Machine Learning in Orbit Estimation: a Survey

Since the late '50s, when the first artificial satellite was launched, the number of resident space objects (RSOs) has steadily increased. It is estimated that around 1 Million objects larger than 1 cm are currently orbiting the Earth, with only 30,000, larger than 10 cm, presently being tracked. To avert a chain reaction of collisions, termed Kessler Syndrome, it is indispensable to accurately track and predict space debris and satellites' orbit alike. Current physics-based methods have errors in the order of kilometres for 7 days predictions, which is insufficient when considering space debris that have mostly less than 1 meter. Typically, this failure is due to uncertainty around the state of the space object at the beginning of the trajectory, forecasting errors in environmental conditions such as atmospheric drag, as well as specific unknown characteristics such as mass or geometry of the RSO. Leveraging data-driven techniques, namely machine learning, the orbit prediction accuracy can be enhanced: by deriving unmeasured objects' characteristics, improving non-conservative forces' effects, and by the superior abstraction capacity that Deep Learning models have of modelling highly complex non-linear systems. In this survey, we provide an overview of the current work being done in this field.Comment: submitted to AIAA Journal of Guidance, Control and Dynamic

Gulf Estuarine Research Society 2014 Meeting

Table of Contents: Thank You to Our Sponsors! (p. 3) -- About the Gulf Estuarine Research Society (p. 4) -- Student Travel Award winners (p. 5) -- Abbreviated Schedule (p. 7) -- 2014 Plenary Speaker – Dr. Michael Osland (p. 8) -- 2014 Plenary Speaker – Dr. Maggie Walser (p. 9) -- Full Schedule (p. 10) -- Poster Session Directory (p. 17) -- Oral Presentation Abstracts (p. 21) -- Poster Presentation Abstracts (p. 38) -- Things to Do in Port Aransas (p. 52) -- Greening the Meeting (p. 53) -- Map of University of Texas Marine Science Institute (p. 54)Coastal and Estuarine Research Foundation, Port Aransas, Gulf of Mexico Foundation, Coastal Bend Bays & Estuaries Program, Lotek Wireless Fish & Wildlife Monitoring, Sea Grant Mississippi-Alabama, Sea Grant Louisiana, Sea Grant Texas, The University of Austin Marine Science Institute, Mission-Aransas National Estuarine Research ReserveMarine Scienc

