ZIP
Automatic generation control (AGC) of power generation units aims at providing satisfactory responses of generated active powers to desired active powers dispatched from a power grid center. This paper proposes a method to estimate two frequently-used AGC performance metrics of response rapidity and accuracy. The proposed method is composed of two main parts . The first part selects step-like data segments as those being similar to a designed step-change time sequence, based on consecutive piece-wise linear representations of the desired active power.
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This dataset contains the raw data of the measurements/simulations presented in "Modulation Scheme Analysis for Low-Power Leadless Pacemaker Synchronization Based on Conductive Intracardiac Communication" by A. Ryser et al. This work analyzed the bit error rate (BER) performance of a prototype dual-chamber leadless pacemaker both in simulation and in-vitro experiments on porcine hearts.
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Cryo-EM dataset of 80S ribosomes from yeast. This dataset has been described in Dashti et al. (2014, PNAS) "Trajectories of the ribosome as a Brownian nanomachine". In that study, a subset of the dataset was used to demonstrate the performance of a machine learning technique (now termed ManifoldEM) using manifold embedding to determine the energy landscape of a molecule. The dataset is re-analyzed in Seitz et al.
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The Internet of Things and edge computing are fostering a future of ecosystems
hosting complex decentralized computations, deeply integrated with our very dynamic
environments. Digitalized buildings, communities of people, and cities will be the
next-generation “hardware and platform”, counting myriads of interconnected devices, on top of
which intrinsically-distributed computational processes will run and self-organize. They will
spontaneously spawn, diffuse to pertinent logical/physical regions, cooperate and compete,
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Driving behavior plays a vital role in maintaining safe and sustainable transport, and specifically, in the area of traffic management and control, driving behavior is of great importance since specific driving behaviors are significantly related with traffic congestion levels. Beyond that, it affects fuel consumption, air pollution, public health as well as personal mental health and psychology. Use of Smartphone sensors for data acquisition has emerged as a means to understand and model driving behavior. Our aim is to analyze driving behavior using on Smartphone sensors’ data streams.
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This data resource is an outcome of the NSF RAPID project titled "Democratizing Genome Sequence Analysis for COVID-19 Using CloudLab" awarded to University of Missouri-Columbia.
The resource contains the output of variant analysis (along with CADD scores) on human genome sequences obtained from the COVID-19 Data Portal. The variants include single nucleotide polymorphisms (SNPs) and short insert and deletes (indels).
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This dataset is released with our research paper titled “Scene-graph Augmented Data-driven Risk Assessment of Autonomous Vehicle Decisions” (https://arxiv.org/abs/2009.06435). In this paper, we propose a novel data-driven approach that uses scene-graphs as intermediate representations for modeling the subjective risk of driving maneuvers. Our approach includes a Multi-Relation Graph Convolution Network, a Long-Short Term Memory Network, and attention layers.
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The datasets consist of operational data and detailed information of three inverter transformers in a 3.275 MW PV plant in the outskirt of Brisbane, Australia. The data includes load current, top-oil temperature, moisture in top oil, ambient temperature, solar irradiance and individual current harmonics (up to 31st order). The time interval of the data is either 1 minute or 3 seconds (dependent on the data type). The data can be used to study the ageing of inverter transformers in this PV plant.
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A team of researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh along with their collaborators from Malaysia in collaboration with medical doctors from Hamad Medical Corporation and Bangladesh have created a database of chest X-ray images for Tuberculosis (TB) positive cases along with Normal images. In our current release, there are 3500 TB images, and 3500 normal images.
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