Dissertations / Theses on the topic 'Flush air data sensing'

Research suggests what leaders should do or the qualities or characteristics they should have to be ethical leaders (Brown & Treviño, 2006). The ethical decision-making process that leaders should follow to avoid scandals and unethical behavior are overlooked. Few studies focused on ethical decision-making within higher education. Yet, educational leaders have an ethical responsibility that may be even more complex than those of other leaders due in part to increasingly diverse student populations enrolled in higher education that is having an impact on the growth of educational institutions on a global basis (Shapiro & Stekfovich, 2011). Further, ethical scandals are no longer contained by national borders. The rapid growth of technology coupled with changes in political and societal landscapes has advanced ethical scandals to global prominence. A more collective need to understand ethical values and ethical decision-making practices on a global level has emerged. To be globally effective, leaders must be aware of the similarities and differences across and within cultures that could influence business practices (Resick, Hanges, Dickson, & Mitchelson, 2006). However, cross-cultural research has not yet addressed the topic of ethical decision-making. In this study, the ethical decision-making process of higher education was not only examined in the United Stated but also in Poland. This exploratory study used the Delphi research technique to identify an ethical decision-making definition that higher administration leaders in both the United States and Poland use to make ethical decisions and identify the environmental factors that influence their decisions. Findings showed that the United States and Polish expert panels were different and showed very little in common in the identification of a definition and environmental factors. Lastly, both sets of experts identified a new process for ethical decision-making, each constructing a different ethical decision-making process model. This research on ethical decision-making provided evidence that the Polish and United States cultures are not as similar as identified in previous studies in terms of how they identify ethical decision-making and the factors they identify with influencing ethical decision-making. Using this information will create a better understanding of the practices and approaches to ethics that leaders use because of the huge influence they have and exert on people within their own organization and society around them.

A method is introduced to consider flush air data system (FADS) pressures using a technique based on inertial navigation to reconstruct the trajectory of an atmospheric entry vehicle. The approach augments the recently-developed Inertial Navigation Statistical Trajectory and Atmosphere Reconstruction (INSTAR), which is an extension of inertial navigation that provides statistical uncertainties by utilizing Monte Carlo dispersion techniques and is an alternative to traditional statistical approaches to entry, descent, and landing trajectory and atmosphere reconstruction.

The method is demonstrated using flight data from the Mars Science Laboratory (MSL) entry vehicle, which contained an inertial measurement unit and a flush air data system called the Mars Entry Atmospheric Data System (MEADS). An MSL trajectory and atmosphere solution that was updated using landing site location in INSTAR is first presented. This solution and corresponding uncertainties, which were obtained from Monte Carlo dispersions, are then used in a minimum variance algorithm to obtain aerodynamic estimates and uncertainties from the MEADS observations. MEADS-derived axial force coefficient and freestream density estimates and uncertainties are also derived from the minimum variance solutions independent of the axial force coefficients derived from computation fluid dynamics (CFD), which have relatively high a priori uncertainty. Results from probabilistic analyses of the solutions are also presented.

This dissertation also introduces a method to consider correlated CFD uncertainties in INSTAR. From a priori CFD uncertainties, CFD force and pressure coefficients are dispersed in a Monte Carlo sense and carried over into the reconstructions. An analysis of the subsequent effects on the trajectory, atmosphere, and aerodynamic estimates and statistics is presented.

Trajectory, atmospheric, and aerodynamic estimates compare favorably to extended Kalman filter solutions obtained by the MSL reconstruction team at NASA Langley Research Center. The uncertainties obtained through the methods from this work are generally smaller in magnitude because of assumptions made regarding sources of error in the MEADS pressure transducer uncertainties. Using data-derived uncertainties in the pressure measurement noise covariance results in aerodynamic parameter estimate uncertainties that are in better agreement with the uncertainties derived from the Monte Carlo dispersions. CFD database errors dominate the uncertainties of parameters derived from aerodatabase axial force coefficients.