머신러닝 기법을 활용한 기후변화 영향에 따른 재해 리스크 평가

학위논문(박사) -- 서울대학교대학원 : 환경대학원 협동과정 조경학, 2022. 8. 이동근.기후 변화는 우리 세대에게 시급한 위협이다. 자연 재해는 기후 변화로 인해 더 잦은 빈도와 강력하게 발생하고 있어 예측불가성이 커져가고 있다. 특히, 한국의 자연재해는 대부분 기상 현상으로 인해 발생하는데, 지난 10년간 재해로 인한 전체 피해는 주로 태풍(49%)과 호우(40%)에 기인하였다. 따라서 장기적으로 대비하기 위해서는 홍수, 산사태 등 호우와 관련된 위험을 분석하고 평가하는 위험관리가 필요하다. 따라서 본 논문의 주요 연구질문은 다음과 같다: 1) 기후변화로 인한 복잡한 상황에서 다양한 요인을 고려하여 미래의 잠재적 위험을 어떻게 예측할 것인가, 2) 이러한 위험을 줄이기 위해 어떤 노력을 하는 것이 지속가능한가?. 먼저 연안 홍수, 산사태 등 복합적 영향의 미래 위험도를 평가하기 위해 첫째, 최근 연구에서 널리 활용되고 있는 다중 머신러닝(ML) 알고리즘을 확률론적 접근 방식으로 활용하여 현재의 위험도를 분석하였다. 다양한 RCP 기후변화 시나리오 및 지역 기후 모델에 따른 예측 강우량을 고려하여 미래 위험을 추정했습니다. 둘째, 기후변화 영향으로 인한 재난위험 대응을 위한 적응전략의 실효성을 평가하기 위하여, 적응전략으로 중요한 역할을 하는 녹지, 방파제 등 구조적 대책의 효과성과 지속가능성을 여러 적응경로로 나눠 연안침수에 대한 위험저감을 평가하였다. 연구의 결과는 미래의 위험 지역을 식별하고 위험 관리를 위한 의사 결정 과정, 그리고 토지 이용 계획 및 의사 결정 프로세스를 포함한 재난 감소 및 관리 조치에 대해 지원 가능할 것이다.Climate change is an urgent threat to our generation. Natural hazards have become more unpredictable, occurring more frequently and with greater force, due to climate change. Natural disasters in Korea are mostly caused by meteorological events. The total damage caused by disasters in the last ten years is attributed mainly to typhoons (49%) and heavy rain (40%). Therefore, risk management, which analyzes and evaluates hazard risk related to heavy rainfall such as flooding and landslides, is needed to prepare for the long term. Also, effective monitoring and detection responses to climate change are critical for predicting and managing threats to hazard risks. Therefore, the main research questions of this thesis are as follows: 1) How to predict future potential risks in a complex situation due to climate change considering various factors, 2) And what kind of efforts are made to reduce such risks? Is it sustainable? First of all, to assess the future risk of multiple hazards such as coastal flooding, landslide, 1) this study analyzed the present risk by using multiple machine learning (ML) algorithms that have been widely used in recent studies as part of probabilistic approaches, and future risks were estimated by considering the forecasted rainfall according to different representative concentration pathway (RCP) climate change scenarios and regional climate models. Secondly, to evaluate the effectiveness of adaptation strategies to respond to disaster risks posed by climate change impacts, 2) this research analyzed the effectiveness and sustainability of structural measures such as green space and seawall, which are widely used and play an important role as countermeasures against coastal flooding, by dividing into several adaptation pathways. The results of this study identify future at-risk areas and can support decision-making for risk management and can guide disaster reduction and management measures, including land use planning and decision-making processes.Abstract i Chapter 1. Introduction ２ 1. Background ２ 2. Purpose ４ Chapter 2. Prediction of coastal flooding risk under climate change impacts in South Korea using machine learning algorithms ７ 1. Introduction ７ 2. Materials and Method ９ 2.1 Study Area ９ 2.2 Machine learning algorithms １０ 2.3 Method １１ 3. Results １５ 3.1 Comparison of ML algorithms １５ 3.2 Risk probability map １６ 3.3 Future risk under climate change impacts １７ 4. Discussion １８ 4.1 Regional differences １８ 4.2 Significance factor ２０ 4.3 Methodological implications ２１ 5. Conclusions ２２ Chapter 3. Predicting susceptibility to landslides under climate change impacts in metropolitan areas of South Korea using machine learning ２５ 1. Introduction ２５ 2. Materials and Method ２８ 2.1 Study Area ２８ 2.2 Data ２９ 2.3 Landslide factors analysis ３０ 2.4 Machine learning algorithms and validation ３２ 2.5 LSA using different algorithms ３３ 2.6 Predicting landslide susceptibility ３４ 3. Results ３５ 3.1 Multi-collinearity and influencing factor analysis ３５ 3.2 Comparison of machine learning algorithms ３７ 3.3 Predicting landslide susceptibility ３８ 4. Discussion ３９ 4.1 Analysis of results from different ML algorithms ３９ 4.2 Difference in susceptibilities based on land cover type ４０ 5. Conclusions ４１ Chapter 4. Adaptation strategies to future coastal flooding: performance evaluation of green and grey infrastructure in South Korea ４３ 1. Introduction ４３ 2. Materials and Method ４６ 2.1 Study area ４６ 2.2 Data ４７ 2.3 Comparison of machine learning (ML) techniques and coastal flooding risk analysis ４９ 2.4 Evaluation of coastal flooding risk with ASs ５０ 2.5 Potential coastal flooding risk depending on different adaptive pathways ５１ 3. Results ５３ 3.1 Performances of ML algorithms ５３ 3.2 Coastal flooding risk with ASs ５４ 3.3 Potential coastal flooding risk according to different adaptive pathways ５６ 4. Discussion ５９ 4.1 Effect of AS according to spatial characteristics ５９ 4.2 Importance of nature-based solutions as ASs ６２ 5. Conclusion ６３ Chapter 5. Conclusion ６６ Bibliography ７１ Abstract in Korean ８６박

