An investigation into voltage control approaches on an example distribution feeder to increase PV penetration

Solar power has become an increasing electricity resource in Australia’s electrical energy in recent years. The increase is due to the decrease in the cost of solar Photovoltaic (PV) systems and incentives provided by the Federal Government’s “Renewable Energy Target” scheme to offset carbon emissions. The existing electrical grid infrastructure was not originally designed to face high penetration levels of PV systems, so the growing embedded PV penetration levels has aroused various technical challenges and one of the key challenges is voltage rise. In order to provide methods to reduce technical barriers for achieving high penetration levels in Australian electricity networks, several approaches are studied in this report. The methods are studied with respect to prosumer (the combination of producer and consumer) aspect, utility aspect and a combination of these two aspects. The simulations were carried out using DIgSILENT PowerFactory software. Where possible, all designs and specifications are undertaken in accordance and in compliance with relevant standards and Western Power requirements and guidelines. Three prosumers’ methods which can be implemented in individual inverters are studied in chapter 6. They can be used to keep the voltage within the defined limits when the PV generation is 5kW/house, which is its assumed maximum value. But these technologies need to be upgraded to be more effective since the PV generation keeps climbing in Australian distribution networks. The utilities’ methods with additional devices implemented in the network are discussed in chapter 7. These control methods can effectively and efficiently control the voltage rise problem but one disadvantage is that they are all expensive and are not economically viable options. The combination of utilities’ method and prosumers’ method are introduced in this report as well. A recommendation for future studies that could be a continuation of this topic is provided at the end of the thesis report

FEAFA: A Well-Annotated Dataset for Facial Expression Analysis and 3D Facial Animation

Facial expression analysis based on machine learning requires large number of well-annotated data to reflect different changes in facial motion. Publicly available datasets truly help to accelerate research in this area by providing a benchmark resource, but all of these datasets, to the best of our knowledge, are limited to rough annotations for action units, including only their absence, presence, or a five-level intensity according to the Facial Action Coding System. To meet the need for videos labeled in great detail, we present a well-annotated dataset named FEAFA for Facial Expression Analysis and 3D Facial Animation. One hundred and twenty-two participants, including children, young adults and elderly people, were recorded in real-world conditions. In addition, 99,356 frames were manually labeled using Expression Quantitative Tool developed by us to quantify 9 symmetrical FACS action units, 10 asymmetrical (unilateral) FACS action units, 2 symmetrical FACS action descriptors and 2 asymmetrical FACS action descriptors, and each action unit or action descriptor is well-annotated with a floating point number between 0 and 1. To provide a baseline for use in future research, a benchmark for the regression of action unit values based on Convolutional Neural Networks are presented. We also demonstrate the potential of our FEAFA dataset for 3D facial animation. Almost all state-of-the-art algorithms for facial animation are achieved based on 3D face reconstruction. We hence propose a novel method that drives virtual characters only based on action unit value regression of the 2D video frames of source actors.Comment: 9 pages, 7 figure

The effect of Cr impurity to superconductivity in electron-doped BaFe2-xNixAs2

We use transport and magnetization measurements to study the effect of Cr-doping to the phase diagram of the electron-doped superconducting BaFe2-xNixAs2 iron pnictides. In principle, adding Cr to electron-doped BaFe2-xNixAs2 should be equivalent to the effect of hole-doping. However, we find that Cr doping suppresses superconductivity via impurity effect, while not affecting the normal state resistivity above 100 K. We establish the phase diagram of Cr-doped BaFe2-x-yNixCryAs2 iron pnictides, and demonstrate that Cr-doping near optimal superconductivity restore the long-range antiferromagnetic order suppressed by superconductivity.Comment: 10 pages, 5 figure

Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted ncomponent of this work in other works.Efficient defect classification is one of the most important preconditions to achieve online quality inspection for hot-rolled strip steels. It is extremely challenging owing to various defect appearances, large intraclass variation, ambiguous interclass distance, and unstable gray values. In this paper, a generalized completed local binary patterns (GCLBP) framework is proposed. Two variants of improved completed local binary patterns (ICLBP) and improved completed noise-invariant local-structure patterns (ICNLP) under the GCLBP framework are developed for steel surface defect classification. Different from conventional local binary patterns variants, descriptive information hidden in nonuniform patterns is innovatively excavated for the better defect representation. This paper focuses on the following aspects. First, a lightweight searching algorithm is established for exploiting the dominant nonuniform patterns (DNUPs). Second, a hybrid pattern code mapping mechanism is proposed to encode all the uniform patterns and DNUPs. Third, feature extraction is carried out under the GCLBP framework. Finally, histogram matching is efficiently accomplished by simple nearest-neighbor classifier. The classification accuracy and time efficiency are verified on a widely recognized texture database (Outex) and a real-world steel surface defect database [Northeastern University (NEU)]. The experimental results promise that the proposed method can be widely applied in online automatic optical inspection instruments for hot-rolled strip steel.Peer reviewe