According to World Health Organization, around 7 million people die every year due to diseases caused by air pollution. With the improvements in Internet of Things in the recent years, environmental sensing systems has started to gain importance. By using technologies like Cloud Computing, RFID, Wireless Sensor Networks, and open Application Programming Interfaces, it has become easier to collect data for visualization on different platforms. However, collected data need to be represented in an efficient way for better understanding and analysis, which requires design of data visualization tools. The GreenIoT initiative aims to provide open data with its infrastructure for sustainable city development in Uppsala. An environmental web application is presented within this thesis project, which visualizes the gathered environmental data to help municipality organizations to implement new policies for sustainable urban planning, and citizens to gain more knowledge to take sustainable decisions in their daily life. The application has been developed making use of the 4Dialog API, which is developed to provide data from a dedicated cloud storage for visualization purposes. According to the evaluation presented in this thesis, further development is needed to improve the performance to provide faster and more reliable service as well as the accessibility to promote openness and social inclusion.
Enligt World Health Organization dör 7 miljoner människor varje år på grund av sjukdomar orsakade av luftföroreningar. Med förbättringar inom Internet of Things under senare år, har betydelsen av system för miljösensorer. Genom att använda tekniker som molntjänster, RFID, trådlösa sensornätverk och öppna programmeringsgränssnitt, har det blivit enklare att samla in data för visualisering på olika plattformar. Men insamlad data behöver bli representerad på ett effektivt sätt för bättre förståelse och analys, vilket kräver utformande av verktyg för visualisering av data. Initiativet GreenIoT strävar mot att erbjuda öppen data med sin infrastruktur för hållbar stadsutveckling i Uppsala. I detta arbete presenteras en webb-tillämpning, som visualiserar den insamlade miljödatan för att hjälpa kommunen att implementera nya policies för hållbar stadsutveckling, och stimulera medborgare till att skaffa mer kunskap för att göra miljövänliga val i sin vardag. Tillämpningen har utvecklats med hjälp av 4Dialog API, som tillhandahåller data från lagring i molnet för visualiseringssyfte. Enligt den utvärdering som presenteras i denna rapport konstateras att vidare utveckling behövs för att förbättra dels prestanda för att erbjuda en snabbare och mer tillförlitlig service, och dels åtkomstmöjligheter för att främja öppenhet och social inkludering.

The purpose of this research is to develop best practices for deploying air quality sensors in a remote location such as Mongolia. I discussed the architecture and design constraints when collecting remote air quality sensors data, the challenges that emerge while implementing a sensor-based network in a remote location such as Mongolia. The tradeoffs of using different architectures are described. I observed the usage of electrical heaters in modified gers in remote locations and conclude how effective they are in reducing PM2.5 levels by analyzing air quality data and go through the process of cleaning up the data and removing humidity from low-cost sensors used to deploy in a remote location such as Mongolia so that the PM2.5 reading is more accurate. In order to help many humanitarian efforts dealing with better air quality in developing countries, an air quality sensor was designed to keep low cost as much as possible. The cost is about $200 to build, which is cheaper than other low-cost sensors, yet provides more functionality (e.g., CO2 sensing) and used cellular connectivity to upload data in real-time. This sensor has implications beyond Mongolia. The sensor can be used anywhere WiFi connectivity is not available, such as parks, bus stops, and along roadways, breaking the constraints that other low-cost sensors have. Removing the need for WiFi is a necessary step in allowing ubiquitous air quality sensing. The contributions in this thesis are: First, I presented the challenges one should consider while deploying air quality sensors in developing countries. Second, since Mongolia offers a unique environment and constraints, I shared experiences in deploying sensors in a remote location like Mongolia. This experience goes beyond air quality sensors and can inform anyone who is deploying sensors in remote areas. Third the analysis of the PM2.5 on the gers gives us better insights as to whether modifying gers with insulation and using electrical heaters as opposed to burning coal to heat up the gers makes a difference in regard to better air quality in the gers.

