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In order to obtain the ex-ante least-cost schedule of energy generation and reserves for online generating units, the system operator addresses a dynamic decision-making process known as the economic dispatch (ED) problem. Current industry practice involves adopting a deterministic two-stage optimization framework that relies on a one-day-ahead horizon and a forecast of uncertain parameters. The optimal solution to the resulting problem thus yields a generation schedule for the entire day ahead.


Dataset consisting of 17 images of Nile Tilapia bred in a circular pond (diameter=10m, deep = 1.5m ) located at the tronconal in Hermosillo, Sonora, México and an aquarium.

From the total of fish, 17 were selected. Each fish was measured with a ruler; the longitude was measured from the beginning of tail to tip and the height was measured from the beginning of the dorsal fin to the bottom, and its weight was measured with a standard balance.






The presented dataset comprises the electrical conductivity and relative permittivity data derived from the lower-scale model simulations as a part of multiscale computational modelling of electrical properties of thyroid and parathyroid tissues, which relates to the paper 'Multiscale Model Development for Electrical Properties of Thyroid and Parathyroid Tissues' submitted to IEEE Open Journal of Engineering in Medicine and Biology.


Deep learning has revolutionized the field of robotics. To deal with the lack of annotated training samples for learning deep models in robotics, Sim-to-Real transfer has been invented and widely used. However, such deep models trained in simulation environment typically do not transfer very well to the real world due to the challenging problem of “reality gap”. In response, this letter presents a conceptually new Digital Twin (DT)-CycleGAN framework by integrating the advantages of both DT methodology and the CycleGAN model so that the reality gap can be effectively bridged.


This article presents the details of the Cardinal RF (CardRF) dataset. CardRF is acquired to foster research in RF- based UAV detection and identification or RF fingerprinting. RF signals were collected from UAV controllers, UAV, Bluetooth, and Wi-Fi devices. Signals are collected at both visual line-of-sight and beyond-line-of-sight. The assumptions and procedure for the data acquisition are presented. A detailed explanation of how the data can be utilized is discussed. CardRF is over 65 GB in storage memory.


This project investigates bias in automatic facial recognition (FR). Specifically, subjects are grouped into predefined subgroups based on gender, ethnicity, and age. We propose a novel image collection called Balanced Faces in the Wild (BFW), which is balanced across eight subgroups (i.e., 800 face images of 100 subjects, each with 25 face samples).


The C3I Thermal Automotive Dataset provides > 35,000 distinct frames along with annotated thermal frames for the development of smart thermal perception system/ object detection system that will enable the automotive industry and researchers to develop safer and more efficient ADAS and self-driving car systems. The overall dataset is acquired, processed, and open-sourced in challenging weather and environmental scenarios. The dataset is recorded from a lost-cost yet effective 640x480 uncooled LWIR thermal camera.


Study of mind and nature of intelligence is widely studied in cognitive science. Also, Artificial Wisdom which redefines the Artificial Wisdom is emerging research area where machine intelligence must collaborates with the constructive behavior and values of humanity. Thinking ability of human beings is recognized as the consciousness. Researchers from different domains like Cognitive Science, Artificial Intelligence, Psychology, Computer Engineering etc. are used to perform experimentations on consciousness or arousal of thoughts.


COVID-19 tracing data are utilized to form two dataset networks, one is based on the virus transition between the world countries, as the dataset consists of 36 countries and 75 relationships between them. Whereas the other dataset is an attributed network based on the virus transition among the contact tracing in the Kingdom of Bahrain. This type of networks that is concerned in tracking a disease or virus was not formed based on COVID-19 virus transmission.