Bayesian Evidence and Model Selection

In this paper we review the concepts of Bayesian evidence and Bayes factors, also known as log odds ratios, and their application to model selection. The theory is presented along with a discussion of analytic, approximate and numerical techniques. Specific attention is paid to the Laplace approximation, variational Bayes, importance sampling, thermodynamic integration, and nested sampling and its recent variants. Analogies to statistical physics, from which many of these techniques originate, are discussed in order to provide readers with deeper insights that may lead to new techniques. The utility of Bayesian model testing in the domain sciences is demonstrated by presenting four specific practical examples considered within the context of signal processing in the areas of signal detection, sensor characterization, scientific model selection and molecular force characterization.Comment: Arxiv version consists of 58 pages and 9 figures. Features theory, numerical methods and four application

DETECTION AND INFERENCE IN GRAVITATIONAL WAVE ASTRONOMY

We explore the detection and astrophysical modeling of gravitational waves de- tected by the Advanced Laser Interferometer Gravitational wave Observatory (LIGO) and Virgo. We discuss the techniques used in the PyCBC search pipeline to discover the first gravitational wave detection GW150914, and estimate the statistical signifi- cance of GW150914, and the marginal trigger LVT151012. During Advanced LIGO’s first observing run there were no detections of mergers from binary neutron star and neutron star-black hole binaries. We use Bayesian inference to place upper limits on the rate of coalescence of these binaries. We use developments made in the PyCBC search pipeline during Advanced LIGO and Virgo’s second observing run to re-analyze Advanced LIGO’s first observing run and re-estimate the statistical significance of LVT151012. We present sufficient evidence to claim LVT151012 as a gravitational wave event. In Advanced LIGO and Virgo’s 2nd observing run a gravitational wave due to the merger of two binary neutron stars, known as GW170817, was discov- ered. We develop tools for Bayesian hypothesis testing so that we can investigate the interior dynamics of neutron stars using the GW170817 signal. Finally, we use Bayesian parameter estimation from PyCBC with tools of Bayesian hypothesis testing to investigate the presence of nonlinear tidal dynamics from a pressure – gravity mode instability in GW170817. We find that significant waveform degeneracies allow the effect of nonlinear tides to be compatible with the data at the level of nonsignificance (Bayes factor of unity). We also investigate further constraints on these nonlinear tides

STATE-AND-TRANSITION MODELING AND ADAPTIVE MANAGEMENT: TOOLS FOR FUTURE CO-DEVELOPMENT OF MADAGASCAR’S NATURAL RESOURCES AND PEOPLE

Madagascar’s terrestrial and aquatic ecosystems have long supported a unique set of ecological communities, many of whom are endemic to the tropical island. Those same ecosystems have been a source of valuable natural resources to some of the poorest people in the world. Nevertheless, with pride, ingenuity and resourcefulness, the Malagasy people of the southwest coast, being of Vezo identity, subsist with low development fishing techniques aimed at an increasingly threatened host of aquatic seascapes. Mangroves, sea grass bed, and coral reefs of the region are under increased pressure from the general populace for both food provisions and support of economic opportunity. Besides purveyors and extractors, the coastal waters are also subject to a number of natural stressors, including cyclones and invasive, predator species of both flora and fauna. In addition, the aquatic ecosystems of the region are undergoing increased nutrient and sediment runoff due, in part, to Madagascar’s heavy reliance on land for agricultural purposes (Scales, 2011). Moreover, its coastal waters, like so many throughout the world, have been proven to be warming at an alarming rate over the past few decades. In recognizing the intimate interconnectedness of the both the social and ecological systems, conservation organizations have invoked a host of complimentary conservation and social development efforts with the dual aim of preserving or restoring the health of both the coastal ecosystems and the people of the region. This paper provides a way of thinking more holistically about the social-ecological system within a resiliency frame of understanding. Secondly, it applies a platform known as state-and-transition modeling to give form to the process. State-and-transition modeling is an iterative investigation into the physical makeup of a system of study as well as the boundaries and influences on that state, and has been used in restorative ecology for more than a decade. Lastly, that model is sited within an adaptive management scheme that provides a structured, cyclical, objective-oriented process for testing stakeholders cognitive understanding of the ecosystem through a pragmatic implementation and monitoring a host of small-scale interventions developed as part of the adaptive management process. Throughout, evidence of the application of the theories and frameworks are offered, with every effort made to retool conservation-minded development practitioners with a comprehensive strategy for addressing the increasingly fragile social-ecological systems of southwest Madagascar. It is offered, in conclusion, that the seascapes of the region would be an excellent case study worthy of future application of state-and-transition modeling and adaptive management as frameworks for conservation-minded development practitioners whose multiple projects, each with its own objective, have been implemented with a single goal in mind: preserve and protect the state of the supporting environment while providing for the basic needs of the local Malagasy people

