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Quality assurance has been one of the major challenges in laser-based additive manufacturing (AM) processes. This study proposes a novel process modeling methodology for layer-wise in-situ quality monitoring based on image series analysis. An image-based autoregressive (AR) model has been proposed based on the image registration function between consecutively observed thermal images. Image registration is used to extract melt pool location and orientation change between consecutive images, which contains sensing stability information. Subsequently, a Gaussian process model is used to characterize the spatial correlation within the error matrix. Finally, the extracted features from the aforementioned processes are jointly used for layer-wise quality monitoring. A case study of a thin wall fabrication by a Directed Laser Deposition (DLD) process is used to demonstrate the effectiveness of the proposed methodology.

Several calamities occur throughout the world each year, resulting in varying losses. Disasters wreak havoc on infrastructures and impair operation. They result in human deaths and injuries and stress people's mental and emotional states. These negative impacts of natural disasters induce significant economic losses, as demonstrated by the $ 423 billion loss in 2011 in Tohoku, Japan, and the $ 133 billion loss in hurricane Harvey, U.S.A. Every year, hurricanes and tropical storms result in 10,000 human deaths worldwide. To mitigate losses, communities' readiness, flexibility, and resilience must be strengthened. To this end, appropriate techniques for forecasting a community's capacity and functionality in the face of impending crises must be developed and suitable community resilience metrics and their quantification must be established. Collaboration between critical infrastructures such as power systems and emergency services and social networks is critical for building a resilient community. As a result, we require metrics that account for both the social and infrastructure aspects of the community. While the literature on critical infrastructures such as power systems discusses the effect of social factors on resilience, they do not model these social factors and metrics due to their complexity. On the other hand, it turns out that the role of critical infrastructures and some critical social characteristics is overlooked in the computational social science literature on community resilience. Thus, this dissertation presents a multi-agent socio-technical model of community resilience, taking into account the interconnection of power systems, emergency services, and social communities. We offer relevant measures for each section and describe dynamic change and its dependence on other metrics using a variety of theories and expertise from social science, psychology, electrical engineering, and emergency services. To validate the model, we used data on two hurricanes (Irma and Harvey) collected from Twitter, GoogleTrends, FEMA, power utilities, CNN, and Snopes (a fact-checking organization). We also describe methods for quantifying social metrics such as anxiety, risk perception, cooperation using social sensing, natural language processing, and text mining tools. / Doctor of Philosophy / Power systems serve social communities that consist of residential, commercial, and industrial customers. The social behavior and degree of collaboration of all stakeholders, such as consumers, prosumers, and utilities, affect the level of preparedness, mitigation, recovery, adaptability, and, thus, power system resilience. Nonetheless, the literature pays scant attention to stakeholders' social characteristics and collaborative efforts when confronted with a disaster and views the problem solely as a cyber-physical system. However, power system resilience, which is not a standalone discipline, is inherently a cyber-physical-social problem, making it complex to address. To this end, in this dissertation, we develop a socio-technical power system resilience model based on neuroscience, social science, and psychological theories and use the threshold model to simulate the behavior of power system stakeholders during a disaster. We validate our model using datasets of hurricane Harvey of Category 4 that hit Texas in August 2017 and hurricane Irma of Category 5 that made landfall in Florida in September 2017. We retrieve these datasets from Twitter and GoogleTrend and then apply natural language processing and language psychology analysis tools to deduce the social behavior of the end-users.

Community resilience (CR) has been studied as an indicator to measure how well a given community copes with a given disaster and provides policy directions on what aspects of the community should be improved with high priority. Although the impact of the COVID-19 has been serious all over the world and every aspect of our daily life, some countries have handled this disaster better than others. In this thesis, I aim to assess the effect of various news and Tweets collected during the COVID-19 pandemic on community functionality and resilience. First, we measure the community resilience (CR) in five different countries using Tweeter data and investigated how each country shows different trends of the CR, which is measured based on real or fake Tweets. We use Tweets generated in Australia (AUS), Singapore (SG), Republic of Korea (ROK), the United Kingdom (UK), and the United States (US) for Mar.-Nov. 2020 and measured the CR of each country and associated attributes for analyzing the overall trends. In the next step, we scrap and manually clean 4,952 full-text news articles from Jan. 2020 to Jun. 2021 and classify them into real, mixed, and fake news by fact-checking. Then we retrieve Tweets from 42,877,312 Tweets IDs from the same period and classify them into real, mixed, and fake Tweets using machine learning classifiers. We compare CR measured from news articles and Tweets based on three categories, namely, real, mixed, and fake. Based on the news articles and Tweets collected, we quantify CR based on two key factors, community wellbeing and resource distribution. We evaluate community wellbeing by assessing mental wellbeing and physical wellbeing while evaluating resource distribution by assessing economic resilience, infrastructural resilience, institutional resilience, and community capital. Based on the estimates of these two factors, we quantify CR from both news articles and Tweets and analyze the extent to which CR measured from the news articles can reflect the actual state of CR measured from Tweets. / M.S. / The COVID-19 pandemic has severely harmed every aspect of our daily lives, resulting in a slew of social problems. It is critical to accurately assess the current state of community functionality and resilience under this pandemic to recover from it successfully. To accomplish this, various types of social sensing techniques, such as Tweeting and publicly released news, have been employed to understand individuals’ and communities’ thoughts, behaviors, and attitudes during the COVID-19 pandemic. However, some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19. In this thesis, I aim to assess the effect of various news and Tweets collected during the COVID-19 pandemic on community functionality and resilience. First, we measure the community resilience (CR) in five different countries, i.e., Australia (AUS), Singapore (SG), Republic of Korea (ROK), the United Kingdom (UK), and the United States (US), for Mar.-Nov. 2020 and measured the CR of each country and associated attributes for analyzing the overall trends. In the next step, we compare CR measured from news articles and Tweets based on three categories, namely, real, mixed, and fake. We quantify CR based on two key factors, community wellbeing and resource distribution. We evaluate community wellbeing by assessing mental wellbeing and physical wellbeing while evaluating resource distribution by assessing economic resilience, infrastructural resilience, institutional resilience, and community capital.