A major opportunity for using satellite observations of tropospheric chemical concentrations is to improve our scientific understanding of atmospheric processes by integrated analysis of satellite, aircraft, and ground-based observations with global and regional scale models. One endpoint of such efforts is to reduce modeling biases and uncertainties. The idea of coupling these observations with a regional scale air quality model was the starting point of this research. The overall objective of this research was to improve the NOₓ emission inventories by integrating observations from different platforms and regional air quality modeling. Specific objectives were: 1) Comparison of satellite NO₂ retrievals with simulated NO₂ by the regional air quality model. Comparison of simulated tropospheric gas concentrations simulated by the regional air quality model, with aircraft and ground-based observations; 3) Assessment of the uncertainties in comparing satellite NO₂ retrievals with NOₓ emissions estimates and model simulations; 4) Identification of biases in emission inventories by data assimilation of satellite NO₂ retrievals, and ground-based NO, NO₂ and O₃ observations with an iterative inverse method using the regional air quality model coupled with sensitivity calculations; 5) Improvement of our understanding of NOₓ emissions, and the interaction between regional and global air pollution by an integrated analysis of satellite NO₂ retrievals with the regional air quality model. Along with these objectives, a lightning NOₓ emission inventory was prepared for two months of summer 2004 to account for a significant upper level NOₓ source. Spatially-resolved weekly NO₂ variations from satellite retrievals were compared with estimated NOₓ emissions for different region types. Data assimilation of satellite NO₂ retrievals, and ground-based NO, NO₂ and O₃ observations were performed to evaluate the NOₓ emission inventory. This research contributes to a better understanding of the use of satellite NO₂ retrievals in air quality modeling, and improvements in the NOₓ emission inventories by correcting some of the inconsistencies that were found in the inventories. Therefore, it may provide groups that develop emissions estimates guidance on areas for improvement. In addition, this research indicates the weaknesses and the strengths of the satellite NO₂ retrievals and offers suggestions to improve the quality of the retrievals for further use in the tropospheric air pollution research.

Avec l'expansion des zones urbaines, la pollution de l'air et l'effet d'îlot de chaleur augmentent, entraînant des problèmes de santé pour les habitants et des changements climatiques mondiaux. Dans ce contexte, les arbres urbains sont une ressource précieuse pour améliorer la qualité de l'air et promouvoir les îlot de fraîcheur. D'autre part, les canopées sont soumises à des conditions spécifiques dans l'environnement urbain, causant la propagation de maladies et la diminution de l'espérance de vie parmi les arbres. Cette thèse explore le potentiel de la télédétection pour la cartographie automatique des arbres urbains, de la détection des couronnes d'arbres à l'estimation des espèces, une tâche préliminaire essentielle pour la conception des futures villes vertes, et pour une surveillance efficace de la végétation. Fondé sur des données hyperspectrales aéroportées, panchromatiques et un modèle numérique de surface, le premier objectif de cette thèse consiste à tirer parti de plusieurs sources de données pour améliorer les cartes d'arbres urbains existants, en testant différentes stratégies de fusion (fusion de caractéristiques et fusion de décision). La nature des résultats nous a conduit à optimiser la complémentarité des sources. En particulier, le deuxième objectif est d'étudier en profondeur la richesse des données hyperspectrales, en développant une approche d'ensemble classifier fondée sur des indices de végétation, où les "classifier" sont spécifiques aux espèces. Enfin, la première partie a mis en évidence l'intérêt de distinguer les arbres de rue des autres structures d'arbres urbains. Dans un cadre de Marked Point Process, le troisième objectif est de détecter les arbres en alignement urbain. Par le premier objectif, cette thèse démontre que les données hyperspectrales sont le principal moteur de la précision de la prédiction des espèces. La stratégie de fusion au niveau de décision est la plus appropriée pour améliorer la performance en comparaison des données hyperspectrales seules, mais de légères améliorations sont obtenues (quelques %) en raison de la faible complémentarité des caractéristiques texturales et structurelles en plus des caractéristiques spectrales. L'approche d'ensemble classifier développée dans la deuxième partie permet de classer les espèces d'arbres à partir de références au sol, avec des améliorations significatives par rapport à une approche standard de classification au niveau des caractéristiques. Chaque classifieur d'espèces extrait reflète les attributs spectraux discriminants de l'espèce et peut être relié à l'expertise des botanistes. Enfin, les arbres de rue peuvent être cartographiés grâce au terme d'interaction des MPP proposé qui modélise leurs caractéristiques contextuelles (alignement et hauteurs similaires). De nombreuses améliorations doivent être explorées comme la délimitation plus précise de la couronne de l'arbre, et plusieurs perspectives sont envisageables après cette thèse, parmi lesquelles le suivi de l'état de santé des arbres urbains
With the expansion of urban areas, air pollution and heat island effect are increasing, leading to state of health issues for the inhabitants and global climate changes. In this context, urban trees are a valuable resource for both improving air quality and promoting freshness islands. On the other hand, canopies are subject to specific conditions in the urban environment, causing the spread of diseases and life expectancy decreases among the trees. This thesis explores the potential of remote sensing for the automatic urban tree mapping, from the detection of the individual tree crowns to their species estimation, an essential preliminary task for designing the future green cities, and for an effective vegetation monitoring. Based on airborne hyperspectral, panchromatic and Digital Surface Model data, the first objective of this thesis consists in taking advantage of several data sources for improving the existing urban tree maps, by testing different fusion strategies (feature and decision level fusion). The nature of the results led us to optimize the complementarity of the sources. In particular, the second objective is to investigate deeply the richness of the hyperspectral data, by developing an ensemble classifiers approach based on vegetation indices, where the classifiers are species specific. Finally, the first part highlighted to interest of discriminating the street trees from the other structures of urban trees. In a Marked Point Process framework, the third objective is to detect trees in urban alignment. Through the first objective, this thesis demonstrates that the hyperspectral data are the main driver of the species prediction accuracy. The decision level fusion strategy is the most appropriate one for improving the performance in comparison the hyperspectral data alone, but slight improvements are obtained (a few percent) due to the low complementarity of textural and structural features in addition to the spectral ones. The ensemble classifiers approach developed in the second part allows the tree species to be classified from ground-based references, with significant improvements in comparison to a standard feature level classification approach. Each extracted species classifier reflects the discriminative spectral attributes of the species and can be related to the expertise of botanists. Finally, the street trees can be mapped thanks to the proposed MPP interaction term which models their contextual features (alignment and similar heights). Many improvements have to be explored such as the more accurate tree crown delineation, and several perspectives are conceivable after this thesis, among which the state of health monitoring of the urban trees

