Recent Submissions

  • NATURALISTIC DRIVING DATABASE DEVELOPMENT AND ANALYSIS OF CRASH AND NEAR-CRASH TRAFFIC EVENTS IN HONOLULU

    Pereira, Luana Carneiro; Prevedouros, Panos (2021-09-30)
    Dashboard cameras and sensors were installed in 233 taxi vans on Oahu, Hawaii which produced several hours of events classified as naturalistic driving data (NDD) in a period of seven months between fall 2019 and spring 2020. The study achieved its objectives to: (1) collect data from NDD events where driving maneuvers caused an acceleration of 0.5g or higher; (2) develop a database suitable for statistical analysis; (3) derive basic statistics for all variables; (4) investigate correlations between variables; and (5) further investigate correlations (which may represent causality effects) for the most frequent types of events, using stepwise linear regression models. The database included a total of 402 harsh events, of which were 398 near-crashes and four were crashes. Several variables such as road, environmental, driver and vehicle characteristics were coded for each event. The installation of Samsara by the CTL company proved to be a successful tool for coaching drivers, and for providing useful insights into traffic safety factors relating to near-miss events.
  • Extracting Rural Crash Injury and Fatality Patterns Due to Changing Climates in RITI Communities Based on Enhanced Data Analysis and Visualization Tools (Phase I)

    Zhang, Guohui; Prevedouros, Panos; Ma, David; Yu, Hao; Li, Zhenning; Yuan, Runze (2021-09)
    Traffic crashes cause considerable incapacitating injuries and losses in Rural, Isolated, Tribal, or Indigenous (RITI) communities. Compared to urban traffic crashes, those rural crashes, especially for those occurred in RITI communities, are heavily associated with factors such as speeding, low safety devices application (for instance, seatbelt), adverse weather conditions and lacking maintenance and repairers for road conditions, inferior lighting conditions, and so on. Therefore, there exists an urgent need to investigate the unique attributes associated with the RITI traffic crashes based on numerous approaches, such as statistical methods, and data-driven approaches. This project focused on extracting rural crash injury and fatality patterns due to changing climates in RITI communities based on enhanced data analysis and visualization tools. Three new interactive graphic tools were added to the Rural Crash Visualization Tool System (RCVTS), to enhance the visualization approach. A Bayesian vector auto-regression based data analysis approach was proposed to enable irregularly-spaced mixture-frequency traffic collision data interpretation with missing values. Moreover, a finite mixture random parameters model was formulated to explore driver injury severity patterns and causes in low visibility related single-vehicle crashes. The research findings are helpful for transportation agencies to develop cost-effective countermeasures to mitigate rural crash severities under extreme climate and weather conditions and minimize the rural crash risks and severities in the States of Alaska, Washington, Idaho, and Hawaii.
  • BUILDING CAPACITY FOR CLIMATE ADAPTATION Assessing the Vulnerability of Transportation Infrastructure to Sea Level Rise for Safety Enhancement in RITI Communities

    Shen, Suwan; Shim, Dayea (2021-09-01)
    Sea level rise (SLR) and more frequent extreme weather events are an emerging concern for transportation infrastructures in coastal areas. In particular, the livelihoods and transportation safety of vulnerable populations such as indigenous rural communities may be at higher risk to sea-level rise and exacerbated coastal flooding due to their heavy dependence on natural resources, settlements in relatively isolated fringe land, limited accessibility to services, and alternative economic activities, as well as lack of resources and tools for adaptation. Despite existing studies on sea-level rise’s impacts, there is a lack of understanding of how the impacts of tidal flooding and sea-level rise may be unevenly distributed both spatially and socially, and how vulnerable (e.g. rural, relatively isolated) communities have experienced such impacts and perceive future risks. Using survey data, this project helps to better understand the current experience and risk perception of different communities when facing sea-level rise and more frequent coastal flooding. It helps to understand different communities’ perceived travel challenges with coastal flooding, the social sensitivity to different types of challenges, and the priorities and concerns to access various types of resources with the projected sea-level rise. The findings could be used to develop adaptation strategies that improve communities’ safe access to highly valued resources and activities.
  • DEVELOPMENT OF GRASS-ROOTS DATA COLLECTION METHODS IN RURAL, ISOLATED, AND TRIBAL COMMUNITIES

