John F. Kennedy Space Center's Technology Development and Application 2006-2007 Report

Topics covered include: Reversible Chemochromic Hydrogen Detectors; Determining Trajectory of Triboelectrically Charged Particles, Using Discrete Element Modeling; Using Indium Tin Oxide To Mitigate Dust on Viewing Ports; High-Performance Polyimide Powder Coatings; Controlled-Release Microcapsules for Smart Coatings for Corrosion Applications; Aerocoat 7 Replacement Coatings; Photocatalytic Coatings for Exploration and Spaceport Design; New Materials for the Repair of Polyimide Electrical Wire Insulation; Commodity-Free Calibration; Novel Ice Mitigation Methods; Crack Offset Measurement With the Projected Laser Target Device; New Materials for Structural Composites and Protective Coatings; Fire Chemistry Testing of Spray-On Foam Insulation (SOFI); Using Aerogel-Based Insulation Material To Prevent Foam Loss on the Liquid-Hydrogen Intertank; Particle Ejection and Levitation Technology (PELT); Electrostatic Characterization of Lunar Dust; Numerical Analysis of Rocket Exhaust Cratering; RESOLVE Projects: Lunar Water Resource Demonstration and Regolith Volatile Characterization; Tribocharging Lunar Soil for Electrostatic Beneficiation; Numerically Modeling the Erosion of Lunar Soil by Rocket Exhaust Plumes; Trajectory Model of Lunar Dust Particles; Using Lunar Module Shadows To Scale the Effects of Rocket Exhaust Plumes; Predicting the Acoustic Environment Induced by the Launch of the Ares I Vehicle; Measuring Ultrasonic Acoustic Velocity in a Thin Sheet of Graphite Epoxy Composite; Hail Size Distribution Mapping; Launch Pad 39 Hail Monitor Array System; Autonomous Flight Safety System - Phase III; The Photogrammetry Cube; Bird Vision System; Automating Range Surveillance Through Radio Interferometry and Field Strength Mapping Techniques; Next-Generation Telemetry Workstation; GPS Metric Tracking Unit; and Space-Based Range

Smart Cities: Inverse Design of 3D Urban Procedural Models with Traffic and Weather Simulation

Urbanization, the demographic transition from rural to urban, has changed how we envision and share the world. From just one-fourth of the population living in cities one hundred years ago, now more than half of the population does, and this ratio is expected to grow in the near future. Creating more sustainable, accessible, safe, and enjoyable cities has become an imperative

Design, Data Collection, and Driver Behavior Simulation for the Open- Mode Integrated Transportation System (OMITS)

