Broken Art

My “Broken Art” series created between 2013 and 2014 is the subject of my Thesis. My study is focused on three characteristics: Semiotics (signs, symbols, and indices explore artwork in a series that is broken). Great changes in art history between two art movements, Abstract Expressionism and Pop Art are synthesized in one artwork. Finally, commercialism is dealt with through symbols which I construct. The following scholars and artists are discussed in relation to my series: Charles Sanders Pierce (1839~1914, American), Rene Magritte (1898~1967, Belgian), Rosy Keyser (1974~, Baltimore, MD), Roy Lichtenstein (1923~1997, American), and Barry Le Va (1941, Long Beach, CA). Their artwork in relation to my series is discussed in this dissertation, and was exhibited at the Bevier Gallery, Booth Building, at R.I.T. during the spring of 2014

A liquid lens based on electrowetting

A primary goal of this work is to develop and characterize a novel liquid lens based on electrowetting. A droplet of silicone oil confined in an aqueous solution works as a lens. Electrowetting then controls the shape of the confined silicone oil, and the focal length may be varied upon an applied electric potential. The planar lens design is employed for easy integration of a lens system into a microfluidic device. The achievements of this work are to develop an electrowetting-based planar liquid lens without an off-the-plane electrode structure and to demonstrate the planar liquid lens with variable focal length control. Electrowetting has recently become popular in many applications including a liquid lens. However, reported liquid lenses based on electrowetting had a limitation of integration onto a lab-on-a-chip system due to their off-the-plane electrode structures. In order to overcome the structural issue, a planar liquid lens is proposed in this thesis. A silicone oil droplet confined in an aqueous solution acts as a lens material. Planar ring-type electrodes control the confinement of silicone oil by electrowetting. With an applied potential, the surface above the ring-type electrodes becomes hydrophilic and attracts the surrounding aqueous solution making the confined silicone oil more curved. By charging the curvature of the lens, the focal length can be controllable. As the lens is in a planar shape, it will be simple to integrate the planar lens on a microfluidic system. The lack of a vertical wall requirement in the demonstrated liquid lens eliminates the limitation of the integration issue for lab-on-a-chip systems. In addition, due to the controllable variable focal length of this lens, it is applicable to various optical applications which need an integrated and controllable lens

Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks

We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The system aims at delivering contents on demand with minimal average latency from a time-varying library of popular contents. Information about uncached requested files can be transferred from the cloud to the eRRHs by following either backhaul or fronthaul modes. The backhaul mode transfers fractions of the requested files, while the fronthaul mode transmits quantized baseband samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the eRRHs to be updated, which may lower future delivery latencies. In contrast, the fronthaul mode enables cooperative C-RAN transmissions that may reduce the current delivery latency. Taking into account the trade-off between current and future delivery performance, this paper proposes an adaptive selection method between the two delivery modes to minimize the long-term delivery latency. Assuming an unknown and time-varying popularity model, the method is based on model-free Reinforcement Learning (RL). Numerical results confirm the effectiveness of the proposed RL scheme.Comment: 5 pages, 2 figure

The Impact of Different Types of Media on Tourists\u27 Behavioral Intentions

The primary purpose of this study was to examine how much different types of media affect a tourist’s decision when choosing a destination to travel. Further, this study attempted to investigate the impact of the different types of media on a tourist’s behavioral intentions. A primary field survey was designed for this study to collect data and multivariate analysis of variance (MANOVA) was performed to analyze the data and test the hypotheses. As a result, the most influential media form for choosing a destination to travel was social media, while brochure ranked the last. Subsequently, demographic factors showed noticeable propensities for the different types of media. Lastly, media types had a significant impact on three behavioral intentions for traveling as well. Study findings are expected to provide valuable information to better utilize the media as a marketing tool for the tourism industry

