Hybrid Electric Power Systems In Remote Arctic Villages: Economic And Environmental Analysis For Monitoring, Optimization, And Control

Thesis (Ph.D.) University of Alaska Fairbanks, 2006The need for energy-efficient and reliable electric power in remote arctic communities of Alaska is a driving force for research in this work. Increasing oil prices, high transportation costs for fuels, and new environmental standards have forced many utilities to explore hybrid energy systems in an attempt to reduce the cost of electricity (COE). This research involves the development of a stand-alone hybrid power system model using MATLABRTM SimulinkRTM for synthesizing the power system data and performing the economic and environmental analysis of remote arctic power systems. The hybrid model consists of diesel electric generators (DEGs), a battery bank, a photovoltaic (PV) array, and wind turbine generators (WTGs). The economic part of the model is used to study the sensitivity analysis of fuel cost and the investment rate on the COE, the life cycle cost (LCC) of the system, and the payback time of the system. The environmental part of the model calculates the level of various pollutants including carbon dioxide (CO2), nitrogen oxides (NOx), and the particulate matter (PM10). The environmental analyses part of the model also calculates the avoided cost of various pollutants. The developed model was used to study the economics and environmental impacts of a stand-alone DEG system installed at the University of Alaska Fairbanks Energy Center, the wind-diesel-battery hybrid power system installed at Wales Village, Alaska, and the PV-diesel-battery hybrid power system installed at Lime Village, Alaska. The model was also used to predict the performance of a designed PV-wind-diesel-battery system for Kongiganak Village. The results obtained from the SimulinkRTM model were in close agreement with those predicted by the Hybrid Optimization Model for Electric Renewables (HOMER) software developed at National Renewable Energy Laboratory (NREL)

Synthesis, characterization and application of eco-friendly lavender oil microcapsules on cotton

In this study, lavender oil microcapsules have been synthesized, characterized and then applied on cotton fabric throughpadding followed by drying and curing. The treated samples are evaluated for wash fastness, tensile strength, stiffness, andthe most important, release rate from treated fabric. Most of the synthesized microcapsules are found in the range of10-30 microns. Cross-linking with DETA shows improvement in the shell morphology with slow release of lavender fromtreated fabric. Stiffness of the treated fabric increases proportionately with increase in concentration of microcapsules withsimultaneous fall in strength

DeepInf: Social Influence Prediction with Deep Learning

Social and information networking activities such as on Facebook, Twitter, WeChat, and Weibo have become an indispensable part of our everyday life, where we can easily access friends' behaviors and are in turn influenced by them. Consequently, an effective social influence prediction for each user is critical for a variety of applications such as online recommendation and advertising. Conventional social influence prediction approaches typically design various hand-crafted rules to extract user- and network-specific features. However, their effectiveness heavily relies on the knowledge of domain experts. As a result, it is usually difficult to generalize them into different domains. Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting social influence. In general, DeepInf takes a user's local network as the input to a graph neural network for learning her latent social representation. We design strategies to incorporate both network structures and user-specific features into convolutional neural and attention networks. Extensive experiments on Open Academic Graph, Twitter, Weibo, and Digg, representing different types of social and information networks, demonstrate that the proposed end-to-end model, DeepInf, significantly outperforms traditional feature engineering-based approaches, suggesting the effectiveness of representation learning for social applications.Comment: 10 pages, 5 figures, to appear in KDD 2018 proceeding

Effective Radii and Color Gradients in Radio Galaxies

We present de Vaucouleurs' effective radii in B and R bands for a sample of Molonglo Reference Catalogue radio galaxies and a control sample of normal galaxies. We use the ratio of the scale lengths in the two bands as an indicator to show that the radio galaxies tend to have excess of blue color in their inner region much more frequently than the control galaxies. We show that the scale length ratio is a useful indicator of radial color variation even when the conventional color gradient is too noisy to serve the purpose.Comment: 11 pages, 4 figures, (LaTeX: aaspp4, epsfig), to appear in ApJL 199