With the remarkable increase in the population and number of vehicles, traffic has become a severe problem in most metropolitan areas. Traffic congestion has imposed tight constraints on economic growth, national security, and mobility of riders and goods. The open-mode integrated transportation system (OMITS) has been designed to improve the traffic condition of roadways by increasing the ridership of vehicles and optimizing transportation modes through smart services integrating emerging information communication technologies, big data management, social networking, and transportation management. Even a modest reduction in the number of vehicles on roadways will lead to a considerable cost savings in terms of time and money. Additionally the reduction in traffic jams will lead to a significant decrease in both gasoline consumption and greenhouse gas emissions. As a result, novel transportation management is critical to reduce vehicle mileage in the peak time of the road network. The OMITS was proposed to enhance transportation services in respect to the following three aspects: optimization of the transportation modes by multimodal traveling assignment, dynamic routing and ridesharing service with advanced traveler information systems, and interactive user interface for social networking and traveling information. Therefore, the OMITS encompasses a broad range of advanced transportation research topics, say dynamic trip- match, transportation-mode optimization, traffic prediction, dynamic routing, and social network- based carpooling. This dissertation will focus on a kernel part of the OMITS, namely traffic simulation and prediction based on data containing the distribution of vehicles and the road network configuration. A microscopic traffic simulation framework has been developed to take into account various traffic phenomena, such as traffic jams resulting from bottlenecking, incidents, and traffic flow shock waves. Four fundamental contributions of the present study are summarized as follows: Firstly, an accurate and robust vehicle trajectory data collection method based on image data of unmanned aerial vehicle (UAV) has been presented, which can be used to rapidly and accurately acquire the real-time traffic conditions of the region of interest. Historically, a lack in the availability of trajectory data has posed a significant obstacle to the enhancement of microscopic simulation models. To overcome this obstacle, a UAV based vehicle trajectory data collection algorithm has been developed. This method extracts vehicle trajectory data from the UAV’s video at different altitudes with different view scopes. Compared with traditional methods, the present data collection algorithm incorporates many unique features to customize the vehicle and traffic flow, through which vehicle detection and tracking system accuracy can be considerably increased. Secondly, an open mechanics-based acceleration model has been presented to simulate the longitudinal motion of vehicles, in which five general factors—namely the subject vehicle’s speed and acceleration sensitivity, safety consideration, relative speed sensitivity and gap reducing desire—have been identified to describe drivers’ preferences and the interactions between vehicles. Inspired by the similarity between vehicle interactions and particle interactions, a mechanical system with force elements has been introduced to quantify the vehicle’s acceleration. Accordingly, each of the aforementioned five factors are assumed to function as an individual trigger to alter each vehicle’s speed. Based on Newton’s second law of motion, the subject vehicle’s longitudinal behavior can be simulated by the present open mechanics-based acceleration model. By introducing feeling gap, multilane acceleration behavior is included in the presented model. The simulation results fit realistic conditions for the traffic flow and the road capacity very well, where traffic shockwaves can be observed for a certain range of the traffic density. This model can be extended to more general scenarios if other factors can be recognized and introduced into the modeling framework. Thirdly, a driver decision-based lane change execution model has been developed to describe a vehicle’s lane change execution process, which includes two steps, i.e. driver’s lane selection and lane change execution. Currently, most lane change models focus on the driver’s lane selection, and overlook the driver’s behavior during a process of lane change execution which plays a significant role in the simulation of traffic flow characteristics. In this model, a lane change execution is analyzed as a driver’s decision-making process, which consists of desire point setting, priority decision-making, corresponding actions and achievement of consensus analysis. Compared with the traditional lane change execution models, the present model describes a realistic lane change process, and it provides more accurate and detailed simulation results in the microscopic traffic simulation. Based on the presented open mechanics-based acceleration model and the driver decision- based lane change execution model, a reverse lane change model has further been developed to simulate some complex traffic situations such as reverse lane change process at a two-way-two- lane road section where one lane is blocked by a traffic incident. Based on this reverse lane change model, information on the average waiting time and road capability can be obtained. The simulation results show that the present model is able to reflect real driver behavior and the corresponding traffic phenomenon during a reverse lane change process Through a homogenization process of the microscopic vehicle motion, we can obtain the macroscopic traffic flow of the roadway network within certain time and spatial ranges, which will be integrated into the OMITS system for traffic prediction. The validation of the models through future OMITS operations will also enable them to be high fidelity models in future driverless technologies and autonomous vehicles

Knowledge Discovery and Data Mining for Shared Mobility and Connected and Automated Vehicle Applications

The rapid development of shared mobility and connected and automated vehicles (CAVs) has not only brought new intelligent transportation system (ITS) challenges with the new types of mobility, but also brought a huge opportunity to accelerate the connectivity and informatization of transportation systems, particularly when we consider all the new forms of data that is becoming available. The primary challenge is how to take advantage of the enormous amount of data to discover knowledge, build effective models, and develop impactful applications. With the theoretical and experimental progress being made over the last two decades, data mining and machine learning technologies have become key approaches for parsing data, understanding information, and making informed decisions, especially as the rise of deep learning algorithms bringing new levels of performance to the analysis of large datasets. The combination of data mining and ITS can greatly benefit research and advances in shared mobility and CAVs.This dissertation focuses on knowledge discovery and data mining for shared mobility and CAV applications. When considering big data associated with shared mobility operations and CAV research, data mining techniques can be customized with transportation knowledge to initially parse the data. Then machine learning methods can be used to model the parsed data to elicit hidden knowledge. Finally, the discovered knowledge and extracted information can help in the development of effective shared mobility and CAV applications to achieve the goals of a safer, faster, and more eco-friendly transportation systems.In this dissertation, there are four main sections that are addressed. First, new methodologies are introduced for extracting lane-level road features from rough crowdsourced GPS trajectories via data mining, which is subsequently used as the fundamental information for CAV applications. The proposed method results in decimeter level accuracy, which satisfies the positioning needs for many macroscopic and microscopic shared mobility and CAV applications. Second, macroscopic ride-hailing service big data has been analyzed for demand prediction, vehicle operation, and system efficiency monitoring. The proposed deep learning algorithms increase the ride-hailing demand prediction accuracy to 80% and can help the fleet dispatching system reduce 30% of vacant travel distance. Third, microscopic automated vehicle perception data has been analyzed for a real-time computer vision system that can be used for lane change behavior detection. The proposed deep learning design combines the residual neural network image input with time serious control data and reaches 95% of lane change behavior prediction accuracy. Last but not least, new ride sharing and CAV applications have been simulated in a behavior modeling framework to analyze the impact of mobility and energy consumption, which addresses key barriers by quantifying the transportation system-wide mobility, energy and behavior impacts from new mobility technologies using real-world data

