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Publication Qualitative and quantitative analysis of sedimentary characteristic variability in tide-dominated and -influenced deltas(Colorado School of Mines. Arthur Lakes Library, 2024)This thesis collected sedimentological information such as sedimentary structures, biological indicators, erosional information, grain size distribution, etc., from articles documenting tide-dominated and -influenced deltas. For an article to be included it must directly interpret a tide-dominated or tide-influenced delta, separate the information into subenvironments, and present sedimentological information to a degree of completeness. This information was recorded into a dataset and analyzed to present a quantitative description of the reported variability in tide-dominated and -influenced deltas throughout the literature. The results show little consistency in the reported sedimentary characteristics for each subenvironment, but some sedimentary structures and tidal indicators are reported consistently somewhere in of tide-dominated and -influenced deltas. The results of this thesis can be referenced by other researchers when interpreting tide-dominated and -influenced deltas. The results also lead to suggestions on what should be added to future tide-dominated and -influenced delta models to aid researchers in the field. The end of this paper consists of suggestions based on lessons I learned throughout this thesis for researchers that would like to conduct a similar study. I then suggest plans to create a quantitative “grand database” comprised of sedimentological information for all interpreted outcrops. This “grand database” provides data for accurate quantitative analysis of sedimentological data.Publication Social robot interaction design to mitigate risk in sensitive and adverse contexts(Colorado School of Mines. Arthur Lakes Library, 2024)To be successful and acceptable, social robots must demonstrate social competence, navigate sensitive situations, and react to adverse events. Designing robot behaviors for these interactions is challenging because poor robot responses risk harming humans’ dignity and well-being. This dissertation explores how social robots can be designed to effectively and appropriately respond to adverse or sensitive social interactions in positive ways that minimize risk to users’ well-being. Chapter 2 begins by exploring an instance in which social robots are already used in the wild for potentially sensitive interactions— the use of teleoperated socially assistive robots in education, therapy, and telehealth for children. This work demonstrates the advantages of human oversight in this domain by identifying users’ existing strategies to mitigate the social and emotional risks of child-robot interaction. It then presents design recommendations summarizing how roboticists can develop tools that support users’ ability to prepare for and adapt to unforeseen situations. Chapters 3 and 4 evaluate interaction design for autonomous robots in adverse interactions involving norm violations, such as unethical commands or hate speech. Chapter 3 explores how people appraise these interactions and investigates why they may prefer a robot to intervene or abdicate from responding to adverse events. Chapter 4 furthers this work through an empirical evaluation of robots’ use of human-like linguistic politeness cues to address unethical commands. It presents a framework delineating how robots could use human-like cues to effectively and appropriately address adverse interactions while avoiding negative perceptions. This work also reemphasizes broader concerns about the extent to which robots should be able to perceive and react to such scenarios. Overall, this dissertation makes empirical and design contributions to the field of HRI that inform how social robots can preserve humans’ dignity and well-being in adverse interactions. It argues that these contexts require roboticists to recognize factors outside of individual human-robot interactions— including the experiences of secondary stakeholders and bystanders, existing sociocultural norms of collaboration and conflict, and the potential for ill use of robots’ capabilities.Publication Application of predictive blasting model to improve grade control and optimize blast value(Colorado School of Mines. Arthur Lakes Library, 2024)Each block in the geological block model has an economic value. The block economic value (BEV) distinguishes economic (ore) and uneconomic blocks (waste). A block will be economic to mine if its BEV is positive and uneconomic if it is negative, assuming there are no waste blocks on top of it. Reconciliation is a process to verify the resource model against actual production data. The grade control model as an important part of reconciliation process is a model developed for daily ore/waste selection to determine where each material mined should go. It is crucial to avoid sending the block of material to the wrong destination as the consequences can be overall ore loss and dilution which lead to loss of value. Blasting is a process of breaking rock mass using explosives. Blasting can break a large amount of rocks at a low cost. However, regardless of how well-controlled the blasting is, rock displacement will occur due to the forces applied. The blasting for rock breakage will result in movement of ore and waste blocks in the grade control block model from their pre blast positions into new post blast positions. This can affect the definition and accuracy of the ore and waste boundaries used for grade control within the resulting post blast muck pile compared to pre blast definition of ore and waste boundaries. Accurate definition of precise post blast grade control polygons is vital for the economics of any mine. The vital question is, "What is the impact of blasting on grade control value and how can the most value be captured by considering the probable changes caused by the blast?" Orica developed OREPro™ 3D Predict ("The Predictive Model"), a real-time blast movement prediction model that uses complex physics algorithms to predict rock movement and the post blast muckpile in minutes allowing one to be able to predict the post blast location of individual blocks within the grade control block model. The Predictive Model utilizes these new block locations within the grade control polygon optimizer tool to generate post