    Chang, Kevin; Hodgson, Cody (2021-07-05)
    While extensive procedures have been developed for the collection and dissemination of motor vehicle volumes and speeds, these same procedures cannot always be used to collect pedestrian data, given the comparably unpredictable behavior of pedestrians and their smaller physical size. There is significant value to developing lower cost, lower intrusion methods of collecting pedestrian travel data, and these collection efforts are needed at the local or “grass-roots” level. While previous studies have documented many different data collection methods, one newer option considers the use of drones. This study examined its feasibility to collect pedestrian data and used this technology as part of a school travel mode case study. Specific information with regard to the study methodology, permissions required, and final results are described in detail as part of this report. This study concluded that while purchasing and owning a drone requires relatively minimal investment, the initial steps required to operate a drone, along with processing time required to analyze the data collected, represent up-front barriers that may prevent widespread usage at this time. However, the use of drones and the opportunities that it presents in the long-term offer promising outcomes.
  • Investigation of Drone Applications to Improve Traffic Safety in RITI Communities

    Ban, Xuegang (Jeff); Abramson, Daniel; Zhang, Yiran; Cano-Calhoun, Cristina (2021-06-30)
    Transportation and traffic safety is a primary concern among the Rural, Isolated, Tribal, or Indigenous (RITI) communities in the U.S. Although emerging technologies (e.g., connected and autonomous vehicles, drones) have been developed and tested in addressing traffic safety issues, they are often not widely shared in RITI communities for various reasons. This research aims to explore, understand, and synthesize the opportunities and challenges of applying drone technologies to alleviate or resolve traffic safety and emergency related issues within RITI communities. The project team first sent out online surveys to communities on the outer Pacific coast of Washington State and selected the City of Westport as the study area based on the feedback. A pilot study using drones for mapping and sensing in Westport was then conducted, followed by two community meetings to explore potential drone applications. With the three outreach activities, it was found that the current need in the communities was education on drones, including training for remote pilot certification (drone license) and drone operations. Findings of this research will help guide the project team to set up specific drone-related programs in the Westport area in future research.
  • ROADS AND AIRFIELDS CONSTRUCTED ON PERMAFROST: A Synthesis of Practice

    Connor, Billy; Goering, Douglas J.; Kanevskiy, Mikhail; Trochim, Erin; Bjella, Kevin L.; McHattie, Robert L. (2020-12)
    This synthesis provides the practicing engineer with the basic knowledge required to build roadway and airports over permafrost terrain. Topic covered include an overview of permafrost, geotechnical investigations, slope stability, impacts of climate, and adaptation strategies during the design, construction and maintenance phases. The purpose of the synthesis is not to provide a comprehensive body of knowledge or to provide a complete how‐to manual. Rather the synthesis provides a working knowledge for those working in permafrost regions such that the practicing engineer will be able to work with subject matter experts to obtain the desired project outcomes.
  • Measuring the impact of cooperative rewards on AI

    Harmon, Dain; Lawlor, Orion S.; Chappell, Glenn G.; Metzgar, Jonathan B. (2020-12)
    We consider the effects of varying individualistic and team rewards on learning for a Deep Q-Network AI in a multi-agent system, using a synthetic team game ‘Futlol’ designed for this purpose. Experimental results with this game using the OpenSpiel framework indicate that mixed reward structures result in lower win rates. It is unclear if this is due to faster learning on simpler reward structures or a flaw in the nature of the reward system.
  • Review and case study of electric submersible pump performance with dispersions

    Ellexson, Dexter Bryant; Awoleke, Obadare; Ning, Samson; Dandekar, Abhijit (2020-12)
    Centrifugal pump performance is very sensitive to fluid viscosity, gas fraction, and flow pattern in impeller channels. Viscous oil reduces the head and rate capacity of the pump. High gas fraction reduces the head capacity of the pump at high rates and leads to unstable surging at low rates. If the flow pattern in the impeller transitions to an elongated bubble the pump can gas-lock causing loss of production and excessive heat buildup. The complex geometry and 3-dimensional flow in a pump stage make the analysis of flow in a pump difficult without simplifying assumptions. Empirical and mechanistic models have been developed for correcting pump performance for viscosity, gas fraction, and predicting flow pattern within the impeller with reasonable accuracy. Difficulties arise when produced fluids form stable dispersions. Foams, emulsions, and solid suspensions make the determination of viscosity, gas separation efficiency, and flow pattern more difficult. Interfacial properties between phases become important in determining the bulk fluid properties, and the presence of surfactants exacerbates the interfacial effects. The objective of this project is to describe the fundamentals of electrical submersible centrifugal pumps, ESPs, and the effects that produced fluids have on their performance. These findings are then used to evaluate a case study of an ESP installed in a well with foamy and viscous crude. The ESP exhibits reduced head and rate compared to predicted viscous and gas corrections. Including interfacial effects on the fluid viscosity allow a satisfactory performance match of pump performance to be achieved. The effect of foam on pump performance can be attributed to the increased viscosity exhibited when gas behaves as a dispersed phase in a continuous oil phase rather than a separate phase in a mixture.
  • Automated remote security scoring engine (ARSSE): gamification of cyber security education

