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- Genre: Doctoral Dissertation
Description
Energy harvesting from ambient is important to configuring Wireless Sensor Networks (WSN) for environmental data collecting. In this work, highly flexible thermoelectric generators (TEGs) have been studied and fabricated to supply power to the wireless sensor notes used for data collecting in hot spring environment. The fabricated flexible TEGs can be easily deployed on the uneven surface of heated rocks at the rim of hot springs. By employing the temperature gradient between the hot rock surface and the air, these TEGs can generate power to extend the battery lifetime of the sensor notes and therefore reduce multiple batteries changes where the environment is usually harsh in hot springs. Also, they show great promise for self-powered wireless sensor notes. Traditional thermoelectric material bismuth telluride (Bi2Te3) and advanced MEMS (Microelectromechanical systems) thin film techniques were used for the fabrication. Test results show that when a flexible TEG array with an area of 3.4cm2 was placed on the hot plate surface of 80°C in the air under room temperature, it had an open circuit voltage output of 17.6mV and a short circuit current output of 0.53mA. The generated power was approximately 7mW/m2.
On the other hand, high pressure, temperatures that can reach boiling, and the pH of different hot springs ranging from <2 to >9 make hot spring ecosystem a unique environment that is difficult to study. WSN allows many scientific studies in harsh environments that are not feasible with traditional instrumentation. However, wireless pH sensing for long time in situ data collection is still challenging for two reasons. First, the existing commercial-off-the-shelf pH meters are frequent calibration dependent; second, biofouling causes significant measurement error and drift. In this work, 2-dimentional graphene pH sensors were studied and calibration free graphene pH sensor prototypes were fabricated. Test result shows the resistance of the fabricated device changes linearly with the pH values (in the range of 3-11) in the surrounding liquid environment. Field tests show graphene layer greatly prevented the microbial fouling. Therefore, graphene pH sensors are promising candidates that can be effectively used for wireless pH sensing in exploration of hot spring ecosystems.
On the other hand, high pressure, temperatures that can reach boiling, and the pH of different hot springs ranging from <2 to >9 make hot spring ecosystem a unique environment that is difficult to study. WSN allows many scientific studies in harsh environments that are not feasible with traditional instrumentation. However, wireless pH sensing for long time in situ data collection is still challenging for two reasons. First, the existing commercial-off-the-shelf pH meters are frequent calibration dependent; second, biofouling causes significant measurement error and drift. In this work, 2-dimentional graphene pH sensors were studied and calibration free graphene pH sensor prototypes were fabricated. Test result shows the resistance of the fabricated device changes linearly with the pH values (in the range of 3-11) in the surrounding liquid environment. Field tests show graphene layer greatly prevented the microbial fouling. Therefore, graphene pH sensors are promising candidates that can be effectively used for wireless pH sensing in exploration of hot spring ecosystems.
ContributorsHan, Ruirui (Author) / Yu, Hongyu (Thesis advisor) / Jiang, Hanqing (Committee member) / Yu, Hongbin (Committee member) / Garnero, Edward (Committee member) / Li, Mingming (Committee member) / Arizona State University (Publisher)
Created2018
Description
Cyber-Physical Systems (CPS) are being used in many safety-critical applications. Due to the important role in virtually every aspect of human life, it is crucial to make sure that a CPS works properly before its deployment. However, formal verification of CPS is a computationally hard problem. Therefore, lightweight verification methods such as testing and monitoring of the CPS are considered in the industry. The formal representation of the CPS requirements is a challenging task. In addition, checking the system outputs with respect to requirements is a computationally complex problem. In this dissertation, these problems for the verification of CPS are addressed. The first method provides a formal requirement analysis framework which can find logical issues in the requirements and help engineers to correct the requirements. Also, a method is provided to detect tests which vacuously satisfy the requirement because of the requirement structure. This method is used to improve the test generation framework for CPS. Finally, two runtime verification algorithms are developed for off-line/on-line monitoring with respect to real-time requirements. These monitoring algorithms are computationally efficient, and they can be used in practical applications for monitoring CPS with low runtime overhead.
ContributorsDokhanchi, Adel (Author) / Fainekos, Georgios (Thesis advisor) / Lee, Yann-Hang (Committee member) / Sarjoughian, Hessam S. (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2017
Description
Node proximity measures are commonly used for quantifying how nearby or otherwise related to two or more nodes in a graph are. Node significance measures are mainly used to find how much nodes are important in a graph. The measures of node proximity/significance have been highly effective in many predictions and applications. Despite their effectiveness, however, there are various shortcomings. One such shortcoming is a scalability problem due to their high computation costs on large size graphs and another problem on the measures is low accuracy when the significance of node and its degree in the graph are not related. The other problem is that their effectiveness is less when information for a graph is uncertain. For an uncertain graph, they require exponential computation costs to calculate ranking scores with considering all possible worlds.
