Doctor of Philosophy

dissertationThe objective of this dissertation is to estimate possible leakage pathways such as abandoned wells and fault zones in the deep subsurface for CO2 storage using inverse analysis. Leakage pathways through a cap rock may cause CO2 to migrate into the layers above cap rock. An inverse analysis using iTOUGH2 was applied to estimate possible leakage pathways using pressure anomalies in the overlying formation induced by brine and/or CO2 leaks. Prior to applying inverse analysis, sensitivity analysis and forward modeling were conducted. In addition, an inverse model was developed for single-phase flow and it was applied to the leakage pathway estimation in a brine/CO2 system. Migration of brine/CO2 through the leakage pathway was simulated in the generic homogeneous and heterogeneous domains. The increased pressure gradient due to CO2 injection continuously induced brine leaks through the leakage pathway. Capillary pressure was induced by the migration of CO2 along the leakage pathway saturated by brine. Pressure anomalies due to capillary pressures were propagated to the entire overlying formation. The sensitivity analysis was focused on how the hydrogeological properties affect the pressure signals at monitoring wells. Parameter estimation using the iTOUGH2 model was applied to detect locations of leakage pathways in homogeneous and heterogeneous model domains. For homogeneous models, the parameterization of uncertain permeability in an overlying formation could improve location estimation accuracy. Residual analysis illustrated that pressure anomalies in the overlying formation induced by leaks are critical information for the leakage pathway estimation. For heterogeneous models, the calibration of renormalized permeability values could reduce systematic modeling errors and should improve the leakage pathway location estimation accuracy. The weighting factors significantly influenced the accuracy of the leakage pathway estimation. The developed inverse model was applied to estimate the leakage pathway in a brine/CO2 system using pressure anomalies induced by only brine leaks. To estimate a possible leakage pathway, the developed inverse model calibrated each integrated parameter (of both cross-sectional area and vertical hydraulic conductivity) of initial guesses of the leakage pathway. This application can provide warning before the CO2 leaks, and will be useful in mitigating the risk of CO2 leaks

Human Facial Expression Recognition Using Fisher Independent Component Analysis and Hidden Markov Model

Non

Generalized gravity model for human migration

The gravity model (GM) analogous to Newton's law of universal gravitation has successfully described the flow between different spatial regions, such as human migration, traffic flows, international economic trades, etc. This simple but powerful approach relies only on the 'mass' factor represented by the scale of the regions and the 'geometrical' factor represented by the geographical distance. However, when the population has a subpopulation structure distinguished by different attributes, the estimation of the flow solely from the coarse-grained geographical factors in the GM causes the loss of differential geographical information for each attribute. To exploit the full information contained in the geographical information of subpopulation structure, we generalize the GM for population flow by explicitly harnessing the subpopulation properties characterized by both attributes and geography. As a concrete example, we examine the marriage patterns between the bride and the groom clans of Korea in the past. By exploiting more refined geographical and clan information, our generalized GM properly describes the real data, a part of which could not be explained by the conventional GM. Therefore, we would like to emphasize the necessity of using our generalized version of the GM, when the information on such nongeographical subpopulation structures is available.Comment: 14 pages, 6 figures, 2 table

Offline Imitation Learning by Controlling the Effective Planning Horizon

In offline imitation learning (IL), we generally assume only a handful of expert trajectories and a supplementary offline dataset from suboptimal behaviors to learn the expert policy. While it is now common to minimize the divergence between state-action visitation distributions so that the agent also considers the future consequences of an action, a sampling error in an offline dataset may lead to erroneous estimates of state-action visitations in the offline case. In this paper, we investigate the effect of controlling the effective planning horizon (i.e., reducing the discount factor) as opposed to imposing an explicit regularizer, as previously studied. Unfortunately, it turns out that the existing algorithms suffer from magnified approximation errors when the effective planning horizon is shortened, which results in a significant degradation in performance. We analyze the main cause of the problem and provide the right remedies to correct the algorithm. We show that the corrected algorithm improves on popular imitation learning benchmarks by controlling the effective planning horizon rather than an explicit regularization.Comment: Preprin

Recycling Studies for Swine Manure Slurry Using Multi Process of Aerobic Digestion (MPAD)

This study was carried out to investigate the feasibility of Multi Process of Aerobic Digestion (MPAD) for recycling of swine manure slurry as fertilizer. MPAD consisted of three kinds of difference process which are thermophilic aerobic oxidation (TAO) system, lime solidification system, and reverse osmosis (R/O) membrane system. TAO system was studied well previously for decade. The chemical composition of the lime-treated solid fertilizer was as like that organic matter 17.4%, moisture 34.1%, N 0.9%, P 1.7%, K 0.3%, Ca 12.7%, and which was expected to be useful as acid soil amendment material. The concentrated liquid material produced by R/O membrane system was also expected as a good fertilizer for crops production and soil fertility improvement.OtherShinshu University International Symposium 2010 : Sustainable Agriculture and Environment : Asian Networks II 　信州大学国際シンポジウム2010 : 持続的農業と環境 : アジアネットワークII ― アジアネットワークの発展をめざして―. 信州大学農学部, 2010, 71-77conference pape

Applications of pre-open sets

[EN] Using the concept of pre-open set, we introduce and study topological properties of pre-limit points, pre-derived sets, preinterior and pre-closure of a set, pre-interior points, pre-border, prefrontier and pre-exterior. The relations between pre-derived set (resp. pre-limit point, pre-interior (point), pre-border, pre-frontier, and preexterior) and α-derived set (resp. α-limit point, α-interior (point), α-border, α-frontier, and α-exterior) are investigatedJun, YB.; Jeong, SW.; Lee, HJ.; Lee, JW. (2008). Applications of pre-open sets. Applied General Topology. 9(2):213-228. https://doi.org/10.4995/agt.2008.18022132289

Adaptive Noise Reduction Algorithm to Improve R Peak Detection in ECG Measured by Capacitive ECG Sensors

Electrocardiograms (ECGs) can be conveniently obtained using capacitive ECG sensors. However, motion noise in measured ECGs can degrade R peak detection. To reduce noise, properties of reference signal and ECG measured by the sensors are analyzed and a new method of active noise cancellation (ANC) is proposed in this study. In the proposed algorithm, the original ECG signal at QRS interval is regarded as impulsive noise because the adaptive filter updates its weight as if impulsive noise is added. As the proposed algorithm does not affect impulsive noise, the original signal is not reduced during ANC. Therefore, the proposed algorithm can conserve the power of the original signal within the QRS interval and reduce only the power of noise at other intervals. The proposed algorithm was verified through comparisons with recent research using data from both indoor and outdoor experiments. The proposed algorithm will benefit a noise reduction of noisy biomedical signal measured from sensors.11Ysciescopu