Artificial Intelligence
The research were incorporated an extended cohort monitoring campaign, validation of an existing exposure model and development of a predictive model for COPD exacerbations evaluated against historical electronic health records.
A miniature personal sensor unit were manufactured for the study from a prototype developed at the University of Cambridge. The units monitored GPS position, temperature, humidity, CO, NO, NO2, O3, PM10 and PM2.5.
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Evaluation data of the experiments for the paper "Comparison of Anomaly Detectors: Context Matters".
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This is an ontology used for the identification of common algae involved in harmful algal bloom events. This ontology is used as a guide to determine the algae species to be identified by the expert system.
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In recent years, drones have been used to transport payloads to remote and difficult access areas. A payload delivery with assisted relay system of two unmanned aerial vehicles (UAVs) is implemented. A local and web remote monitoring and control from a ground control station (GCS) for a pre-planned autonomous trajectory flight is developed in this work.
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There is an industry gap for publicly available electric utility infrastructure imagery. The Electric Power Research Institute (EPRI) is filling this gap to support public and private sector AI innovation. This dataset consists of ~30,000 images of overhead Distribution infrastructure. These images have been anonymized, reviewed, and .exif image-data scrubbed. EPRI intends to label these data to support its own research activities. As these labels are created, EPRI will periodically update this dataset with those data.
Update: July 2022
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To study the driver's behavior in real traffic situations, we conducted experiments using an instrumented vehicle, which comprises:
(i) a camera, installed above the vehicle's side window and oriented toward the driver, and (ii) a Mobile Digital Video Recorder (MDVR).
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The "eternal war in cache" has reached browsers, with multiple cache-based side-channel attacks and countermeasures being suggested. A common approach for countermeasures is to disable or restrict JavaScript features deemed essential for carrying out attacks.
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To train the machine learning model, a dataset was generated containing data for «Budennovskoye» field, part of which is shown in title figure. (AR and SP are given for 90 centimeter intervals, for which, in turn, the actual values K_fpo. obtained by pumping out (pump out) was determined. As a result, the input variable set consisted of 19 values, including the rock code (AR, SP). The target column isK_f_pump_out .
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