Made available in DSpace on 2014-10-09T12:51:34Z (GMT). No. of bitstreams: 0
Made available in DSpace on 2014-10-09T13:58:18Z (GMT). No. of bitstreams: 0
Dissertacao (Mestrado)
IPEN/D
Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP

O Laboratório de Aplicações Ambientais a Laser, situado no Centro de Lasers e Aplicações no IPEN (Instituto de Pesquisas Energéticas e Nucleares), efetua medidas das concentrações de aerossóis atmosféricos, enviando um feixe de laser à atmosfera e coletando a luz retroespalhada. Tal sistema fornece um grande número de parâmetros físicos que devem ser administrados de forma ágil para a obtenção de análises resultantes. Em conseqüência disso, a implementação de um banco de dados tornou-se imprescindível como instrumento de comunicação e visualização gráfica das medidas coletadas. Um critério de classificação destas valiosas informações foi adotado, estabelecendo níveis de armazenamento definidos a partir de características específicas aos tipos de dados determinados. A compilação e automação destas medidas promoverá a integração entre dados, análise e retorno otimizado de resultados das propriedades da atmosfera, propiciando futuras pesquisas e análise de dados.
The LIDAR system (Light Detection and Ranging) laser remote sensing at the Nuclear and Energy Research Institute Laboratory of Environmental Laser Applications allows on line measurements of variations in the concentrations of atmospheric aerosols by sending a laser beam to the atmosphere and collecting the backscattered light. Such a system supplies a great number of physical parameters that must be managed in an agile form to the attainment of a real time analysis. Database implementation therefore becomes an important toll of communication and graphical visualization of measurements. A criterion for classification of this valuable information was adopted, establishing defined levels of storage from specific characteristics of the determined data types. The compilation and automation of these measurements will promote optimized integration between data, analysis and retrieval of the resulting properties and of the atmosphere, improving future research and data analysis.