blast grade control polygons that maximize value of the extracted resource before initiating the blast. This thesis investigates and evaluates the potential application of the Predictive Model to improve grade control model and optimize blast value. Additionally, variables were identified and reviewed when utilizing the Predictive Model in various blasting conditions.Publication Data-driven cyber-physical energy systems(Colorado School of Mines. Arthur Lakes Library, 2024)Distributed solar energy resources (DSERs) in smart grid systems are rapidly increasing due to the steep decline in solar module prices. Smart cities, utilities, and government agencies are having pressure on managing stochastic power generation with this distributed solar energy penetration, such as predicting and reacting to the variation in the smart grid. Recently, there is a rising interest in solar energy analytics to improve the efficiency of DSERs management. Unfortunately, DSERs management is still suffering from low efficiency and low fairness. For instance, DSERs performance models are not very accurate and cannot detect damage or degradation conditions accurately. The energy sharing among DSERs is suffering low efficiency and low fairness. To address these problems, this dissertation presents data-driven cyber-physical energy systems that leverage data analytics techniques to improve the efficiency of DSERs management. My insight is that the smart grid ecosystem produces large volumes of data, which can be analyzed to improve DSERs management and CPS system sustainability. Building on this insight, I make the following contributions: DSERs Identification Using Big Satellite Imagery Data. I design a new approach that can automatically detect distributed solar arrays in a given geospatial region (such as location and radius, zip code, or county name) without any extra cost. Rather than using Very High Resolution(VHR) aerial images, I leverage regular or low-resolution imagery datasets. I designed a two-step identification method to combine the benefits from both machine learning modelings and deep learning approaches. This dissertation shows that my SolarFinder can detect distributed solar arrays accurately. However, prior DSER detection approaches, including SolarFinder, are still suffering low accuracy due to insufficient sample and feature learning. To further improve the detection performance, I design a new automatic system that can accurately detect and profile distributed solar photovoltaic arrays in a given region without any extra cost. I leverage multiple data augmentation techniques (e.g., CycleGAN, latent diffusion models, and Generative Adversarial networks) to build a large rooftop satellite imagery dataset (RSID) to learn more samples and features. This dissertation shows that my SolarDetector can accurately and reliably report multi-panel solar deployments. Peer-to-Peer Solar Energy Sharing and Trading. To mitigate the impact of the intermittent DSERs while also benefiting from distributed generation for more reliable and profitable grid management, I design a new solar energy trading system that can enable unsupervised, distributed, scalable, and long-term fair solar energy trading in residential Virtual Power Plants(VPPs). I leverage a new multi-agent reinforcement learning approach that enables peer-to-peer solar energy trading among different DSERs to ensure that both the DSER users and the VPPs maximize benefit. The dissertation shows that SolarTrader is capable of enabling its users to trade solar energy with their neighbors more efficiently to achieve optimal monetary benefits while empowering the VPP with reliable, online, distributed DSERs management. DSERs Profiling and Evaluation. Homeowners may have to spend up to $375 to diagnose their damaged rooftop solar PV system. To help inspect potential damage on solar PV arrays automatically and passively, I design a new system that automatically detects and profiles damages on rooftop solar PV arrays using their rooftop images at a lower cost. The dissertation shows that SolarDiagnostics can detect damaged solar PV arrays accurately. For DSERs identification, I plan to expand to solar farms and smart and connected communities. For peer-to-peer energy trading, I plan to deploy a prototype of SolarTrader in two community-shared solar PV systems. I also plan to design new policies and expand the current DSER agent definition to abstract and integrate more DSERs into SolarTrader, such as EVs, utility battery banks, and wind-generated energy.Publication Electronic and mechanical scanning-probe based characterization of multi-phase composite silicon anodes for lithium-ion batteries(Colorado School of Mines. Arthur Lakes Library, 2024)Silicon anodes for lithium ion batteries offer promising improvements in energy density due to their high theoretical capacity. However, silicon undergoes a large volumetric expansion when it lithiates, resulting in mechanical damage that renders active material electrically isolated and results in an unstable solid electrolyte interphase (SEI), which then causes unwanted electrolyte reduction. These issues contribute to poor cycling in silicon anodes. Many approaches are utilized to mitigate the problems resulting from silicon’s expansion, including alloyed materials, coatings on the silicon, modified processing steps, and composite, multiphase electrodes. The results in this thesis demonstrate electrical and mechanical measurements on several different silicon electrode systems to explore and characterize the microstructure, phase distribution, mechanical property, and electrical property changes that accompany different mitigation strategies. The techniques used—scanning spreading resistance microscopy (SSRM) and contact resonance/force volume force microscopy (CR-FV)—are scanning probe-based techniques that are both powerful and underutilized for composite electrode research. This work demonstrates the use of SSRM to: (i) study the SEI, (ii) understand how thermal processing impacts electrode resistivity, (iii) identify individual components of electrodes and analyze their distribution, and (iv) study how the binder used in the electrode is impacted by cycling. The use of CR-FV to study composite electrodes is also demonstrated for the first time, providing previously unrealized insight into mechanical changes based on morphology and cycling in composite silicon anodes.