    Chauhan, Arsh; Lawlor, Orion S.; Hartman, Christopher M.; Metzgar, Jonathan B. (2020-12)
    The goal of this project is to create an easy to use, extensible, and engaging method to compute scores interactively during a practical cyber security education. Gamification has been shown to be an effective teaching tool and has been used in the offensive cybersecurity education space (via Capture The Flag competitions and challenges such as hackthebox.eu) but there has not been an open-source effort to bring this idea to the defensive side (blue team) aspect of cybersecurity. The Automated Remote Security Scoring Engine (ARSSE, pronounced "Arsh") uses a combination of well maintained open-source tools and custom connectors to facilitate an easy to use, scalable, and secure system to check the state of a computer system against a desired state and award points based on passed checks. ARSSE has been released to the public with the hope that it will fill a gap in training the next generation of information security professionals.
  • Open-pit slope geotechnical considerations and its effects on mine planning

    Enkhbayar, Bayasgalan; Chen, Gang; Ahn, Il Sang; Arya, Sampurna (2020-08)
    In open-pit mining, a stable pit slopes design is essential for safe operation and economic performance of the mine. However, a steeper pit is more desirable from an economic standpoint due to reduced overburden removal. As the mine deepens, the open-pit walls become increasingly prone to slope failure, which causes human and economic losses. Therefore, a feasible and stable slope mine design requires a serious geotechnical investigation. The optimization of this design requires steepening the overall slope angle as much as possible while maintaining mine safety for efficient and effective mining operations. The open-pit slope geotechnical investigation calls for detailed geological and geotechnical data and advanced numerical modeling. In this study, geological and geotechnical data are collected from the Erdenet Copper Mine of Mongolia. The collected information includes data from discontinuity face mapping, geotechnical core logging, groundwater condition, geological exploration cross-sections, pit map, and rock property lab test results. The open-pit slope stability is analyzed with geotechnical numerical modeling software FLAC2D, and the variation and distribution of factors of safety (FOS) are computed and studied. The stability of Erdenet mine’s North-West open-pit is simulated by dividing the pit into ten representative cross-sections, and subsequently, FOS is calculated for each cross-section. The simulation results show that each cross-section has a higher overall FOS value than the allowable mine FOS, set at 1.5 with an earthquake magnitude of 0.165g peak ground acceleration (PGA). However, the localized high shear strain on individual benches may still occur, which can cause potential failures. Parametric studies indicate that changes in the bench angles and rock mass properties will have various degrees of impact on pit slope FOS. The effect of bench angle changes appears to be more significant. The study of pit slope design on mine planning shows that a 1° increase on slope angle will reduce excavation volume by 5 M m3 and save $15 million in excavation cost, but will also reduce FOS by 0.12. Engineering judgment and decision will have to be made regarding this tradeoff for a safe and economical mining operation. Practice and analysis indicate that the computer simulation alone is not sufficient to ensure the accurate estimation of slope stability. It is recommended to use a combination of slope monitoring and computer simulation to provide verification against each other to detect any potential hazards in mine. Mine pit slope movement monitoring program setup and monitoring procedure are analyzed and proposed in this study. The above findings allow mining engineers to optimally design pit slopes under the given geotechnical conditions and minimize the risk of slope failures while improving the stripping ratio and enhancing production profit.
  • Ugnu pilot area - simulation model and sensitivity analysis