International Telemetering Conference Proceedings / October 17-20, 1994 / Town & Country Hotel and Conference Center, San Diego, California
Since the Gulf War, there has been significant interest in Theater Missile Defense (TMD) resulting in funding growth from tens of millions of dollars at the time of the Gulf War to $1.7 Billion in 1994. The Ballistic Missile Defense Organization (BMDO) has developed a Theater Missile Defense test and evaluation program that will assess technological feasibility and the degree to which system functionality and performance meet technical and operational requirements. The complexity of the TMD program necessitates a comprehensive test program which includes flight testing, ground testing, and modeling and simulation. This article will provide and overview the requirements and capabilities needed to satisfy these requirements. The data processing, and telemetry communities will play a major role in providing the expertise to support the development of the nation’s future Theater Missile Defense capabilities.

In this work the problem of calibrating Flush Air Data Sensing (FADS) has been addressed. The inverse problem of extracting freestream wind speed and angle of attack from pressure measurements has been solved. The aim of this work was to develop machine learning and statistical tools to optimize design and calibration of FADS systems. Experimental and Computational Fluid Dynamics (EFD and CFD) solve the forward problem of determining the pressure distribution given the wind velocity profile and bluff body geometry. In this work three ways are presented in which machine learning techniques can improve calibration of FADS systems. First, a scattered data approximation scheme, called Sequential Function Approximation (SFA) that successfully solved the current inverse problem was developed. The proposed scheme is a greedy and self-adaptive technique that constructs reliable and robust estimates without any user-interaction. Wind speed and direction prediction algorithms were developed for two FADS problems. One where pressure sensors are installed on a surface vessel and the other where sensors are installed on the Runway Assisted Landing Site (RALS) control tower. Second, a Tikhonov regularization based data-model fusion technique with SFA was developed to fuse low fidelity CFD solutions with noisy and sparse wind tunnel data. The purpose of this data model fusion approach was to obtain high fidelity, smooth and noiseless flow field solutions by using only a few discrete experimental measurements and a low fidelity numerical solution. This physics based regularization technique gave better flow field solutions compared to smoothness based solutions when wind tunnel data is sparse and incomplete. Third, a sequential design strategy was developed with SFA using Active Learning techniques from the machine learning theory and Optimal Design of Experiments from statistics for regression and classification problems. Uncertainty Sampling was used with SFA to demonstrate the effectiveness of active learning versus passive learning on a cavity flow classification problem. A sequential G-optimal design procedure was also developed with SFA for regression problems. The effectiveness of this approach was demonstrated on a simulated problem and the above mentioned FADS problem.

Photochemical grid models are used to evaluate air pollution control strategies by simulating the physical and chemical processes that influence pollutant concentrations. Their accuracy depends on the accuracy of input data used for anthropogenic and biogenic emissions, land surface characteristics, initial and boundary conditions and meteorological conditions. Evaluation of model performance requires sufficient ambient data. This work develops approaches for applying satellite data to allow more frequent and timely estimates of parameters required to estimate emissions and pollutant removal processes for regional air quality modeling. Land use and land cover (LULC) data prepared from remote sensing satellite data were evaluated for use as inputs to photochemical grid models for estimating dry deposition velocities and biogenic emissions. The results indicated that satellite-based data derived from the Moderate Resolution Imaging Spectroradiometer instrument can be used to provide periodic updates to LULC information used in photochemical models. The sensitivity of predicted ozone concentrations to LULC data used for biogenic emission estimates was examined by comparing the database currently used for modeling in southeastern Texas with a new database prepared from Landsat satellite imagery and field data. The satellite data and image classification techniques provide useful tools for mapping and monitoring changes in LULC. However, field validation is necessary to link species and biomass densities to the classification system needed for accurate biogenic emissions estimates, especially in areas that have dense concentrations of species that emit high levels of biogenic hydrocarbons. The application of NO2 measurements from the Ozone Monitoring Instrument (OMI) to validation of NOx emission estimates and identification of emission sources for regional air quality modeling for Texas was examined. OMI observations can be used to identify regions with changes in emissions over time or where estimates have large uncertainties and to evaluate the effectiveness of emission reduction strategies. For example, in the Dallas-Fort Worth area, observed NO2 column densities from OMI indicate that emission controls are less effective than anticipated due to increased area source emissions. The techniques developed in this work have broad applicability in the advancement of methods for including satellite remote sensing data in regional air quality modeling.
text

