Engineering Applications of Artificial Intelligence

wti crude oil price GSVI data economic data

Academic spaces are an environment that promotes student performance not only because of the quality of its equipment, but also because of its ambient comfort conditions, which can be controlled by means of actuators that receive data from sensors. Something similar can be said about other environments, such as home, business, or industry environment. However, sensor devices can cause faults or inaccurate readings in a timely manner, affecting control mechanisms. The mutual relationship between ambient variables can be a source of knowledge to predict a variable in case a sensor fails. Moreover, the relationship between these variables and the occupation of spaces by students over time also contains an adequate knowledge of the context for prediction. This dataset provides sensor readings from sensors over time in different academic rooms. The data are supplied in a file in Excel format .xlsx. It containts several sheets corresponding with the different smart spaces (laboratories and classrooms). Each dataset is a matrix where the rows are the time dates of the readings, and there are three columns for the sensor readings of temperature, CO2 and humidity.

CubeSat Training and Test Dataset used for the training, validation and testing of ultralight CubeSatNet CNN model designed for 1U CubeSat

Training and Testing data for the 6 PSS parallel robot

The attached file includes our input parameters in the computational results.

Complete datasets corresponding to the robots used for the experiments.

36 daily time series data of stock markets and commodities.

source data for the paper "A population-based iterated greedy algorithm for no-wait job shop scheduling with total flow time criterion"

By applying the spray method (IEC Standard 62073), about 4500 photos were collected and are available online, from all hydrophobicity classes using distilled water-ethyl alcohol as spraying solution. The pictures of the seven different hydrophobicity classes were split into three separate sets for each hydrophobicity class. The first one consisting of 400 instances of each class (400 × 7 = 2800 photos) was used for the training of the networks. The second one consisting of 100 instances of each class (100 × 7 = 700 photos) was used for the evaluation-validation of the learning course and the comparison of the different models. The last one with 122-165 different instances of each class (980 photos) was used for the final assessment of our chosen model.

It contains the Time Series Datasets used and Simulation Results Obtained relating to this paper.