    Wooster, Arin J.; Dandekar, Abhijit; Ning, Samson; Zhang, Yin (2020-05)
    Collaborating with Hilcorp Alaska, LLC, the Ugnu pilot area is the subject of this project. Hilcorp Alaska is conducting field pilot test at Milne Point Field to prove commerciality with Ugnu heavy oil as well as an on-going Milne viscous oil polymer flood field pilot test in the Schrader Bluff sands. The Ugnu sand heavy oil represents much of the heavy oil on Alaska’s North Slope and has potential for future development. Typical heavy oil has a viscosity of 1,000 - 10,000 centipoise, approximately akin to viscosities of honey and molasses, respectively. North Slope heavy oil is located around 3,000-foot depths and typically overlays existing fields. The project involves a reservoir simulation model and sensitivity analysis to support developmental drilling plans from a Milne Point Unit pad. Necessary geologic and reservoir properties were provided for usage in this project by Hilcorp. Production data was provided for history matching. Field geologic background was also supplied to aid in the understanding of the reservoir. The reservoir simulation model was built using Computer Modelling Group software, namely Builder and IMEX. The first model iteration contained one producer in an 8,500-foot lateral pattern. Further iterations included additional producers and injectors for waterflood and polymer flood studies. Conclusions and recommendations were drawn upon analyzing the reservoir simulation results centering around favorable production strategies, polymer flood performance, comparison to the on-going Milne viscous oil polymer flood pilot, and future polymer flood studies. Completed objectives of this project included: 1. Developing a numerical reservoir simulation model for the Ugnu MB sand in the pilot area; 2. Evaluating the productivity of horizontal wells in the Ugnu MB sand; 3. Predicting ultimate oil recovery with waterflood and polymer flood; 4. Predicting polymer utilization, polymer injected per incremental oil barrels over waterflood.
  • A modular LoRaWAN inspired Internet of Things approach to collecting sensor data via Software Defined Radio

    Van Cise, Tristan; Genetti, Jon; Lawlor, Orion; Metzgar, Jonathan (2020-05)
    The emergence of simple Internet of Things (IoT) devices has habituated the ability to efficiently collect data and communicate information between devices with ease. Similarly, Software Defined Radio (SDR) has compacted radio communication into a USB dongle capable of receiving radio signals from most radio transmitters. In this approach, the ease of IoT device communication and versatility of SDR data collection and transmission techniques is combined to monitor building thermal decay. The system developed to collect thermal decay data is adapted from the Long Range Wide Area Network (LoRaWAN) IoT architecture and is designed to facilitate variable size collection environments and real-time data visualization. This paper will outline the implementation and capabilities of the collection system and highlight alternate applications and hardware implementations of the underlying framework.
  • LucidDream: Dynamic Story Generation through Directed Chatbot Interactions

    Stonebraker, Ryan; Metzgar, Jonathan; Lawlor, Orion; Hartman, Chris (2020-05)
    Natural Language Understanding and Generation are both areas of active research with widespread potential for story telling. This paper proposes an architecture for dynamically generating stories that allows a scene to be constructed and then dynamically written through the interaction of individual chatbots. Each chatbot in this environment is meant to mimic either the specific emotional profile of a character or holistically represent all of the character’s attributes. Chatbots are created using the conversation history so that they can understand context, a relevant sentence suggestion provided by a question-answering model to keep generated output on topic, and a finetuned version of the GPT-2 transformer-based language model to combine all of this information and generate text. This architecture serves as an ensemble method of approaching character modeling and also introduces the little-explored concept of emotional style transferring as a method for merging a story character’s emotional attributes with an independent training corpus. The question-answering model used in this study achieved 65.24% accuracy when tested on the Stanford Question-Answering Dataset and the emotion classification model achieved 57.3% accuracy on the International Survey on Emotion Antecedents and Reactions dataset. While neither of these performances are SOTA for their respective individual tasks, they are used in combination to produce state of the art directed story generation and pave the way for future research.
  • Examining thermokarst initiation with random forest models

    Spicer, Rawser W.; Bolton, W. Robert; Lawlor, Orion; Chappell, Glenn (2020-05)
    This project examines thermokarst initiation through the application of random forest models. Thermokarst initiation marks the start of the formation of thermokarst features. Changes in landscape, due to the thermokarst process, can result in changes in wildlife habitat, as well as energy, carbon and water fluxes. Random forests are an ensemble learning technique that combines the results of many independent decision trees to create results that avoid the overfitting in regular decision trees. Random forests were trained against an existing thermokarst initiation model. Results showed that random forests were useful in this context. Random forest hyperparameters were also examined through a multiparameter sensitivity analysis.
  • Investigation of nanoscale drug particles and their effect on the fluid dynamic properties of the blood

