. The primary value of this analysis is to provide a decision framework for railroads seeking to implement IoT systems in their freight car inspection practices. As an additional result, the software and IoT source code for the mobile app developed for this project will be open source to promote future collaboration within the industry.

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Xie, Minghui, Qi Zhang, Shengbo Chen, and Fengli Zha. "A lithological classification method from fully polarimetric SAR data using Cloude-Pottier decomposition and SVM." In Applied Optics and Photonics China (AOPC2015), edited by Haimei Gong, Nanjian Wu, Yang Ni, Weibiao Chen, and Jin Lu. SPIE, 2015. http://dx.doi.org/10.1117/12.2196856.

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Cao Fang, Hong Wen, and Wu Yirong. "An Improved Cloude-Pottier Decomposition Using H/α/SPAN and Complex Wishart Classifier for Polarimetric SAR Classification." In Proceedings of 2006 CIE International Conference on Radar. IEEE, 2006. http://dx.doi.org/10.1109/icr.2006.343203.

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