In this thesis, I first introduce Locality-sensitive, Re-use promoting, approximate Personalized PageRank (LR-PPR) which is an approximate personalized PageRank calculating node rankings for the locality information for seeds without calculating the entire graph and reusing the precomputed locality information for different locality combinations. For the identification of locality information, I present Impact Neighborhood Indexing (INI) to find impact neighborhoods with nodes' fingerprints propagation on the network. For the accuracy challenge, I introduce Degree Decoupled PageRank (D2PR) technique to improve the effectiveness of PageRank based knowledge discovery, especially considering the significance of neighbors and degree of a given node. To tackle the uncertain challenge, I introduce Uncertain Personalized PageRank (UPPR) to approximately compute personalized PageRank values on uncertainties of edge existence and Interval Personalized PageRank with Integration (IPPR-I) and Interval Personalized PageRank with Mean (IPPR-M) to compute ranking scores for the case when uncertainty exists on edge weights as interval values.
In this thesis, I first introduce Locality-sensitive, Re-use promoting, approximate Personalized PageRank (LR-PPR) which is an approximate personalized PageRank calculating node rankings for the locality information for seeds without calculating the entire graph and reusing the precomputed locality information for different locality combinations. For the identification of locality information, I present Impact Neighborhood Indexing (INI) to find impact neighborhoods with nodes' fingerprints propagation on the network. For the accuracy challenge, I introduce Degree Decoupled PageRank (D2PR) technique to improve the effectiveness of PageRank based knowledge discovery, especially considering the significance of neighbors and degree of a given node. To tackle the uncertain challenge, I introduce Uncertain Personalized PageRank (UPPR) to approximately compute personalized PageRank values on uncertainties of edge existence and Interval Personalized PageRank with Integration (IPPR-I) and Interval Personalized PageRank with Mean (IPPR-M) to compute ranking scores for the case when uncertainty exists on edge weights as interval values.
ContributorsKim, Jung Hyun (Author) / Candan, K. Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Tong, Hanghang (Committee member) / Sapino, Maria Luisa (Committee member) / Arizona State University (Publisher)
Created2017
Description
In this research work, a novel control system strategy for the robust control of an unmanned ground vehicle is proposed. This strategy is motivated by efforts to mitigate the problem for scenarios in which the human operator is unable to properly communicate with the vehicle. This novel control system strategy consisted of three major components: I.) Two independent intelligent controllers, II.) An intelligent navigation system, and III.) An intelligent controller tuning unit. The inner workings of the first two components are based off the Brain Emotional Learning (BEL), which is a mathematical model of the Amygdala-Orbitofrontal, a region in mammalians brain known to be responsible for emotional learning. Simulation results demonstrated the implementation of the BEL model to be very robust, efficient, and adaptable to dynamical changes in its application as controller and as a sensor fusion filter for an unmanned ground vehicle. These results were obtained with significantly less computational cost when compared to traditional methods for control and sensor fusion. For the intelligent controller tuning unit, the implementation of a human emotion recognition system was investigated. This system was utilized for the classification of driving behavior. Results from experiments showed that the affective states of the driver are accurately captured. However, the driver's affective state is not a good indicator of the driver's driving behavior. As a result, an alternative method for classifying driving behavior from the driver's brain activity was explored. This method proved to be successful at classifying the driver's behavior. It obtained results comparable to the common approach through vehicle parameters. This alternative approach has the advantage of directly classifying driving behavior from the driver, which is of particular use in UGV domain because the operator's information is readily available. The classified driving mode was used tune the controllers' performance to a desired mode of operation. Such qualities are required for a contingency control system that would allow the vehicle to operate with no operator inputs.
ContributorsVargas-Clara, Alvaro (Author) / Redkar, Sangram (Thesis advisor) / McKenna, Anna (Committee member) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2015
Description
In this dissertation, two problems are addressed in the verification and control of Cyber-Physical Systems (CPS):
1) Falsification: given a CPS, and a property of interest that the CPS must satisfy under all allowed operating conditions, does the CPS violate, i.e. falsify, the property?
2) Conformance testing: given a model of a CPS, and an implementation of that CPS on an embedded platform, how can we characterize the properties satisfied by the implementation, given the properties satisfied by the model?
Both problems arise in the context of Model-Based Design (MBD) of CPS: in MBD, the designers start from a set of formal requirements that the system-to-be-designed must satisfy.
