Browsing by Author "Chen, Xi"

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    A Data Mining Framework for Forest Fire Mapping
    (2012-03-29) Karpatne, Anuj; Chen, Xi; Chamber, Yashu; Mithal, Varun; Lau, Michael; Steinhaeuser, Karsten; Boriah, Shyam; Steinbach, Michael; Kumar, Vipin
    Forests are an important natural resource that support economic activity and play a significant role in regulating the climate and the carbon cycle, yet forest ecosystems are increasingly threatened by fires caused by a range of natural and anthropogenic factors. Mapping these fires, which can range in size from less than an acre to hundreds of thousands of acres, is an important task for supporting climate and carbon cycle studies as well as informing forest management. There are two primary approaches to fire mapping: field and aerial-based surveys, which are costly and limited in their extent; and remote sensing-based approaches, which are more cost-effective but pose several interesting methodological and algorithmic challenges. In this paper, we introduce a new framework for mapping forest fires based on satellite observations. Specifically, we develop spatio-temporal data mining methods for Moderate Resolution Imaging Spectroradiometer (MODIS) data to generate a history of forest fires. A systematic comparison with alternate approaches across diverse geographic regions demonstrates that our algorithmic paradigm is able to overcome some of the limitations in both data and methods employed by other prior efforts.
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    A Study of Time Series Noise Reduction Techniques in the Context of Land Cover Change Detection
    (2011-08-12) Chen, Xi; Mithal, Varun; VangalaReddy, Sruthi; Brugere, Ivan; Boriah, Shyam; Kumar, Vipin
    Remote sensing data sets frequently suffer from noise due to atmospheric conditions and instrument issues. This noise negatively affects the usability of these data sets and therefore noise reduction techniques are frequently used to reduce the impact of noise. A well-known remote sensing data set, MODIS Enhance Vegetation Index (EVI), measures the amount of vegetation (based on surface reflectance) observed from satellite. This data set has been used for land cover change detection, in both regional-scale and global-scale studies. Many noise reduction techniques have seen proposed in the remote sensing literature but comparative studies to understand relative performance of these techniques are scarce. Furthermore, the existing comparative studies typically evaluate a small number of techniques on a specific geographical region. Therefore, little is known about the global applicability of these techniques. In addition, time series based land cover change detection algorithms are known to be negatively impacted by the presence of noise. This paper investigates the interrelations of regional noise characteristics, change detection algorithms, and noise reduction methods. The methods for noise reduction are applied in three different geographic regions and through comparison we outline the noise characteristics relevant to the performance of land cover change detection.
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    A Conflict Analysis of Two Chinese Sport Organizations
    (2015-12) Chen, Xi
    Using Robbins and Judges’ (2010) Five Stages Conflict Model, this study investigated the conflict that arose between two Chinese sports organizations regarding the transfer of a professional team. A single case study design was used that utilized interviews to gain an understanding of the causes of the conflict, the processes of conflict, and the outcomes of the conflict. Semi structured phone interviews were conducted with six participants included three individuals associated with each of the two organizations to provide a recollection of the conflict from both perspectives. Using an interview guide participants were asked to describe the causes, process and outcomes of this conflict case. The findings showed that the conflict was caused by lack of communication, incompatible government regulations, goal incompatibility and ambiguity of organizations sector conflicting management strategies were observed between the two organizations in order to fulfill their own intentions including: problem solving, expansion of resources, authoritative command, compromise and bringing in outsiders. In order to resolve the conflict outside actors were needed to manage the conflict. Finally, it is important to analyze the outcomes of the conflict for each party in order to prevent this conflict from occurring again in the future. The findings also showed in order for the two organizations to collaborate harmoniously they should identify a common goal from the beginning. This research concludes that the government should have specifically clarified their regulations for publicly owned basketball teams and the specific sector to which the basketball team belonged should have been identified beforehand. For future collaboration between two or more Chinese sports organizations, the findings of this study may help to develop a model that will prevent conflict and promote harmonious cooperation.
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    Contextual Time Series Change Detection
    (2012-07-23) Chen, Xi; Steinhaeuser, Karsten; Boriah, Shyam; Chatterjee, Snigdhansu; Kumar, Vipin
    Time series are commonly used in a variety of fields, ranging from economics to manufacturing. As a result, time series analysis and modeling has become an active research area in statistics and data mining. In this paper, we focus on a type of change we call contextual time series change (CTC) and propose a novel two-stage algorithm to address it. In contrast to traditional change detection methods, which consider each time series separately, CTC is defined as a change relative to the behavior of a group of related time series. As a result, our proposed method is able to identify novel types of changes not found by other algorithms. We demonstrate the unique capabilities of our approach with several case studies on real-world datasets from the financial and Earth science domains.
