766 resultados para Data Mining, Big Data, Consumi energetici, Weka Data Cleaning


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Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

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We analyzed more than 200 OSIRIS NAC images with a pixel scale of 0.9-2.4 m/pixel of comet 67P/Churyumov-Gerasimenko (67P) that have been acquired from onboard the Rosetta spacecraft in August and September 2014 using stereo-photogrammetric methods (SPG). We derived improved spacecraft position and pointing data for the OSIRIS images and a high-resolution shape model that consists of about 16 million facets (2 m horizontal sampling) and a typical vertical accuracy at the decimeter scale. From this model, we derive a volume for the northern hemisphere of 9.35 km(3) +/- 0.1 km(3). With the assumption of a homogeneous density distribution and taking into account the current uncertainty of the position of the comet's center-of-mass, we extrapolated this value to an overall volume of 18.7 km(3) +/- 1.2 km(3), and, with a current best estimate of 1.0 X 10(13) kg for the mass, we derive a bulk density of 535 kg/m(3) +/- 35 kg/m(3). Furthermore, we used SPG methods to analyze the rotational elements of 67P. The rotational period for August and September 2014 was determined to be 12.4041 +/- 0.0004 h. For the orientation of the rotational axis (z-axis of the body-fixed reference frame) we derived a precession model with a half-cone angle of 0.14 degrees, a cone center position at 69.54 degrees/64.11 degrees (RA/Dec J2000 equatorial coordinates), and a precession period of 10.7 days. For the definition of zero longitude (x-axis orientation), we finally selected the boulder-like Cheops feature on the big lobe of 67P and fixed its spherical coordinates to 142.35 degrees right-hand-rule eastern longitude and -0.28 degrees latitude. This completes the definition of the new Cheops reference frame for 67P. Finally, we defined cartographic mapping standards for common use and combined analyses of scientific results that have been obtained not only within the OSIRIS team, but also within other groups of the Rosetta mission.

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Sediments of Lake Donggi Cona on the northeastern Tibetan Plateau were studied to infer changes in the lacustrine depositional environment, related to climatic and non-climatic changes during the last 19 kyr. The lake today fills a 30 X 8 km big and 95 m deep tectonic basin, associated with the Kunlun Fault. The study was conducted on a sediment-core transect through the lake basin, in order to gain a complete picture of spatiotemporal environmental change. The recovered sediments are partly finely laminated and are composed of calcareous muds with variable amounts of carbonate micrite, organic matter, detrital silt and clay. On the basis of sedimentological, geochemical, and mineralogical data up to five lithological units (LU) can be distinguished that document distinct stages in the development of the lake system. The onset of the lowermost LU with lacustrine muds above basal sands indicates that lake level was at least 39 m below the present level and started to rise after 19 ka, possibly in response to regional deglaciation. At this time, the lacustrine environment was characterized by detrital sediment influx and the deposition of siliciclastic sediment. In two sediment cores, upward grain-size coarsening documents a lake-level fall after 13 cal ka BP, possibly associated with the late-glacial Younger Dryas stadial. From 11.5 to 4.3 cal ka BP, grainsize fining in sediment cores from the profundal coring sites and the onset of lacustrine deposition at a litoral core site (2m water depth) in a recent marginal bay of Donggi Cona document lake-level rise during the early tomid-Holocene to at least modern level. In addition, high biological productivity and pronounced precipitation of carbonate micrites are consistent with warm and moist climate conditions related to an enhanced influence of summer monsoon. At 4.3 cal ka BP the lake system shifted from an aragonite- to a calcite-dominated system, indicating a change towards a fully open hydrological lake system. The younger clay-rich sediments are moreover non-laminated and lack any diagenetic sulphides, pointing to fully ventilated conditions, and the prevailing absence of lake stratification. This turning point in lake history could imply either a threshold response to insolation-forced climate cooling or a response to a non-climatic trigger, such as an erosional event or a tectonic pulse that induced a strong earthquake, which is difficult to decide from our data base.

