Matching Items (294)
Description
High-efficiency DC-DC converters make up one of the important blocks of state-of-the-art power supplies. The trend toward high level of transistor integration has caused load current demands to grow significantly. Supplying high output current and minimizing output current ripple has been a driving force behind the evolution of Multi-phase topologies. Ability to supply large output current with improved efficiency, reduction in the size of filter components, improved transient response make multi-phase topologies a preferred choice for low voltage-high current applications.
Current sensing capability inside a system is much sought after for applications which include Peak-current mode control, Current limiting, Overload protection. Current sensing is extremely important for current sharing in Multi-phase topologies. Existing approaches such as Series resistor, SenseFET, inductor DCR based current sensing are simple but their drawbacks such low efficiency, low accuracy, limited bandwidth demand a novel current sensing scheme.
This research presents a systematic design procedure of a 5V - 1.8V, 8A 4-Phase Buck regulator with a novel current sensing scheme based on replication of the inductor current. The proposed solution consists of detailed system modeling in PLECS which includes modification of the peak current mode model to accommodate the new current sensing element, derivation of power-stage and Plant transfer functions, Controller design. The proposed model has been verified through PLECS simulations and compared with a transistor-level implementation of the system. The time-domain parameters such as overshoot and settling-time simulated through transistor-level
implementation is in close agreement with the results obtained from the PLECS model.
Current sensing capability inside a system is much sought after for applications which include Peak-current mode control, Current limiting, Overload protection. Current sensing is extremely important for current sharing in Multi-phase topologies. Existing approaches such as Series resistor, SenseFET, inductor DCR based current sensing are simple but their drawbacks such low efficiency, low accuracy, limited bandwidth demand a novel current sensing scheme.
This research presents a systematic design procedure of a 5V - 1.8V, 8A 4-Phase Buck regulator with a novel current sensing scheme based on replication of the inductor current. The proposed solution consists of detailed system modeling in PLECS which includes modification of the peak current mode model to accommodate the new current sensing element, derivation of power-stage and Plant transfer functions, Controller design. The proposed model has been verified through PLECS simulations and compared with a transistor-level implementation of the system. The time-domain parameters such as overshoot and settling-time simulated through transistor-level
implementation is in close agreement with the results obtained from the PLECS model.
ContributorsBurli, Venkatesh (Author) / Bakkaloglu, Bertan (Thesis advisor) / Garrity, Douglas (Committee member) / Kitchen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2017
Description
The media disperses a large amount of information daily pertaining to political events social movements, and societal conflicts. Media pertaining to these topics, no matter the format of publication used, are framed a particular way. Framing is used not for just guiding audiences to desired beliefs, but also to fuel societal change or legitimize/delegitimize social movements. For this reason, tools that can help to clarify when changes in social discourse occur and identify their causes are of great use. This thesis presents a visual analytics framework that allows for the exploration and visualization of changes that occur in social climate with respect to space and time. Focusing on the links between data from the Armed Conflict Location and Event Data Project (ACLED) and a streaming RSS news data set, users can be cued into interesting events enabling them to form and explore hypothesis. This visual analytics framework also focuses on improving intervention detection, allowing users to hypothesize about correlations between events and happiness levels, and supports collaborative analysis.
ContributorsSteptoe, Michael (Author) / Maciejewski, Ross (Thesis advisor) / Davulcu, Hasan (Committee member) / Corman, Steven (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
A major challenge in automated text analysis is that different words are used for related concepts. Analyzing text at the surface level would treat related concepts (i.e. actors, actions, targets, and victims) as different objects, potentially missing common narrative patterns. Generalized concepts are used to overcome this problem. Generalization may result into word sense disambiguation failing to find similarity. This is addressed by taking into account contextual synonyms. Concept discovery based on contextual synonyms reveal information about the semantic roles of the words leading to concepts. Merger engine generalize the concepts so that it can be used as features in learning algorithms.
ContributorsKedia, Nitesh (Author) / Davulcu, Hasan (Thesis advisor) / Corman, Steve R (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2015