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    Inventory and Supply Chain Management with Carbon Emissions
    (2014-07) Chen, Xi
    Although the literature on carbon emissions, from fields such as environmental economics, public policy, and industrial ecology among others, is quite extensive, the literature in supply chain management is relatively limited. Moreover, in addressing concerns about carbon emissions, much of the focus has been on technological fixes (e.g., more carbon-efficient technologies and alternative sources of energy). Much less attention has been paid to the potential of reducing carbon emissions via adjustments in supply chain design and operation. This research, utilizing optimization, game theory, deterministic and stochastic modeling, and mechanism design, aims to bridge this gap. The first part of the study, using the economic order quantity (EOQ) model framework, suggests that it is possible to reduce emissions by modifying order quantities, and provides conditions under which the relative reduction in emissions is greater than the relative increase in cost. The second part examines the extent to which penalizing the emission of harmful pollutants can successfully reduce overall emissions in decentralized supply chains, and shows that requiring each firm to pay for the emissions for which it is directly responsible can paradoxically lead to higher overall supply chain emissions and for this emission to increase in the price of emissions. The third part includes preliminary results including the impact of price variability as well as consumers' preferences for low emission products on the ways firms manage their inventory and the corresponding emissions.
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    Multi-bioactive Peptide Coatings for Dental Implants
    (2014-05) Chen, Xi
    Functionalization of implants with multiple bioactivities is desired to obtain surfaces with improved biological and clinical performance. The outcome of dental implants depends on the process of "racing for the surface". To assist bone cells to win the race, bacterial colonization of the surface and tissue healing promotion around it need to be accomplished soon after implantation. Therefore, functionalizing titanium (Ti) surfaces with bioactive coatings which can either enhance cellular adhesion and differentiation or inhibit bacteria adhesion, or both, is desired. To enhance cellular performance on Ti surface, we developed a simple route to covalently co-immobilize two different oligopeptides on Ti surfaces. Appropriately designed oligopeptides containing either RGD or PHSRN bioactive sequences were mixed and covalently-bonded on CPTES-silanized surfaces. The obtained peptide coatings showed strong mechanical stability as well as enhanced osteoblast adhesion. To prevent bacteria adhesion on Ti surfaces, we tethered using silanes on Ti surface an antimicrobial peptide, GL13K which is derived from human parotid secretory protein. Our previous work demonstrated that, after immobilization, GL13K displayed antimicrobial effect against Porphyromonas gingivalis, a pathogen closely associated with dental peri-implantitis. In addition, GL13K coating showed adequate cyto-compatibility with osteoblasts and human gingival fibroblasts. This work showed that the covalently bonded GL13K coating resisted mechanical, hydrolytic and proteolytic challenges and displayed sustained bioactivity after cycles of body fluid incubation and autoclaving. GL13K coating prevented biofilm formation by killing S.gordonii at their early developmental stage and the antimicrobial effect of GL13K coating was highly dependent on the secondary structure of the tethered peptides. When we investigated the activity of GL13K coating in simulated dynamic conditions with a drip flow bioreactor, unique cell wall damage was observed. The simple and reliable methodology to tether peptides on Ti surface that was developed in this work can be used to establish a multifunctional coating with both bone-regenerative and bacteria inhibitive bioactivities.
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    Online Discovery of Group Level Events in Time Series
    (2014-01-22) Chen, Xi; Mueen, Abdullah; Karampatziakis, Nikos; Bansal, Gagan; Kumar, Vipin
    Recent advances in high throughput data collection and storage technologies have led to a dramatic increase in the availability of high-resolution time series data sets in various domains. These time series reflect the dynamics of the underlying physical processes in these domains. Detecting changes in a time series over time or changes in the relationships among the time series in a data set containing multiple contemporaneous time series can be useful to detect changes in these physical processes. Contextual events detection algorithms detect changes in the relationships between multiple related time series. In this work, we introduce a new type of contextual events, called group level contextual change events. In contrast to individual contextual change events that reflect the change in behavior of one target time series against a context, group level events reflect the change in behavior of a target group of time series relative to a context group of time series. We propose an online framework to detect two types of group level contextual change events: (i) group formation (i.e., detecting when a set of multiple unrelated time series or groups of time series with little prior relationship in their behavior forms a new group of related time series) and (ii) group disbanding (i.e., detecting when one stable set of related time series disbands into two or more subgroups with little relationship in their behavior). We demonstrate this framework using two real world datasets and show that the framework detects group level contextual change events that can be explained by plausible causes.