The role of Artificial Intelligence and distributed computing in IoT applications

[EN]The exchange of ideas between scientists and technicians, from both academic and business areas, is essential in order to ease the development of systems which can meet the demands of today’s society. Technology transfer in this field is still a challenge and, for that reason, this type of contributions are notably considered in this compilation. This book brings in discussions and publications concerning the development of innovative techniques of IoT complex problems. The technical program focuses both on high quality and diversity, with contributions in well-established and evolving areas of research. Specifically, 10 chapters were submitted to this book. The editors particularly encouraged and welcomed contributions on AI and distributed computing in IoT applications.Financed by regional government of Castilla y León and FEDER funds

The role of Artificial Intelligence and Distributed computing in IoT applications

[ES] La serie «El rol de la inteligencia artificial y la computación distribuida en las aplicaciones IoT» contiene publicaciones sobre la teoría y aplicaciones de la computación distribuida y la inteligencia artificial en el Internet de las cosas. Prácticamente todas las disciplinas como la ingeniería, las ciencias naturales, la informática y las ciencias de la información, las TIC, la economía, los negocios, el comercio electrónico, el medio ambiente, la salud y las ciencias de la vida están cubiertas. La lista de temas abarca todas las áreas de los sistemas inteligentes modernos y la informática como: inteligencia computacional, soft computing incluyendo redes neuronales, inteligencia social, inteligencia ambiental, sistemas auto-organizados y adaptativos, computación centrada en el ser humano y centrada en el ser humano, sistemas de recomendación, control inteligente, robótica y mecatrónica, incluida la colaboración entre el ser humano y la máquina, paradigmas basados en el conocimiento, paradigmas de aprendizaje, ética de la máquina, análisis inteligente de datos, gestión del conocimiento, agentes inteligentes, toma de decisiones inteligentes y apoyo, seguridad de la red inteligente, gestión de la confianza, entretenimiento interactivo, inteligencia de la Web y multimedia. Las publicaciones en el marco de «El rol de la inteligencia artificial y la computación distribuida en las aplicaciones IoT» son principalmente las actas de seminarios, simposios y conferencias. Abarcan importantes novedades recientes en la materia, tanto de naturaleza fundacional como aplicable. Un importante rasgo característico de la serie es el corto tiempo de publicación. Esto permite una rápida y amplia difusión de los resultados de las investigaciones[EN] The series «The Role of Artificial Intelligence and Distributed Computing in IoT Applications» contains publications on the theory and applications of distributed computing and artificial intelligence in the Internet of Things. Virtually all disciplines such as engineering, natural sciences, computer and information sciences, ICT, economics, business, e-commerce, environment, health and life sciences are covered. The list of topics covers all areas of modern intelligent systems and computer science: computational intelligence, soft computing including neural networks, social intelligence, ambient intelligence, self-organising and adaptive systems, human-centred and people-centred computing, recommendation systems, intelligent control, robotics and mechatronics including human-machine collaboration, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, web intelligence, and multimedia. The publications in the framework of «The Role of Artificial Intelligence and Distributed Computing in IoT Applications» are mainly the proceedings of seminars, symposia and conferences. They cover important recent developments in the field, whether of a foundational or applicable character. An important feature of the series is the short publication time. This allows for the rapid and wide dissemination of research results