A first model of the system is created.
Because it may not be possible to formally verify the CPS model against the requirements, falsification tries to verify whether the model satisfies the requirements by searching for behavior that violates them.
In the first part of this dissertation, I present improved methods for finding falsifying behaviors of CPS when properties are expressed in Metric Temporal Logic (MTL).
These methods leverage the notion of robust semantics of MTL formulae: if a falsifier exists, it is in the neighborhood of local minimizers of the robustness function.
The proposed algorithms compute descent directions of the robustness function in the space of initial conditions and input signals, and provably converge to local minima of the robustness function.
The initial model of the CPS is then iteratively refined by modeling previously ignored phenomena, adding more functionality, etc., with each refinement resulting in a new model.
Many of the refinements in the MBD process described above do not provide an a priori guaranteed relation between the successive models.
Thus, the second problem above arises: how to quantify the distance between two successive models M_n and M_{n+1}?
If M_n has been verified to satisfy the specification, can it be guaranteed that M_{n+1} also satisfies the same, or some closely related, specification?
This dissertation answers both questions for a general class of CPS, and properties expressed in MTL.
1) Falsification: given a CPS, and a property of interest that the CPS must satisfy under all allowed operating conditions, does the CPS violate, i.e. falsify, the property?
2) Conformance testing: given a model of a CPS, and an implementation of that CPS on an embedded platform, how can we characterize the properties satisfied by the implementation, given the properties satisfied by the model?
Both problems arise in the context of Model-Based Design (MBD) of CPS: in MBD, the designers start from a set of formal requirements that the system-to-be-designed must satisfy.
A first model of the system is created.
Because it may not be possible to formally verify the CPS model against the requirements, falsification tries to verify whether the model satisfies the requirements by searching for behavior that violates them.
In the first part of this dissertation, I present improved methods for finding falsifying behaviors of CPS when properties are expressed in Metric Temporal Logic (MTL).
These methods leverage the notion of robust semantics of MTL formulae: if a falsifier exists, it is in the neighborhood of local minimizers of the robustness function.
The proposed algorithms compute descent directions of the robustness function in the space of initial conditions and input signals, and provably converge to local minima of the robustness function.
The initial model of the CPS is then iteratively refined by modeling previously ignored phenomena, adding more functionality, etc., with each refinement resulting in a new model.
Many of the refinements in the MBD process described above do not provide an a priori guaranteed relation between the successive models.
Thus, the second problem above arises: how to quantify the distance between two successive models M_n and M_{n+1}?
If M_n has been verified to satisfy the specification, can it be guaranteed that M_{n+1} also satisfies the same, or some closely related, specification?
This dissertation answers both questions for a general class of CPS, and properties expressed in MTL.
ContributorsAbbas, Houssam Y (Author) / Fainekos, Georgios (Thesis advisor) / Duman, Tolga (Thesis advisor) / Mittelmann, Hans (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2015
Description
Nonlinear responses in the dynamics of climate system could be triggered by small change of forcing. Interactions between different components of Earth’s climate system are believed to cause abrupt and catastrophic transitions, of which anthropogenic forcing is a major and the most irreversible driver. Meantime, in the face of global climate change, extreme climatic events, such as extreme precipitations, heatwaves, droughts, etc., are projected to be more frequent, more intense, and longer in duration. These nonlinear responses in climate dynamics from tipping points to extreme events pose serious threats to human society on a large scale. Understanding the physical mechanisms behind them has potential to reduce related risks through different ways. The overarching objective of this dissertation is to quantify complex interactions, detect tipping points, and explore propagations of extreme events in the hydroclimate system from a new structure-based perspective, by integrating climate dynamics, causal inference, network theory, spectral analysis, and machine learning. More specifically, a network-based framework is developed to find responses of hydroclimate system to potential critical transitions in climate. Results show that system-based early warning signals towards tipping points can be located successfully, demonstrated by enhanced connections in the network topology. To further evaluate the long-term nonlinear interactions among the U.S. climate regions, causality inference is introduced and directed complex networks are constructed from climatology records over one century. Causality networks reveal that the Ohio valley region acts as a regional gateway and mediator to the moisture transport and thermal transfer in the U.S. Furthermore, it is found that cross-regional causality variability manifests intrinsic frequency ranging from interannual to interdecadal scales, and those frequencies are associated with the physics of climate oscillations. Besides the long-term climatology, this dissertation also aims to explore extreme events from the system-dynamic perspective, especially the contributions of human-induced activities to propagation of extreme heatwaves in the U.S. cities. Results suggest that there are long-range teleconnections among the U.S. cities and supernodes in heatwave spreading. Findings also confirm that anthropogenic activities contribute to extreme heatwaves by the fact that causality during heatwaves is positively associated with population in megacities.
