Datasets. This Data Use Agreement (“Agreement”) is made and entered into by and between the SOUTH ALAMO REGIONAL ALLIANCE FOR THE HOMELESS (“Covered Entity”), a 501(c)(3) non-profit organization in the State of Texas, and Insert Name of Entity (“Data Recipient”), a [insert description of legal status (public / private / profit / non-profit / company incorporated in what state, LLP or LLC or other entity incorporated or registered in what state)] on (Today’s Date) for the purpose of collecting and analyzing Homeless Management Information System (HMIS) data from XXXXX.
Datasets. To validate the proposed approach, we have consid- ered five different benchmarks, namely: Handwritten digits dataset (MNIST), XXXXX00, XxxxxXxxx, Animals with At- tributes 2 (AwA2), Street View House Numbers (SVHN). Figure 3 shows some image samples from these data sources. × MNIST [28] dataset consists of 70000 images with dimen- sion 28 28. The dataset has been split in 60000 and 10000 images for training and testing respectively. The dataset is a collection of greyscale images of handwritten numbers clas- sified among 10 classes. SVHN contains 73000 and 26000 real-life digit images for training and testing. × CIFAR10 [25] is a well known standard dataset for im- age recognition experimentation, it consists of 60000 im- ages from 10 classes of objects from different contexts. We maintain the dataset split in training and test suggested by the dataset authors: 50000 images in training set and 10000 images in test set. The images have dimension 32 32 and they are defined over three colour channels (RGB colour space). Model MNIST SVHN XXXXX00 XxxxxXXXX XxX0 Baseline CapsNet AA-Caps (Ours) 99.67% (100E) 99.34% (100E) 93.23% (100E) 92.13% (100E) 68.70% 71.60% 89.56% (50E) 89.72% (50E) 12.1% (100E) 23.97% (100E) Table 1. Summary of evaluation results. The model is validated over different bechmark to prove the contribution provided respect to the original CapsNet. We present results obtained with MNIST, SVHN, XXXXX00, XxxxxXXXX, and AwA2 datasets. Model Description Parameters Test Acc. CapsNet (Baseline) Conv - Primary Capsules - Final Capsules 8.2M 99.67% AA-Caps (Ours) Conv - Primary Capsules - Self-Attention - Conv 6.6M 99.34% Table 2. Comparison of CapsNet model with AA-Caps . The table presents a brief description layers that compose the structure of baseline CapsNet compared to the structure of AA-Caps, the number of trainable parameters, and the accuracy achieved by the model after 100 epochs on MNIST dataset. × SmallNORB [29] consists of 24300 image 96 96 stereo grey-scale images defined over 2 colour channels. We re- sized the images to 48x48 and during training processed random 32x32 crops, and central 32x32 patch during test. AwA2 [51] consists of 37322 images of 50 animals classes. The images are collected from public sources, that makes the dataset challenging due to the uncontrolled images.
Datasets. The selected algorithms were examined on 30 datasets, which were selected manually from the UCI Machine Learning Repository and are widely used by the pattern recognition community for evaluating learning algorithms. The datasets vary across such dimensions as the number of target classes, of instances, of input features and their type (nominal, numeric).
Datasets. Pilot 1a aims to predict the maintenance status of wind turbine electrical drivetrain components, such as generators and power converters. It examines onshore and offshore wind turbines powered by a doubly-fed induction generator and provides five datasets: • La Haute-Lys dataset- Wind turbine SCADA data (SCADA-Pilot1a): This dataset is collected from various wind turbines situated in multiple wind farms. There is a unique data structure and tag name for every turbine brand. A wind turbine's Supervisory Control and Data Acquisition system contains sensor data at the most important subcomponents of the wind turbine; the collected data are analysed at 10- minute intervals. Turbines during a period where the electrical subcomponents had faults are also included. • High-frequency Data (VUB-Pilot1a): This data is derived from a dedicated measurement campaign on onshore wind turbines and consists of a limited set of electric measurements and operational parameters (e.g., wind speed). • Open wind speed dataset (Flemish-banks-data-Pilot1a): includes environmental measurements (e.g., wind speeds, wind directions) collected along the Belgian North Sea. As a basis for defining semantic labels describing wind conditions, the dataset in LLUC 1a-01 shows the typical range of wind measurements occurring in the field. • Offshore measurement campaign data (High-frequency-accelerations-Pilot1a): This dataset includes acceleration measurements, that were taken of the drivetrain of an offshore wind turbine. • Dedicated current measurement campaign data (ENGIE-VUB-Pilot1a): This dataset consists of current signals that are acquired on an onshore wind turbine. These data are similar to the La Haute Lys dataset. As such they will be merged in further discussions on data handling and analytics with the La Haute Lys data as the same processing methodology applies.
Datasets. Pilot 2a focuses on integrating and deploying different PLATOON analytical services with the Institute Xxxxxxx Xxxxx (IMP) proprietary VIEW4 Supervisory control and data acquisition (SCADA) system deploys the energy value chain in Serbia. Energy resources related to Renewable Energy Sources (RES) in this pilot include: wind power plants and PV power Plants. Electricity production from solar and wind plants is subject to forecast errors that drive demand for balancing. These data sources are described as follows: PUPIN-RES-PROD: Historical Wind Power Production Measurements; it contains measurements of the production from the wind power plant, as well as topology data. PUPIN-RES-PV (Predictive Maintenance): Data is collected by the Phasor Measurement Unit installed at Institute Xxxxxxx Xxxxx. PUPIN-WeatherBit: Meteorological Data for RES Production (Generation) Forecasting Modelling Data. Meteorological dataset is utilized for RES production forecasting models training process as input data. Data is historical observational data. PUPIN-RES-Effects: Power System calculated based on the input by Phasor Measurement Unit installed at PUPIN. PUPIN-ENTSO-E: Transparency Platform-Energy Identification Codes (EICs); it maintains data about 39 electricity transmission system operators (TSOs) from 35 countries in Europe. These four data sources composed the catalog EBPM (Electricity Balance and Predictive Maintenance); they provide data in English (ENG) and Serbian (RS). More details in D2.4.