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    Spin transport and current induced magnetization dynamics in magnetic nanostructures.
    (2010-12) Chen, Xi
    The study of the interaction between conducting electrons and magnetization in a ferromagnet has stimulated much interest following the discovery of the giant magnetoresistive effect two decades ago. With the advance of fabrication techniques at the nanometer length scale, a variety of new magnetic nanostructures have emerged. These structures are interesting from both a scientific and technological perspective. Some of them have successfully led to applications in information storage industry. This thesis theoretically studies some of these structures and focuses on two aspects: (1) the effect of surface roughness in magnetoresistive devices, (2) spin transfer torque induced magnetization dynamics. Surface roughness is known to be an important source of scattering in small structures. We employ Landauer's formalism to study spin dependent electron transport in structures like spin valve, magnetic tunnel junction and nanowires. An efficient algorithm is developed to solve the scattering problem numerically. It is found that the resistivity and magnetoresistance are strongly influenced by the surface roughness scattering. The coupling between spin polarized current and local magnetic moment results in a torque on the magnetization. This induces dynamic effects such as magnetization reversal and switching. We propose an exchange coupled composite structure to study current induced reversal and show that this structure can significantly reduce the critical current. The spin torque can cancel the damping torque and induce steady precession. This type of spin torque oscillator is attractive as a microwave device at the nanoscale. Several of these oscillators can couple together and oscillate in a phase coherent manner. The mechanism for the coupling is studied analytically and using micromagnetic simulation. It is found that the coupling exhibits an oscillatory behavior through a spin wave mediated interaction.
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    Supplement for "Contextual Time Series Change Detection"
    (2013-01-25) Chen, Xi; Steinhaeuser, Karsten; Boriah, Shyam; Chatterjee, Snigdhansu; Kumar, Vipin
    Time series data are common in a variety of fields ranging from economics to medicine and manufacturing. As a result, time series analysis and modeling has become an active research area in statistics and data mining. In this paper, we focus on a type of change we call contextual time series change (CTC) and propose a novel two-stage algorithm to address it. In contrast to traditional change detection methods, which consider each time series separately, CTC is defined as a change relative to the behavior of a group of related time series. As a result, our proposed method is able to identify novel types of changes not found by other algorithms. We demonstrate the unique capabilities of our approach with several case studies on real-world datasets from the financial and Earth science domains.
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    Unsupervised methods to discover events from spatio-temporal data
    (2016-05) Chen, Xi
    Unsupervised event detection in spatio-temporal data aims to autonomously identify when and/or where events occurred with little or no human supervision. It is an active field of research with notable applications in social, Earth, and medical sciences. While event detection has enjoyed tremendous success in many domains, it is still a challenging problem due to the vastness of data points, presence of noise and missing values, the heterogeneous nature of spatio-temporal signals, and the large variety of event types. Unsupervised event detection is a broad and yet open research area. Instead of exploring every aspect in this area, this dissertation focuses on four novel algorithms that covers two types of important events in spatio-temporal data: change-points and moving regions. The first algorithm in this dissertation is the Persistence-Consistency (PC) framework. It is a general framework that can increase the robustness of change-point detection algorithms to noise and outliers. The major advantage of the PC framework is that it can work with most modeling-based change-point detection algorithms and improve their performance without modifying the selected change-point detection algorithm. We use two real-world applications, forest fire detection using a satellite dataset and activity segmentation from a mobile health dataset, to test the effectiveness of this framework. The second and third algorithms in this dissertation are proposed to detect a novel type of change point, which is named as contextual change points. While most existing change points more or less indicate that the time series is different from what it was before, a contextual change point typically suggests an event that causes the relationship of several time series changes. Each of these two algorithms introduces one type of contextual change point and also presents an algorithm to detect the corresponding type of change point. We demonstrate the unique capabilities of these approaches with two applications: event detection in stock market data and forest fire detection using remote sensing data. The final algorithm in this dissertation is a clustering method that discovers a particular type of moving regions (or dynamic spatio-temporal patterns) in noisy, incomplete, and heterogeneous data. This task faces two major challenges: First, the regions (or clusters) are dynamic and may change in size, shape, and statistical properties over time. Second, numerous spatio-temporal data are incomplete, noisy, heterogeneous, and highly variable (over space and time). Our proposed approach fully utilizes the spatial contiguity and temporal similarity in the spatio-temporal data and, hence, can address the above two challenges. We demonstrate the performance of the proposed method on a real-world application of monitoring in-land water bodies on a global scale.