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This paper presents the results of a Secchi depth data mining study for the North Sea - Baltic Sea region. 40,829 measurements of Secchi depth were compiled from the area as a result of this study. 4.3% of the observations were found in the international data centers [ICES Oceanographic Data Center in Denmark and the World Ocean Data Center A (WDC-A) in the USA], while 95.7% of the data was provided by individuals and ocean research institutions from the surrounding North Sea and Baltic Sea countries. Inquiries made at the World Ocean Data Center B (WDC-B) in Russia suggested that there could be significant additional holdings in that archive but, unfortunately, no data could be made available. The earliest Secchi depth measurement retrieved in this study dates back to 1902 for the Baltic Sea, while the bulk of the measurements were gathered after 1970. The spatial distribution of Secchi depth measurements in the North Sea is very uneven with surprisingly large sampling gaps in the Western North Sea. Quarterly and annual Secchi depth maps with a 0.5° x 0.5° spatial resolution are provided for the transition area between the North Sea and the Baltic Sea (4°E-16°E, 53°N-60°N).

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The planktonic haptophyte Phaeocystis has been suggested to play a fundamental role in the global biogeochemical cycling of carbon and sulphur, but little is known about its global biomass distribution. We have collected global microscopy data of the genus Phaeocystis and converted abundance data to carbon biomass using species-specific carbon conversion factors. Microscopic counts of single-celled and colonial Phaeocystis were obtained both through the mining of online databases and by accepting direct submissions (both published and unpublished) from Phaeocystis specialists. We recorded abundance data from a total of 1595 depth-resolved stations sampled between 1955-2009. The quality-controlled dataset includes 5057 counts of individual Phaeocystis cells resolved to species level and information regarding life-stages from 3526 samples. 83% of stations were located in the Northern Hemisphere while 17% were located in the Southern Hemisphere. Most data were located in the latitude range of 50-70° N. While the seasonal distribution of Northern Hemisphere data was well-balanced, Southern Hemisphere data was biased towards summer months. Mean species- and form-specific cell diameters were determined from previously published studies. Cell diameters were used to calculate the cellular biovolume of Phaeocystis cells, assuming spherical geometry. Cell biomass was calculated using a carbon conversion factor for Prymnesiophytes (Menden-Deuer and Lessard, 2000). For colonies, the number of cells per colony was derived from the colony volume. Cell numbers were then converted to carbon concentrations. An estimation of colonial mucus carbon was included a posteriori, assuming a mean colony size for each species. Carbon content per cell ranged from 9 pg (single-celled Phaeocystis antarctica) to 29 pg (colonial Phaeocystis globosa). Non-zero Phaeocystis cell biomasses (without mucus carbon) range from 2.9 - 10?5 µg l-1 to 5.4 - 103 µg l-1, with a mean of 45.7 µg l-1 and a median of 3.0 µg l-1. Highest biomasses occur in the Southern Ocean below 70° S (up to 783.9 µg l-1), and in the North Atlantic around 50° N (up to 5.4 - 103 µg l-1).

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This paper describes seagrass species and percentage cover point-based field data sets derived from georeferenced photo transects. Annually or biannually over a ten year period (2004-2015) data sets were collected using 30-50 transects, 500-800 m in length distributed across a 142 km**2 shallow, clear water seagrass habitat, the Eastern Banks, Moreton Bay, Australia. Each of the eight data sets include seagrass property information derived from approximately 3000 georeferenced, downward looking photographs captured at 2-4 m intervals along the transects. Photographs were manually interpreted to estimate seagrass species composition and percentage cover (Coral Point Count excel; CPCe). Understanding seagrass biology, ecology and dynamics for scientific and management purposes requires point-based data on species composition and cover. This data set, and the methods used to derive it are a globally unique example for seagrass ecological applications. It provides the basis for multiple further studies at this site, regional to global comparative studies, and, for the design of similar monitoring programs elsewhere.