ContributorsYang, Xueli (Author) / Yang, Zhihua (Thesis advisor) / Lai, Ying-Cheng (Committee member) / Li, Qi (Committee member) / Xu, Tianfang (Committee member) / Zeng, Ruijie (Committee member) / Arizona State University (Publisher)
Created2023
Description
Failures in the cold chain, the system of refrigerated storage and transport that provides fresh produce or other essentials to be maintained at desired temperatures and environmental conditions, lead to food and energy waste. The mini container (MC) concept is introduced as an alternative to conventional refrigerated trucks (“reefers”), particularly for small growers. The energy consumption and corresponding GHG emissions for transporting tomatoes in two cities representing contrasting climates is analyzed for conventional reefers and the proposed mini containers. The results show that, for partial reefer loads, using the MCs reduces energy consumption and GHG emissions. The transient behavior of the vapor compression refrigeration cycle is analyzed by considering each component as a “lumped” system, and the resulting sub-models are solved using the Runge Kutta 4th-order method in a MATLAB code at hot and cold ambient temperatures. The time needed to reach steady state temperatures and the temperature values are determined. The maximum required compressor work in the transient phase and at steady state are computed, and as expected, as the ambient temperature increases, both values increase. Finally, the average coefficient of performance (COP) is determined for varying heat transfer coefficient values for the condenser and for the evaporator. The results show that the average COP increases as heat transfer coefficient values for the condenser and the evaporator increase. Starting the system from rest has an adverse effect on the COP due to the higher compressor load needed to change the temperature of the condenser and the evaporator. Finally, the impact on COP is analyzed by redirecting a fraction of the cold exhaust air to provide supplemental cooling of the condenser. It is noted that cooling the condenser improves the system's performance better than cooling the fresh air at 0% of returned air to the system.To sum up, the dissertation shows that the comparison between the conventional reefer and the MC illustrates the promising advantages of the MC, then a transient analysis is developed for deeply understanding the behaviors of the system component parameters, which leads finally to improvements in the system to enhance its performance.
ContributorsSyam, Mahmmoud Muhammed (Author) / Phelan, Patrick (Thesis advisor) / Villalobos, Rene (Thesis advisor) / Huang, Huei-Ping (Committee member) / Bocanegra, Luis (Committee member) / Al Omari, Salah (Committee member) / Arizona State University (Publisher)
Created2023
Description
Waste plastic is considered an environmental pollutant because it is not biodegradable. Therefore, there is increased interest in the use of recycled plastic in pavement construction. Polyethylene terephthalate (PET) is a thermoplastic polymer that is commonly used in the manufacturing of containers and bottles. Waste PET is a durable material that has shown enhancement in performance when introduced into asphalt binder and asphalt mixtures. However, PET particles tend to separate from asphalt because of differences in density, molecular structure, molecular weight, and viscosity, leading to inadequate dispersion of PET particles in the asphalt. This incompatibility between PET and asphalt causes segregation, where storage stability becomes an issue. To solve this problem, applying a surface activation on the PET using another abundant urban waste (waste vegetable oil) was examined in this study, showing this method can be effective to enhance PET-asphalt interactions and consequently the storage stability of PET-modified asphalt. To ensure proper surface activation, it is important to thoroughly understand the chemo-mechanics of asphalt containing PET particles as well as the underlying interaction mechanism at the molecular level. Therefore, this study integrates a multi-scale approach using computational modeling based on density functional theory along with laboratory experiments to provide an in-depth understanding of the mechanisms of interaction between surface-activated PET and asphalt. To do so, the efficacy of bio-oil treatment was examined in terms of both the surface-activation capability and the durability of the resulting PET-modified asphalt. It was found that the grafted bio-oil on the PET particles can make a strong interaction with bituminous composites, leading to enhancing the durability and extending the service life of asphalt pavement by reducing the diffusion of free radicals and moisture into the bulk. The study was further extended to study the effect of coating the PET with biochar, showing the latter coating can improve the mechanical properties of the PET-modified asphalt and the adsorption behavior of the PET for volatile organic compounds. The performance of the waste PET was compared with another widely used modifier, crumb rubber.
ContributorsAldagari, Sand (Author) / Fini, Elham (Thesis advisor) / Kaloush, Kamil (Committee member) / Ozer, Hasan (Committee member) / Arizona State University (Publisher)
Created2024