Smart Demand for Improving Short-term Voltage Control on Distribution Networks

Smart grids must involve active roles from end users in order to be truly smart. The energy consumption has to be done in a flexible and intelligent manner, in accordance with the current conditions of the power system. Moreover, with the advent of dispersed and renewable generation, increasing customer integration to aid power system performance is almost inevitable. This study introduces a new type of smart demand side technology, denoted demand as voltage controlled reserve (DVR), to improve short-term voltage control, where customers are expected to play a more dynamic role to improve voltage control. The technology can be provided by thermostatically controlled loads as well as other types of load. This technology is proven to be effective in case of distribution systems with a large composition of induction motors, where the voltage presents a slow recovery characteristic due to deceleration of the motors during faults. This study presents detailed models, discussion and simulation tests to demonstrate the technical viability and effectiveness of the DVR technology for short-term voltage control.3872473

A task-and-technique centered survey on visual analytics for deep learning model engineering

Although deep neural networks have achieved state-of-the-art performance in several artificial intelligence applications in the past decade, they are still hard to understand. In particular, the features learned by deep networks when determining whether a given input belongs to a specific class are only implicitly described concerning a considerable number of internal model parameters. This makes it harder to construct interpretable hypotheses of what the network is learning and how it is learning both of which are essential when designing and improving a deep model to tackle a particular learning task. This challenge can be addressed by the use of visualization tools that allow machine learning experts to explore which components of a network are learning useful features for a pattern recognition task, and also to identify characteristics of the network that can be changed to improve its performance. We present a review of modern approaches aiming to use visual analytics and information visualization techniques to understand, interpret, and fine-tune deep learning models. For this, we propose a taxonomy of such approaches based on whether they provide tools for visualizing a network's architecture, to facilitate the interpretation and analysis of the training process, or to allow for feature understanding. Next, we detail how these approaches tackle the tasks above for three common deep architectures: deep feedforward networks, convolutional neural networks, and recurrent neural networks. Additionally, we discuss the challenges faced by each network architecture and outline promising topics for future research in visualization techniques for deep learning models. (C) 2018 Elsevier Ltd. All rights reserved.</p

A Methodology for Neural Network Architectural Tuning Using Activation Occurrence Maps

Finding the ideal number of layers and size for each layer is a key challenge in deep neural network design. Two approaches for such networks exist: filter learning and architecture learning. While the first one starts with a given architecture and optimizes model weights, the second one aims to find the best architecture. Recently, several visual analytics (VA) techniques have been proposed to understand the behavior of a network, but few VA techniques support designers in architectural decisions. We propose a hybrid methodology based on VA to improve the architecture of a pre-trained network by reducing/increasing the size and number of layers. We introduce Activation Occurrence Maps that show how likely each image position of a convolutional kernel's output activates for a given class, and Class Selectivity Maps, that show the selectiveness of different positions in a kernel's output for a given label. Both maps help in the decision to drop kernels that do not significantly add to the network's performance, increase the size of a layer having too few kernels, and add extra layers to the model. The user interacts from the first to the last layer, and the network is retrained after each layer modification. We validate our approach with experiments in models trained with two widely-known image classification datasets and show how our method helps to make design decisions to improve or to simplify the architectures of such models

Optimal Asynchronous Garbage Collection for RDT Checkpointing Protocols

Communication-induced checkpointing protocols that ensure rollback-dependency trackability (RDT) guarantee important properties to the recovery system without explicit coordination. However, to the best of our knowledge, there was no garbage collection algorithm for them which did not use some type of process synchronization, like time assumptions or reliable control message exchanges. This paper addresses the problem of garbage collection for RDT checkpointing protocols and presents an optimal solution for the case where coordination is done only by means of timestamps piggybacked in application messages. Our algorithm uses the same timestamps as off-the-shelf RDT protocols and ensures the tight upper bound on the number of uncollected checkpoints for each process during all the system execution

Optimal Asynchronous Garbage Collection for Checkpointing Protocols with Rollback-Dependency Trackability

Communication-induced checkpointing protocols that ensure rollback-dependency trackability (RDT) guarantee important properties to the recovery system without explicit coordination. However, to the best of our knowledge, there was no garbage collection algorithm for them which did not use some type of process synchronization, like time assumptions or reliable control message exchanges. This paper addresses the problem of garbage collection for RDT checkpointing protocols and presents an optimal solution for the case where coordination is done only by means of timestamps piggybacked in application messages. Our algorithm uses the same timestamps as off-the-shelf RDT protocols and ensures the tight upper bound on the number of uncollected checkpoints for each process during all the execution

The impact of the crisis in the external policy of Brazil

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Characterization of Mycobacterium chelonae-like strains by comparative genomics

Isolates of the Mycobacterium chelonae-M. abscessus complex are subdivided into four clusters (CHI to CHIV) in the INNO-LiPA (R) Mycobacterium spp DNA strip assay. A considerable phenotypic variability was observed among isolates of the CHII cluster. In this study, we examined the diversity of 26 CHII cluster isolates by phenotypic analysis, drug susceptibility testing, whole genome sequencing and single-gene analysis. Pairwise genome comparisons were performed using several approaches, including average nucleotide identity (ANI) and genome-to-genome distance (GGD) among others. Based on ANI and GGD the isolates were identified as M. chelonae (14 isolates), M. franklinii (2 isolates) and M. salmoniphium (1 isolate). The remaining 9 isolates were subdivided into three novel putative genomospecies. Phenotypic analyses including drug susceptibility testing, as well as whole genome comparison by TETRA and delta differences, were not helpful in separating the groups revealed by ANI and GGD. The analysis of standard four conserved genomic regions showed that rpoB alone and the concatenated sequences clearly distinguished the taxonomic groups delimited by whole genome analyses. In conclusion, the CHII INNO-LiPa is not a homogeneous cluster; on the contrary, it is composed of closely related different species belonging to the M. chelonae-M. abscessus complex and also several unidentified isolates. The detection of these isolates, putatively novel species, indicates a wider inner variability than the presently known in this complex