Time-Series Mapping of PM 10

This paper presents space-time kriging within a multi-Gaussian framework for time-series mapping of particulate matter less than 10 μm in aerodynamic diameter (PM10) concentration. To account for the spatiotemporal autocorrelation structures of monitoring data and to model the uncertainties attached to the prediction, conventional multi-Gaussian kriging is extended to the space-time domain. Multi-Gaussian space-time kriging presented in this paper is based on decomposition of the PM10 concentrations into deterministic trend and stochastic residual components. The deterministic trend component is modelled and regionalized using the temporal elementary functions. For the residual component which is the main target for space-time kriging, spatiotemporal autocorrelation information is modeled and used for space-time mapping of the residual. The conditional cumulative distribution functions (ccdfs) are constructed by using the trend and residual components and space-time kriging variance. Then, the PM10 concentration estimate and conditional variance are empirically obtained from the ccdfs at all locations in the study area. A case study using the monthly PM10 concentrations from 2007 to 2011 in the Seoul metropolitan area, Korea, illustrates the applicability of the presented method. The presented method generated time-series PM10 concentration mapping results as well as supporting information for interpretations, and led to better prediction performance, compared to conventional spatial kriging

Iterative Soft Decoding Algorithm for DNA Storage Using Quality Score and Redecoding

Ever since deoxyribonucleic acid (DNA) was considered as a next-generation data-storage medium, lots of research efforts have been made to correct errors occurred during the synthesis, storage, and sequencing processes using error correcting codes (ECCs). Previous works on recovering the data from the sequenced DNA pool with errors have utilized hard decoding algorithms based on a majority decision rule. To improve the correction capability of ECCs and robustness of the DNA storage system, we propose a new iterative soft decoding algorithm, where soft information is obtained from FASTQ files and channel statistics. In particular, we propose a new formula for log-likelihood ratio (LLR) calculation using quality scores (Q-scores) and a redecoding method which may be suitable for the error correction and detection in the DNA sequencing area. Based on the widely adopted encoding scheme of the fountain code structure proposed by Erlich et al., we use three different sets of sequenced data to show consistency for the performance evaluation. The proposed soft decoding algorithm gives 2.3% ~ 7.0% improvement of the reading number reduction compared to the state-of-the-art decoding method and it is shown that it can deal with erroneous sequenced oligo reads with insertion and deletion errors

Enhancement of hole injection using ozone treated Ag nanodots dispersed on indium tin oxide anode for organic light emitting diodes

The authors report the enhancement of hole injection using an indium tin oxide (ITO) anode covered with ultraviolet (UV) ozone-treated Ag nanodots for fac tris (2-phenylpyridine) iridium Ir(ppy)3-doped phosphorescent organic light-emitting diodes (OLEDs). X-ray photoelectron spectroscopy and UV-visible spectrometer analysis exhibit that UV-ozone treatment of the Ag nanodots dispersed on the ITO anode leads to formation of Ag2O nanodots with high work function and high transparency. Phosphorescent OLEDs fabricated on the Ag2O nanodot-dispersed ITO anode showed a lower turn-on voltage and higher luminescence than those of OLEDs prepared with a commercial ITO anode. It was thought that, as Ag nanodots changed to Ag2O nanodots by UV-ozone treatment, the decrease of the energy barrier height led to the enhancement of hole injection in the phosphorescent OLEDs.This work was supported by Korea Research Foundation grant funded by Korean Government (MOEHRD: Basic Research Promotion Fund)(KRF-2006-003-D00243) and Ministry of Commerce, Industry, and Energy

Empirical essays on patronage, decentralization, and distributive politics.

This dissertation addresses three questions: existence of patronage in the public administration, the effect of decentralization on distributive politics, and the effect of political decentralization on the fiscal management of local governments. The fist chapter provides unique evidence on parochial corruption, the second chapter presents a possibility that decentralization process may activate political factors in distribution of government-controlled resources, and the third chapter suggests that elected local officials may behave differently in the fiscal management of local governments. The first chapter examines how presidential changes affect position assignments of Korean public prosecutors by utilizing personnel data on them during 1992--2000. After controlling for unobservable but fixed characteristics of individual prosecutors, I find that prosecutors who shared birth and school environments with the incumbent president were about twice as likely to be assigned to a variety of influential positions. The second chapter examines whether political factors are more active in the distributive policy after decentralization reforms. Utilizing the resumption of local elections in Korea as an exogenous institutional change and constructing city, county, and district level panel data during 1991--1999, I find that political factors become more active in the distribution of intergovernmental transfers after decentralization reforms. In particular, districts closely contested in elections receive favorable distribution of intergovernmental transfers after decentralization. The third chapter examines whether elected local officials behave differently in their fiscal management as compared to appointed ones. For empirical purpose, fiscal data of Korean local governments during 1991--1999 is employed and the resumption of local elections in 1995 is utilized as a natural experiment. After constructing some fiscal indices reflecting ways of fiscal managements, I find that elected local officials enhance their fiscal independence by raising more non-tax revenues relative to local tax revenues and face softer budget constraints. In particular, local officials in closely contested districts face softer budget constraints and decrease their fiscal independence.Ph.D.EconomicsFinancePublic administrationSocial SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/123768/2/3105997.pd