    Slats, Jason L.; Das, Debendra; Zhang, Lei; Misra, Debasmita (2020-05)
    Research has shown that gold nanoparticles increase the efficiency of radiation treatments of cancer by up to 25%. This means patients can be exposed to lower doses of radiation that does more concentrated damage to cancerous cells and less damage to healthy surrounding tissue. Before these nanoparticles can be introduced to the human body, the behavior of these particles in the blood stream must be understood. A model of gold nanoparticle flow through the aortic arch was developed in the present investigation for predicting behavior of these particles in the human body. A set of initial modeling parameters was developed out of existing data pertaining to blood flow rates and viscosities of a blood-mimicking fluid across a temperature range of 30-40 degrees Celsius. The aorta wall was modeled as a no-slip solid surface. Computational fluid dynamic models using ANSYS Fluent across this temperature range have generated general velocity distributions of blood flow through the aortic arch and identifies several areas of possible recirculation. The current state of the model provides preliminary results, which are valuable in generating an accurate model of gold nanoparticles flowing through the aortic arch.
  • MIL-53 (Al) and graphene oxide nanocomposites for dye adsorption

    Serventi, Daniel R; Zhang, Lei; Peterson, Rorik; Zhang, Junqing; Huang, Daisy (2020-05)
    Textile manufacturers produce large amounts of wastewater every year as a result of global demand. Waste dyes are highly resilient against physical processes, insoluble in water, and resistant to detergents. Carcinogenic and mutagenic effects are linked to these dyes, making them a large health hazard. Current dye removal methods are highly complex and inefficient. Thus, a new means of removing textile dyes from wastewater is needed. Nanomaterials are one such possibility, since they exhibit traits unique from bulk materials. One key trait is their surface area to volume ratio. Since the materials are so small, they’re almost able to be considered two dimensional in certain instances. A high surface area is closely linked to adsorption potential, making nanomaterials a promising candidate for dye removal. This project has two portions: material synthesis and adsorption testing. Material synthesis sets up the adsorption testing phase by fabricating enough nanomaterials for testing. The nanomaterials used for this project are MIL-53 (Al) and graphene oxide (GO). MIL-53 (Al) and GO were chosen since they exhibit good stability in water and effective geometrical structures for water filtration. Synthesized composites of the two materials varying in mass of GO will be tested as well. Adsorption testing uses slightly acidic (pH 5.6) methyl blue and methyl orange solutions of varying parts per million (PPM) concentrations. The tests examine effects of initial concentration, duration of exposure, and temperature effects on adsorption potential. Nanomaterials reached equilibrium adsorption after 12 hours of mixing. Most materials efficiently removed up to 90% or greater of dye particles in solutions with initial concentrations of 100 PPM for both dye colors. Increased temperatures reduced adsorption potential of nearly all materials tested for both dye colors.
  • Computational fluid dynamics model of two-phase heavy oil and air flow in a horizontal pipe