碩士
朝陽科技大學
環境工程與管理系碩士班
92
Vehicle emission is a major source of air pollution in urban areas in Taiwan. Based on the 2002 official data, these registered automobiles emit substantial portion of air pollution, with 21% of HC, 71% of CO, and 44% of NOx, of the total air pollution. Above all, the top 10% gross emitter to contribute over 50% of the total auto emissions. To identify these heavy emission vehicles for inspection and maintenance is an effective method to reduce air pollution. Existing regulation in Taiwan is based on the idle testing data; however, it will waste a lot of time and human resource. Therefore, ROCEPA has devoted large amount of effects to develop remote sensing technology for quick screening of heavy polluters with some success. Moreover, the uncertainty resulted from devices error, it will cause citizen’s complaints. To build the model by remote sensing data and idle data if it can screen gross emitter of air pollution. Then, to put into inspection and maintenance system, it will improve the problem that vehicles are in violation. High emitter profile models have been built in this paper with the genetic algorithm-based logistic regression model and the genetic algorithm-based neural network. These two models will compare with logistic regression and neural network, to find the advantages and disadvantages of each model that offer the best model to screen gross emitter and the other models’ applications. To identify these heavy emission vehicles for inspection and maintenance is an ineffective method to reduce air pollution from previous studies. This study presents that the genetic algorithm-based neural network which use remote sensing data is better than other models. Especially, the target function of the genetic algorithm-based neural network which is has the best predictive ratio to screen gross emitter. Moreover, variables in models by means of scaling will aid genetic algorithm for the global optimization in aspect of the speed of the convergence. Still, the genetic algorithm-based logistic regression model and the genetic algorithm-based neural network both can offer the decision of the remote sensing affairs for ROCEPA. In addition, the result of this study also indicate that it makes models false to screen high emission vehicles when the age of cars are up to between 7 and 10 years. Therefore, this study suggested that vehicles will focus inspection and maintenance system on the age of cars range from 7 and 10 years. Keywords: Remote Sensing, Genetic Algorithms, Logistic Regression, Artificial Neural Work.

Indiana University-Purdue University Indianapolis (IUPUI)
Objective: "The purpose of this research is: Study the use of Moderate Resolution Imaging Spectroradiometer (MODIS) data in retrieving the aerosol optical depth (AOD) over Indiana State at high resolution of 500 meters. Examine the potential of using the resulted AOD data as an indicator of particulate air pollution by comparing the satellite derived AOD data with the ground measurements (provided from the continuous air monitors available over the study area). If an association should be found, AOD data would be used to map particulate matter (PM) concentration. Assess current and future ambient concentrations of air pollutants in the State of Indiana using the AOD."

M.Phil.
This work proposes a fast local image feature detector and descriptor that is im- plementable on a GPU. The BFROST feature detector is the first published GPU implementation of the popular FAST detector. A simple but novel method of feature orientation estimation which can be calculated in constant time is proposed. The robustness and reliability of the orientation estimation is validated against rotation invariant descriptors such as SIFT and SURF. Furthermore, the BFROST feature descriptor is robust to noise, scalable, rotation invariant, fast to compute in parallel and maintains low memory usage. It is demonstrated that BFROST is usable in real-time applications such as vision-based localization and mapping of images captured from micro aerial platforms.

This thesis proposes a DIY environmental sensing approach that empowers citizens to reinvigorate people's awareness of, and concern for, pollution. Current air pollution measuring techniques are described, and a new concept of participatory sensing is presented. I argue that technological advances in sensing, computation, storage, and communication now have the power to turn the near-ubiquitous mobile phone into a global mobile sensing device, and commence the participatory paradigm employing amateurs in environmental data collection. To test the thesis, PAIR, a prototype with interchangeable sensor, was developed. It aims to enable people to sense environment on-the-go and provide users with immediate feedback. Such data can make people learn about their environment, make them aware of air pollution causes, and eventually even bring behavioral changes. Consequently, a user survey and interviews identify strengths and weaknesses of the mobile sensing device, and based on the usability requirements, we conclude design recommendations for further development. Finally, we identify the main benefits amateur data collection and participatory sensing represent for urban dwellers, and we evaluate issues and challenges they have yet to overcome.