A cross-functional value chain approach to geospatial information: a guide to practice for the minerals industry

A research dissertation submitted to the Faculty of Engineering and the Built Environment, of the University of the Witwatersrand, in the fulfillment of the requirements for the degree of Master of Science in Engineering. Swellendam, 2018Reproducing a mining project life-cycle in the form of a value chain, from exploration to mine closure, provides a graphical representation of the interdependencies between functions or activities, both upstream and downstream of a particular process. This can be used to develop the concept of geospatial context, i.e. high-level situational awareness. By understanding and responding to geospatial context, geospatial information can be enhanced in direct support of investment decisions and/or operational control. The risk of deficient geospatial information requires effective mitigation and management throughout the full life-cycle of a project, starting with exploration where the geospatial foundation is laid for all work which follows. Therefore, geospatial information is a primary, not secondary consideration at the commencement of a project. The role of mine surveying in protecting the surface and workings of a mine, through the provision of accurate maps, plans and associated geospatial records, protects people and the asset, spanning mine and public safety. Additionally, measuring, monitoring, reconciling and reporting key performance indicators which drive value, enables value creation through improved foresight, efficiency and effectiveness. This dissertation discusses the critical role of geospatial information in risk mitigation and business performance monitoring, with specific reference to the interdependencies between functions such as exploration, mining, processing, environmental protection and mine closure. The value potential is significant.MT 201

Proceedings from the 1992 national conference

Presented at Irrigation and water resources in the 1990's: proceedings from the 1992 national conference held on October 5-7, 1992 in Phoenix, Arizona.Includes bibliographical references.Sponsored by U.S. Committee on Irrigation and Drainage.Interdisciplinary teams for assessing the performance of irrigated agriculture systems -- Putah South Canal remote acoustic water level monitoring and flow measurement -- Decentralized constant-volume control of irrigation canals -- Field manufacture and application of reinforced plastic canal and pipe linings -- Improving channel maintenance methods for Egypt's irrigation systems -- Routing flood water through an irrigation delivery system -- Experience with flexible schedules and automation on pilot projects -- Canal linings used by the Bureau of Reclamation with emphasis on rehabilitation -- The California Farm Water Coalition: telling thirsty Californians why agriculture needs water -- Institutional framework and challenges in management of agricultural water use in South Florida -- Technology transfer lessons from a U.S. water district -- Management of water conservation through irrigation system modernization and rehabilitation -- Artificial recharge of groundwater -- Long-term storage through indirect recharge -- Mitigating agricultural impacts on groundwater through desalination -- Agriculture's impact on water resources in Eastern Europe: Bulgaria, Hungary, and Romania -- How probiotic fertilizers improve irrigation efficiency, buffer salts, and reduce nitrate infiltration into groundwater -- Drought, supply shortages and E.S.A.: can the farmer survive? -- Avoiding pitfalls in canal automation -- AZSCHED computer software for irrigation scheduling -- Determination of irrigated crops consumptive water use by remote sensing and GIS techniques for river basins -- GIS and conjunctive use for irrigated agriculture -- Mapping technology in the '90's for GIS applications to irrigation and drainage