Topics in Goodness-of-fit Test for Logistic Regression Models with Continuous Covariates

There is no phenomenal method practitioners can use as a appropriate tool for model validation when sparse data are presented in multiple logistic regression models. The characteristics of sparsity, i.e. very few number of observations falling in either grouped or individual covariate patterns, will invalidate the asymptotic chi-square distribution which requires large expected frequencies in each group or bin. Among those tests, Hosmer-Lemeshow (HL) is the most well-known and widely used as the standard test in assessing logistic regression models since its introducing. The disefficiencies of Hosmer-Lemeshow method has been pointed out for years, there is no dominate alternative one emerged yet by far, and the research in assessing logistic regression model fit when sparse data are presented is still very active. Two common methods among a few other proposed methods, namely Copas's unweighted residual sum of squares (RSS) and Su and Wei's & Lin's cumulative sums of residuals (CUMSUM), perform seemly better than the HL in some scenarios, however the limitation of those studies are obvious when those alternatives were introduced: (1) the sample size of the simulation is small (up to 500 observations), (2) the design matrix is relatively simple (usually one continuous and one categorical predictor variables), (3) the number of scenarios considered in their studies are limited, (4) the simulation setups are quite subjective. Due to these reasons, there is no well-established guidelines on model validation available for statistical practitioners' daily use when using a multiple logistic regression model with sparse data, a common approach is suggested to check model validation by investigating all those existing goodness-of-fit tests to see if they provide similar evidence of lack of fit. Therefore, it is crucial to assess the performance of each method through a comprehensive comparative study. We designed the comparison differently in at least four directions as we mentioned above: varied and expanded sample size, relatively complicated design matrix, more scenarios including adding (over-fitting) continuous/categorical predictor variables and omitting (under-fitting) main effect and /or interaction terms, and a more flexible or robust simulation setting in terms of many randomly sampled models rather than very few pre-specified models were investigated. Furthermore, we proposed a goodness-of-fit test by introducing a new method to partition the fitted values based on the commonly known conditions for the limiting distribution of chi-square type statistics for grouped data, which to some extend would overcome the disadvantage of the HL test when the expected counts in some bins are small (usually the cut-off is set as less than five). We also conducted the comparative study by including our proposed method. We summarized the varied goodness-of-fit results in terms of empirical level of significance and power and offered recommendations based on our more generalized simulation studies

SourceP: Smart Ponzi Schemes Detection on Ethereum Using Pre-training Model with Data Flow

As blockchain technology becomes more and more popular, a typical financial scam, the Ponzi scheme, has also emerged in the blockchain platform Ethereum. This Ponzi scheme deployed through smart contracts, also known as the smart Ponzi scheme, has caused a lot of economic losses and negative impacts. Existing methods for detecting smart Ponzi schemes on Ethereum mainly rely on bytecode features, opcode features, account features, and transaction behavior features of smart contracts, and such methods lack interpretability and sustainability. In this paper, we propose SourceP, a method to detect smart Ponzi schemes on the Ethereum platform using pre-training models and data flow, which only requires using the source code of smart contracts as features to explore the possibility of detecting smart Ponzi schemes from another direction. SourceP reduces the difficulty of data acquisition and feature extraction of existing detection methods while increasing the interpretability of the model. Specifically, we first convert the source code of a smart contract into a data flow graph and then introduce a pre-training model based on learning code representations to build a classification model to identify Ponzi schemes in smart contracts. The experimental results show that SourceP achieves 87.2\% recall and 90.7\% F-score for detecting smart Ponzi schemes within Ethereum's smart contract dataset, outperforming state-of-the-art methods in terms of performance and sustainability. We also demonstrate through additional experiments that pre-training models and data flow play an important contribution to SourceP, as well as proving that SourceP has a good generalization ability.Comment: 12 page

Exact physical quantities of a competing spin chain in the thermodynamic limit

We study the exact physical quantities of a competing spin chain which contains many interesting and meaningful couplings including the nearest neighbor, next nearest neighbor, chiral three spins, Dzyloshinsky-Moriya interactions and unparallel boundary magnetic fields in the thermodynamic limit. We obtain the density of zero roots, surface energies and elementary excitations in different regimes of model parameters. Due to the competition of various interactions, the surface energy and excited spectrum show many different pictures from those of the Heisenberg spin chain.Comment: 19 pages, 7 figure