Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks

This paper develops a novel graph convolutional network (GCN) framework for fault location in power distribution networks. The proposed approach integrates multiple measurements at different buses while taking system topology into account. The effectiveness of the GCN model is corroborated by the IEEE 123 bus benchmark system. Simulation results show that the GCN model significantly outperforms other widely-used machine learning schemes with very high fault location accuracy. In addition, the proposed approach is robust to measurement noise and data loss errors. Data visualization results of two competing neural networks are presented to explore the mechanism of GCN's superior performance. A data augmentation procedure is proposed to increase the robustness of the model under various levels of noise and data loss errors. Further experiments show that the model can adapt to topology changes of distribution networks and perform well with a limited number of measured buses.Comment: Accepcted by IEEE Journal on Selected Areas in Communicatio

Process Based Large Scale Molecular Dynamic Simulation of a Fuel Cell Catalyst Layer

In this paper, a large scale molecular dynamic method for reconstruction of the catalyst layers (CLs) in proton exchange membrane fuel cells is developed as a systematic technique to provide an insight into the self-organized phenomena and the microscopic structure. The proposed Coarse-Grained (CG) method is developed and applied to the step formation process, which follows the preparation of the catalyst-coated membranes (CCMs). The fabrication process is mimicked and evaluated in details with consideration of the interactions of material components at a large scale. By choosing three sizes of the unit box, the relevant configurations of the equilibrium states are compared and analyzed. Furthermore, the primary pores of 2-10 nm in the agglomerates mainly consist of the channel space, which acts as the large networks and could be filled with liquid water. Moreover, various physical parameters are predicted and evaluated for four cases. The active Pt surface areas are also calculated by the current model, and then compared with the experimental data available in the literature. Finally, the pair correlation functions are employed to predict the distributions and hydrophobic properties of the components, providing the information on phase segregation and microscopic structure of the CLs. (C) 2011 The Electrochemical Society. [DOI: 10.1149/2.028203jes] All rights reserved

Hairpin DNA functionalized gold nanorods for mRNA detection in homogenous solution

We report a novel fluorescent probe for mRNA detection. It consists of a gold nanorod (GNR) functionalized with fluorophore labeled hairpin oligonucleotides (hpDNA) that are complementary to the mRNA of a target gene. This nanoprobe was found to be sensitive to a complementary oligonucleotide, as indicated by significant changes in both fluorescence intensity and lifetime. The influence of the surface density of hpDNA on the performance of this nanoprobe was investigated, suggesting that high hybridization efficiency could be achieved at a relatively low surface loading density of hpDNA. However, steady-state fluorescence spectroscopy revealed better overall performance, in terms of sensitivity and detection range, for nanoprobes with higher hairpin coverage. Time-resolved fluorescence lifetime spectroscopy revealed significant lifetime changes of the fluorophore upon hybridization of hpDNA with targets, providing further insight on the hybridization kinetics of the probe as well as the quenching efficiency of GNRs

Ultra-intensified intermittent-perfusion fed-batch (UIIPFB) process quadrupled productivity of a bispecific antibody

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Fault Detection for Covered Conductors With High-Frequency Voltage Signals: From Local Patterns to Global Features

The detection and characterization of partial discharge (PD) are crucial for the insulation diagnosis of overhead lines with covered conductors. With the release of a large dataset containing thousands of naturally obtained high-frequency voltage signals, data-driven analysis of fault-related PD patterns on an unprecedented scale becomes viable. The high diversity of PD patterns and background noise interferences motivates us to design an innovative pulse shape characterization method based on clustering techniques, which can dynamically identify a set of representative PD-related pulses. Capitalizing on those pulses as referential patterns, we construct insightful features and develop a novel machine learning model with a superior detection performance for early-stage covered conductor faults. The presented model outperforms the winning model in a Kaggle competition and provides the state-of-the-art solution to detect real-time disturbances in the field.Comment: To be published in IEEE Transactions on Smart Gri

Petuum: A New Platform for Distributed Machine Learning on Big Data

What is a systematic way to efficiently apply a wide spectrum of advanced ML programs to industrial scale problems, using Big Models (up to 100s of billions of parameters) on Big Data (up to terabytes or petabytes)? Modern parallelization strategies employ fine-grained operations and scheduling beyond the classic bulk-synchronous processing paradigm popularized by MapReduce, or even specialized graph-based execution that relies on graph representations of ML programs. The variety of approaches tends to pull systems and algorithms design in different directions, and it remains difficult to find a universal platform applicable to a wide range of ML programs at scale. We propose a general-purpose framework that systematically addresses data- and model-parallel challenges in large-scale ML, by observing that many ML programs are fundamentally optimization-centric and admit error-tolerant, iterative-convergent algorithmic solutions. This presents unique opportunities for an integrative system design, such as bounded-error network synchronization and dynamic scheduling based on ML program structure. We demonstrate the efficacy of these system designs versus well-known implementations of modern ML algorithms, allowing ML programs to run in much less time and at considerably larger model sizes, even on modestly-sized compute clusters.Comment: 15 pages, 10 figures, final version in KDD 2015 under the same titl

A Numerical Study on the Temperature Field of a R290 Hermetic Reciprocating Compressor with Experimental Validation

A numerical model to predict the temperature field in a R290 hermetic reciprocating compressor is presented in this work. The control volume method and the lumped parameter method are used in the simulation. The compressor is divided into 6 control volumes, including the suction muffler, the cylinder, the discharge chamber, the discharge muffler, the discharge pipe and the shell. The system of non-linear equations is formed of the energy balance equations of every control column. The temperature field is derived by solving the equations. To valid the numerical model accurately, temperature experiment has been carried out in 3 same-type hermetic reciprocating compressors using R290 as working fluid. The simulation result shows a good agreement compared with the experiment

Scale- and Context-Aware Convolutional Non-intrusive Load Monitoring

Non-intrusive load monitoring addresses the challenging task of decomposing the aggregate signal of a household's electricity consumption into appliance-level data without installing dedicated meters. By detecting load malfunction and recommending energy reduction programs, cost-effective non-intrusive load monitoring provides intelligent demand-side management for utilities and end users. In this paper, we boost the accuracy of energy disaggregation with a novel neural network structure named scale- and context-aware network, which exploits multi-scale features and contextual information. Specifically, we develop a multi-branch architecture with multiple receptive field sizes and branch-wise gates that connect the branches in the sub-networks. We build a self-attention module to facilitate the integration of global context, and we incorporate an adversarial loss and on-state augmentation to further improve the model's performance. Extensive simulation results tested on open datasets corroborate the merits of the proposed approach, which significantly outperforms state-of-the-art methods.Comment: Accepted by IEEE Transactions on Power System