Modelling driving behaviour at motorway weaving sections

This research focuses on the understanding of driving behaviour in motorway weaving sections, particularly the lane-changing and acceleration behaviours which are significant factors in characterising the operations of weaving section. Drivers’ lane-changing behaviour is a series interdependent decisions according to a particular lane-changing plan (latent). An intensive interaction with neighbouring traffic increases the lane-changing complexity in weaving section. The drivers’ choices in weaving section can be significantly affected by the actions of the neighbourhood drivers and moving as a group (i.e. platoon and weaving). Furthermore, the intensity of lane-changing has significant impact on the acceleration behaviour in weaving section traffic which may response differently from the stimulus (i.e. leave a space for pre-emptive lane-changing). An analysis of detailed trajectory data collected from moderately congested traffic flow of a typical weaving section in the M1 motorway, UK (J 42-43). The data reveals that a substantial proportion (23.4%) of the lane-changing at weaving section exhibits such group behaviour (i.e. platoon and weaving). The current study extends the state-of-the-art latent plan lane-changing model which account explicitly the various mechanisms. The model constitutes that the driver is most likely performing a pre-emptive lane-changing at the beginning of weaving section and moving toward kerbside (left direction). Moreover, the driver aggresiveness affects significantly on weaving and least on platoon lane-changing. The proposed acceleration model allows the car-following behaviour (acceleration/deceleration) corresponds with both stimulus (positive/negative relative speed). The model is conditional on gap threshold and reaction time distributions (probabilistic model) capturing the heterogeneity across drivers. most of traffic response differently from the stimulus condtions where 43.5% falls in deceleration with positive relative speed. All the parameters in each model are estimated jointly using Maximum Likelihood Estimation technique and reveal significant differences. The results show promising contribution towards improving the fidelity of microscopic traffic performance analysis

Advanced Location-Based Technologies and Services

Since the publication of the first edition in 2004, advances in mobile devices, positioning sensors, WiFi fingerprinting, and wireless communications, among others, have paved the way for developing new and advanced location-based services (LBSs). This second edition provides up-to-date information on LBSs, including WiFi fingerprinting, mobile computing, geospatial clouds, geospatial data mining, location privacy, and location-based social networking. It also includes new chapters on application areas such as LBSs for public health, indoor navigation, and advertising. In addition, the chapter on remote sensing has been revised to address advancements

Evaluating the impact of land use and policy on water quality in an agricultural catchment: The Leet water,South-East Scotland.

This is an interdisciplinary study combining research techniques from the natural and social sciences, to evaluate the impact of EU policies and land use change scenarios for assessing water quality in an agricultural catchment. The study focuses on the Leet Water catchment, a left-bank tributary of the River Tweed, Berwickshire, South-East Scotland. The Leet Water and its subcatchment the Lambden Bum cover an area of approximately 114km(^2) within the Lothian and Borders Nitrate Vulnerable Zone (designated in 2002).In the Leet Water catchment, spot measurements of nitrate (NO(^3)-N) from 1977 to 1998 found the 11.3 mg/1 (EU permitted maximum) was often exceeded. Further spot monitoring from October 2002 to August 2004 found 12 instances where the 11.3 mg/1 permitted maximum was exceeded with all streams in the catchment experiencing high levels of nitrate over the winter periods. Interviews with local farmers, advisors, and the regulators found this to be the result of a complex set of circumstances including long-term Common Agricultural Policy subsidies and the farmers' drive for increased profitability without due regard for the environmental consequences. Land management practices such as under- draining of fields, overuse of fertiliser and allowing livestock access to water-courses has exacerbated the problem. The study demonstrates the potential of multispectral airborne remote sensed data for mapping agricultural land cover at the field scale, including the ability to distinguish winter and spring-sown cereal crops. Pollution impacts were modelled using a modified export coefficient approach by integrating land cover with available chemical and fertiliser practice data sets. Results of modelling scenarios of simple land use changes found that reducing fertiliser use by 10% can reduce the number of fields in the very high risk group from 191 to 16 This equates to reducing the high risk area from 〜3255 ha (29% of the catchment) to 〜428 ha (3.3 % of the catchment). This method of water quality modelling provides a means of integrating field research on water quality with the results of socio-economic surveys. The research found the principal causes of the failure of EU policy to address the problems are both socio-economic and institutional barriers, in particular the way in which information is presented to the farming community. Case studies of both large and small farms reveal that agri-environment measures such as the 'points' based Rural Stewardship Scheme (RSS) can attract substantial funding. However, these schemes are of most benefit to large farms where significant land use changes that accrue points can be made. Smaller farms find it difficult to suggest changes that will accrue these 'points' for a successful application. Furthermore, farmers believe recent changes e.g. the Land Management Contract implemented by The Scottish Executive may include a range of funding opportunities for improving land management practices but these are not well presented. There are gaps in the knowledge transfer process in relation to water quality issues between Government and land users. This research suggests that independent facilitators (advisors) such as those used in the Australian landcare approach should be introduced in the UK to help address this problem