    Sanders, Nicholas E.; Ahmadi, Mohabbat; Awoleke, Obadara; Dandekar, Abhijit (2020-05)
    The production of heavy oil resources is becoming more prevalent as the conventional resources of the world continue to deplete. These heavy oil resources are being produced from horizontal wells and need to be transported in pipeline to processing facilities as a two-phase flow. Two-phase flow is important to the oil industry with the general focus being placed on light oil or water and gas flows. With little work having been done on two-phase heavy oil flow this study will examine these two-phase flows by recreating experimental data generated for heavy oil and air flow in a 1.5-inch diameter pipe and expand this data to include larger 2.875-inch and 3.5-inch pipes. A computational fluid dynamics model was generated to mimic the 1.5-inch diameter pipe used in the experiments. This model was validated for laminar and turbulent flow by using the same heavy oil properties from the original experiment and air respectively. The model was then run to simulate the given two-phase oil-air flows provided from the experimental data for the flow velocities that had pressure drop and liquid holdup data available. The two-phase results were compared to both the experimental data and the Beggs and Brill values for both pressure drop and liquid holdup. A 2.875-inch and 3.5-inch model were generated and the same process was followed for laminar and turbulent validation and then with a subset of four two-phase flow velocities. Without the availability of experimental data for the two larger size pipes the two-phase results were only compared to the Beggs and Brill values. Overall the results showed a good correlation to the laminar and turbulent flow in all three models with the turbulent flow showing the largest error for the pressure drop when the flow was in the laminar to turbulent transition zone for Reynolds numbers. The two-phase results showed to be in between the experimental and Beggs and Brill method values for the original 1.5-inch model and showed that as the gas flow velocity increased in the system the error grew for all three models. Given that the Beggs and Brill method values were generated based on experiments for water-air flow in a 1.0-inch pipe the values for the pressure drop in the 2.875-inch pipe and the 3.5-inch pipe were not unexpected and seemed to match well with an extrapolation of the experimental values. This study shows that a model can be generated to examine the two-phase flow behavior in horizontal sections of well and in pipelines on a computational basis. While these models are time consuming to generate and run with the increase in computing capacity available easily they can become more suitable than generating experimental setups for finding the same information. There will need to be more work done on heavy oil two-phase flow and additional experiments run for larger size pipes and two-phase flow to help tune these models but they do show promise for the future.
  • A Software-Defined Radio Transmitter for Variable-Coded Modulation on a CubeSat

    Mullet, John; Thorsen, Denise L.; Raskovic, Dejan; Bossert, Katrina (2020-05)
    The large volume of satellites sharing the same spectrum and the complexities of communications in Low-Earth Orbit (LEO) pose challenges to the downlink of large volumes of data on a platform that is bandwidth, power, and time limited. LEO satellites operate in a highly variable communications environment due to variations in inter-satellite or satellite-to-ground geometries, weather, and interference. Therefore, there is motivation for implementing satellite communication techniques that manage these issues to increase the data throughput. One such technique is variable-coded modulation which shows improvement by taking advantage of the dynamic nature of a satellite link. As part of the Air Force Research Laboratory University Nanosatellite Program, and in collaboration with NASA, this project focuses on the development of an S-band software defined radio for CubeSats that utilizes variable-coded modulation defined by the Digital Video Broadcasting-Satellite-Second Generation standard. This project defense discusses the initial development and testing using GNU Radio, and the challenges for full implementation, as well as the current status of the transmitter, and future work.
  • Applied machine learning using twitter sentiment and time series data for stock market forecasting

    McKenna, Jacob; Hartman, Chris; Genetti, Jon; Metzgar, Jonathan (2020-05)
    This paper presents an approach to determine stock prices using Twitter sentiment. Due to the highly stochastic nature of the stock market, it is difficult to determine a model that accurately predicts prices. In Twitter Mood Predicts the Stock Market by Bollen, capturing tweets and classifying each tweet’s mood was useful in predicting the Dow Industrial Jones Average (DJIA). Accurately predicting a movement quantitatively is profitable. We present a method that captures sentiment from Twitter with mentions of specific companies to predict their price for the following day.
  • Improved ray tracing performance through tri-adaptive sampling

    Craddick, Tristan; Lawlor, Orion; Genetti, Jon; Metzgar, Jonathan (2020-05)
    Ray tracing is a technique capable of rendering high quality images by tracing rays from the camera position into the scene and examining the points they intersect with. With the advent of NVIDIA RTX hardware, improving renderer design through greater algorithmic efficiency will allow for even greater real-time rendering capabilities. Naive implementations are simple to implement and cheap enough to run well on modern systems, but often have issues with aliased edges due to lower quantities of rays for scene sampling. Techniques such as super-sampling are capable of reducing or entirely eliminating aliasing, but carry a high performance cost due to additional ray requirements. Under-sampling is a technique that allows a single ray to determine the color of multiple pixels, allowing for high performance in regions of little variation. The combination of these techniques is collectively referred to as Adaptive Sampling. Our implementation of this algorithm operates by rendering the scene at a low resolution and then sampling the resulting image to determine if rays are necessary at higher resolutions. In this project, we implement a form of this multiple-resolution approach based upon a triangular grid overlaying the pixel grid. Results on RTX cards indicate a performance increase of 29-40% over the naive renderer, and a 1-4% increase over the traditional adaptive sampling algorithm, all while achieving little degradation in quality compared to the ground truth image.

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