Datasets. Two use cases are demonstrated in ParcBit's technological park in Palma de Mallorca, Spain. ParcBit's grid consists of a 5 km long mid-voltage network and 5 km long low-voltage network. Pilot 2B uses three datasets: • Power grid ZIV Power Meters (Power-grid-ZIV-Pilot2b): dataset consists of hourly measurements of active and reactive power conveyed to users (measured by Smart Meters), gathered by concentrator and recognized by power meter. • Transformer Sensors data (TTEMP-Pilot2b): The data is collected from eight temperature sensors installed in various parts of the transformers, two sensors for ambient temperature, humidity, and pressure, and one sensor for oil temperature. • Medium-voltage Network Analyzer (MVNA-Pilot2b): contains an Electrical Network analyzer used for current transformers.
Datasets. Pilot 3a is about an office building equipped with a building management system (BMS) that controls HVAC and comfort in multiple zones of the building. This pilot includes LLUC 3a- 01 - Optimizing HVAC control regarding occupancy, and LLUC 3a-02 - Providing Demand Response Service through HVAC control.
Datasets. Pilot 3b aims at acquiring, aggregating, and processing data of energy consumption and related properties of various buildings for making energy domain-specific analyses, e.g., consumption forecasting, predictive maintenance, benchmarking. Pilot 3b is formed of 2 subpilots: 3b-PI and 3b-ROM. Each of the subpilots have different datasets as explained below: Pilot #3b_PI There are four possible destinations for the building spaces in Poste Italiane buildings located in the Rome Municipality Area: Datacenter, Logistics distribution, and cross-docking (mail & parcels), Retail and Office (Directional), with a total of 16 buildings. • Building Data (ANAG-Pilot3b): This dataset includes information about each building's features and general characteristics (ID office, address, destination use, square feet, climate zone, etc.). • Building Occupancy dataset (OCCU_C & OCCU_E): In each building of the pilot, the number of employees and customers is recorded daily. • Calendar (CALE-Pilot3b): keeps information regarding office openings and shifts. • Consumption on Building (EC_TOT & EC_SB): An overview on the temperature and humidity inside a building or line as well as information about active energy consumption (kWh). Among its many uses will be consumption prediction and benchmarking, identifying anomalies, and assessing lighting consumption. The information from the climate sensors serves various purposes, e.g., predicting consumption to maintain a certain comfort level, and appropriate consumption. • Building Energy Systems (BS-Pilot3b): This dataset provides a description of the heating, cooling, and lighting systems in all buildings. It will be possible to use building HVAC plant information for a variety of purposes, including consumption prediction and consumption benchmarking. In addition, building lighting plants information will be utilized to calculate lighting consumption, benchmarks and estimate lighting energy consumption. • Systems Anomalies (Fault-Pilot3b): The data is derived from monitoring the temperature within the building. In addition, alerts are generated when temperatures exceed a given threshold. Pilot #3b_ROM These are the available datasets: • Energy Meters Electrical Monthly Consumptions (EMEMC-Pilot3b-ROM): All power consumption from last month's meters (energy vendor). • Energy Meters Electrical Historical Consumptions (EMEHC2): A historical daily record (kwh) of the electric consumption for ROM buildings; divided in rows for each 15-min...
Datasets. Pilot 4a consists of four datasets from the area of Milan, Italy: ● Microgrid PV power production and forecast (MicroGridPVPilot4a): consists of forecasting and modeling of Photovoltaic (PV) power. The dataset is expected to grow with more than 30K records per day, and the updates are per minute. ●Microgrid battery (MicroGridBatteryPilot4a): comprises observations of batteries described in terms of State of Charge (SOC), State of Health (SOH), Direct Current (DC), and Alternate Current (AC). Current and voltage are registered, as well as average cell temperature and average ambient temperature. This dataset grows in 86K records per day, and new observations arrive per 1 sec. ● Microgrid potable water production (MPWPPilot4a): contains relevant measurements of a plant for potable water production. The dataset collects active and reactive power values, frequency of pump rotation, feed and permeate water conductivity, concentrate and permeate water flow rate, and temperature and pressure in the hydraulic circuit. It has a growth trend of 1,440 records per day, and updates are per minute. ●Microgrid weather parameters (MicroGridWeatherStationPilot4a): consist of observations sensed by a weather station. It reports ambient temperature, wind speed, wind direction, relative humidity, rain, and irradiance. The growth trend is 65K records per day, and observations are registered every 10 seconds. ● Microgrid full skype imaging (MicroGridFSIPilot4a): comprises full-sky images in JPEG format. It grows in more than 250 records per day every 5 minutes.
Datasets. The Parties intend to generate Datasets under the framework of open data. The Parties consider sharing Datasets necessary as it will enable the Parties to deliver expected outcomes. The purpose and the methodology for the creation of Datasets shall be described in the respective deliverables or reports. The Parties shall publicly share Datasets arising from the Project as close to real-time as possible per customary research publication norms. The Parties shall share such Datasets under the “no copyright reserved" option in the Creative Commons toolkit – CC0. If another Party’s Results or Background is to be published, such shall only occur, provided such Party has approved this beforehand.