Computing Algorithm for an Equilibrium of the Generalized Stackelberg Game

The $1-N$ generalized Stackelberg game (single-leader multi-follower game) is intricately intertwined with the interaction between a leader and followers (hierarchical interaction) and the interaction among followers (simultaneous interaction). However, obtaining the optimal strategy of the leader is generally challenging due to the complex interactions among the leader and followers. Here, we propose a general methodology to find a generalized Stackelberg equilibrium of a $1-N$ generalized Stackelberg game. Specifically, we first provide the conditions where a generalized Stackelberg equilibrium always exists using the variational equilibrium concept. Next, to find an equilibrium in polynomial time, we transformed the $1-N$ generalized Stackelberg game into a $1-1$ Stackelberg game whose Stackelberg equilibrium is identical to that of the original. Finally, we propose an effective computation procedure based on the projected implicit gradient descent algorithm to find a Stackelberg equilibrium of the transformed $1-1$ Stackelberg game. We validate the proposed approaches using the two problems of deriving operating strategies for EV charging stations: (1) the first problem is optimizing the one-time charging price for EV users, in which a platform operator determines the price of electricity and EV users determine the optimal amount of charging for their satisfaction; and (2) the second problem is to determine the spatially varying charging price to optimally balance the demand and supply over every charging station.Comment: 37 pages, 10 figure

Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images

Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images remains. When there is a domain gap between the training and test datasets, CNN-based segmentation models trained by a training dataset fail to segment buildings for the test dataset. In this paper, we propose segmentation networks based on a domain adaptive transfer attack (DATA) scheme for building extraction from aerial images. The proposed system combines the domain transfer and adversarial attack concepts. Based on the DATA scheme, the distribution of the input images can be shifted to that of the target images while turning images into adversarial examples against a target network. Defending adversarial examples adapted to the target domain can overcome the performance degradation due to the domain gap and increase the robustness of the segmentation model. Cross-dataset experiments and the ablation study are conducted for the three different datasets: the Inria aerial image labeling dataset, the Massachusetts building dataset, and the WHU East Asia dataset. Compared to the performance of the segmentation network without the DATA scheme, the proposed method shows improvements in the overall IoU. Moreover, it is verified that the proposed method outperforms even when compared to feature adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure

From Concept to Impact: Beginning with the End in Mind Highlights of the 2015 Cornell Hospitality Entrepreneurship Roundtable

Among the many topics that directly affect entrepreneurs’ success are these five: franchising, funding, technology, opportunity recognition, and legal arrangements. The inaugural roundtable convened by The Leland C. and Mary M. Pillsbury Institute for Hospitality Entrepreneurship at the School of Hotel Administration at Cornell University addressed these five topics, with a goal of supporting hospitality entrepreneurs from concept to impact. The institute provides a springboard for hospitality entrepreneurs, particularly students at the School of Hotel Administration

Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks

We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The system aims at delivering contents on demand with minimal average latency from a time-varying library of popular contents. Information about uncached requested files can be transferred from the cloud to the eRRHs by following either backhaul or fronthaul modes. The backhaul mode transfers fractions of the requested files, while the fronthaul mode transmits quantized baseband samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the eRRHs to be updated, which may lower future delivery latencies. In contrast, the fronthaul mode enables cooperative C-RAN transmissions that may reduce the current delivery latency. Taking into account the trade-off between current and future delivery performance, this paper proposes an adaptive selection method between the two delivery modes to minimize the long-term delivery latency. Assuming an unknown and time-varying popularity model, the method is based on model-free Reinforcement Learning (RL). Numerical results confirm the effectiveness of the proposed RL scheme.Comment: 12 pages, 2 figure

Factors Influencing the Acceptance of Distributed Research Networks in Korea: Data Accessibility and Data Security Risk

Objectives Distributed research networks (DRNs) facilitate multicenter research by enabling the use of multicenter data; therefore, they are increasingly utilized in healthcare fields. Despite the numerous advantages of DRNs, it is crucial to understand researchers’ acceptance of these networks to ensure their effective application in multicenter research. In this study, we sought to identify the factors influencing the adoption of DRNs among researchers in Korea. Methods We used snowball sampling to collect data from 149 researchers between July 7 and August 28, 2020. Five factors were used to formulate the hypotheses and research model: data accessibility, usefulness, ease of use, data security risk, and intention to use DRNs. We applied a structural equation model to identify relationships within the research model. Results Data accessibility and data security were critical to the acceptance and use of DRNs. The usefulness of DRNs partially mediated the relationship between data accessibility and the intention to use DRNs. Interestingly, ease of use did not influence the intention to use DRNs, but it was affected by data accessibility. Furthermore, ease of use impacted the perceived usefulness of DRNs. Conclusions This study highlighted major factors that can promote the broader adoption and utilization of DRNs. Consequently, these findings can contribute to the expansion of active multicenter research using DRNs in the field of healthcare research