桌上遊戲從休閒娛樂逐漸融入到學校教育，運用巧妙設計的遊戲機制引發學生遊玩意願，進而在愉悅中學習。數位桌遊，一個透過結合數位科技的優勢輔助學習與娛樂的概念隨著教育型桌遊而崛起；然而從產業、學習、娛樂等角度來思考，數位桌遊究竟應具何特性？其體驗是否良好？學習是否有效？透過這些問題，本研究旨在（1）瞭解桌遊產業與玩家對數位桌遊的需求，（2）設計一款體驗供需法則的數位桌遊，（3）評估數位桌遊的遊戲性與學習效益。 首先，本研究運用體驗式學習圈與建構主義等學習理論設計出桌遊《寶島建設》，接著透過訪談桌遊產業各利害關係人了解產業對數位桌遊的想像與需求，透過彙整訪談內容建立數位桌遊的設計指標，最後本研究投入研發數位桌遊與數據分析系統，用以分析學習者的學習歷程與經驗。 本研究共有32位參與者，在進行遊戲期間會採集參與者的操作行為和遊戲資料作為分析，遊戲後會填寫含有心流經驗和遊戲接受度的問卷，並接受遊戲性與學習內容相關的訪談。實驗結果顯示，參與者普遍對《寶島建設》感到滿意，從競標的數據上顯示參與者逐漸掌握資源的價格區間；所開發的數據分析系統亦能發現參與者未達表現的原因，進而對學習者提出有效建議。 總結，本研究成果為（1）透過訪談瞭解桌遊產業對數位桌遊的需求與想像。（2）設計出能體驗與學習供需法則的數位桌遊《寶島建設》，並獲得遊戲參與者們對遊戲體驗正向的回饋。（3）數據分析系統能透過歷程分析了解學習者的困難與障礙，從數據分析圖表裡也可發現學習者逐漸掌握價格區間，這顯示透過數位桌遊《寶島建設》的競標機制能有效學習掌握需求與價格的關係。 / Board games in Taiwan has risen from leisure and entertainment towards teachings in schools, by introducing fascinating game mechanism and theme to enhance student motivation makes learning more fun. Digital board games, a concept combining the advantages of digital technologies to enhance learning and entertaining arose with the rise of educational board games; however, from the aspect of industry, learning and entertainment, what characteristic should digital board game have? Does it create good experience? Is learning effective? Through these question, this research aims to (1) Understand the visions and needs of industry towards digital board game, (2) Design a digital board game to learn the law of supply & demand, (3) Evaluate the learning effectiveness and gameplay. First, the research uses the experiential cycle and constructism learning theory to design the board game Formosa Construction Ltd, then interview several industrial stakeholders to understand the needs and visions of digital board game, through the interviews concluded a design guidelines, finally the digital version of Formosa Consturction Ltd was built along with the data analysis program use to evaluate user experience and learning portfolio in game. Experiments was conducted with 32 participants, gameplay data are collected during gameplay, participants was asked to fill in a questionnaire with flow experience and acceptance, an interview session regarding gameplay and learning will be held after the questionnaire. Results indicate that participants are satisfy with the game, and data collected from auction showed that participants were progressively mastering the price range; The data analysis program was able to find reasons for participants that did not perform well, having chance to provide advice to learners. In conclusion, the research results are (1) Understand the needs and visions of digital board game through interviewing The Taiwan Board Game Industry. (2) Design Formosa Construction Ltd and obtain positive feedback. (3) The data analysis program showed the obstacles learners met through portfolio analysis, auction data analysis also showed participants was progressively mastering the price range, showing that Formosa Contruction Ltd is effective in learning the relation between needs and price.

Companies analyse large amounts of sensitive data on clusters of machines, using a framework such as Apache Hadoop to handle inter-process communication, and big data analytic tools such as Apache Spark and Apache Flink to analyse the growing amounts of data. Big data analytic tools are mainly tested on performance and reliability. Security and authentication have not been enough considered and they lack behind. The goal of this research is to improve the authentication and security for data analytic tools.Currently, the aforementioned big data analytic tools are using Kerberos for authentication. Kerberos has difficulties in providing multi factor authentication. Attacks on Kerberos can abuse the authentication. To improve the authentication, an analysis of the authentication in Hadoop and the data analytic tools is performed. The research describes the characteristics to gain an overview of the security of Hadoop and the data analytic tools. One characteristic is that the usage of the transport layer security (TLS) for the security of data transportation. TLS usually establishes connections with certificates. Recently, certificates with a short time to live can be automatically handed out.This thesis develops new authentication mechanism using certificates for data analytic tools on clusters of machines, providing advantages over Kerberos. To evaluate the possibility to replace Kerberos, the mechanism is implemented in Spark. As a result, the new implementation provides several improvements. The certificates used for authentication are made valid with a short time to live and are thus less vulnerable to abuse. Further, the authentication mechanism solves new requirements coming from businesses, such as providing multi-factor authenticationand scalability.In this research a new authentication mechanism is developed, implemented and evaluated, giving better data protection by providing improved authentication.