Spatial Downscaling of TRMM Precipitation Using Geostatistics and Fine Scale Environmental Variables

A geostatistical downscaling scheme is presented and can generate fine scale precipitation information from coarse scale Tropical Rainfall Measuring Mission (TRMM) data by incorporating auxiliary fine scale environmental variables. Within the geostatistical framework, the TRMM precipitation data are first decomposed into trend and residual components. Quantitative relationships between coarse scale TRMM data and environmental variables are then estimated via regression analysis and used to derive trend components at a fine scale. Next, the residual components, which are the differences between the trend components and the original TRMM data, are then downscaled at a target fine scale via area-to-point kriging. The trend and residual components are finally added to generate fine scale precipitation estimates. Stochastic simulation is also applied to the residual components in order to generate multiple alternative realizations and to compute uncertainty measures. From an experiment using a digital elevation model (DEM) and normalized difference vegetation index (NDVI), the geostatistical downscaling scheme generated the downscaling results that reflected detailed characteristics with better predictive performance, when compared with downscaling without the environmental variables. Multiple realizations and uncertainty measures from simulation also provided useful information for interpretations and further environmental modeling

Combining Gaussian Process Regression with Poisson Blending for Seamless Cloud Removal from Optical Remote Sensing Imagery for Cropland Monitoring

Constructing optical image time series for cropland monitoring requires a cloud removal method that accurately restores cloud regions and eliminates discontinuity around cloud boundaries. This paper describes a two-stage hybrid machine learning-based cloud removal method that combines Gaussian process regression (GPR)-based predictions with image blending for seamless optical image reconstruction. GPR is employed in the first stage to generate initial prediction results by quantifying temporal relationships between multi-temporal images. GPR predictive uncertainty is particularly combined with prediction values to utilize uncertainty-weighted predictions as the input for the next stage. In the second stage, Poisson blending is applied to eliminate discontinuity in GPR-based predictions. The benefits of this method are illustrated through cloud removal experiments using Sentinel-2 images with synthetic cloud masks over two cropland sites. The proposed method was able to maintain the structural features and quality of the underlying reflectance in cloud regions and outperformed two existing hybrid cloud removal methods for all spectral bands. Furthermore, it demonstrated the best performance in predicting several vegetation indices in cloud regions. These experimental results indicate the benefits of the proposed cloud removal method for reconstructing cloud-contaminated optical imagery

Effects of Class Purity of Training Patch on Classification Performance of Crop Classification with Convolutional Neural Network

As the performance of supervised classification using convolutional neural networks (CNNs) are affected significantly by training patches, it is necessary to analyze the effects of the information content of training patches in patch-based classification. The objective of this study is to quantitatively investigate the effects of class purity of a training patch on performance of crop classification. Here, class purity that refers to a degree of compositional homogeneity of classes within a training patch is considered as a primary factor for the quantification of information conveyed by training patches. New quantitative indices for class homogeneity and variations of local class homogeneity over the study area are presented to characterize the spatial homogeneity of the study area. Crop classification using 2D-CNN was conducted in two regions (Anbandegi in Korea and Illinois in United States) with distinctive spatial distributions of crops and class homogeneity over the area to highlight the effect of class purity of a training patch. In the Anbandegi region with high class homogeneity, superior classification accuracy was obtained when using large size training patches with high class purity (7.1%p improvement in overall accuracy over classification with the smallest patch size and the lowest class purity). Training patches with high class purity could yield a better identification of homogenous crop parcels. In contrast, using small size training patches with low class purity yielded the highest classification accuracy in the Illinois region with low class homogeneity (19.8%p improvement in overall accuracy over classification with the largest patch size and the highest class purity). Training patches with low class purity could provide useful information for the identification of diverse crop parcels. The results indicate that training samples in patch-based classification should be selected based on the class purity that reflects the local class homogeneity of the study area