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- All Subjects: electrochemical cell
- All Subjects: Signal Processing
- Genre: Masters Thesis
- Creators: Zhang, Junshan
Description
This thesis examines the application of statistical signal processing approaches to data arising from surveys intended to measure psychological and sociological phenomena underpinning human social dynamics. The use of signal processing methods for analysis of signals arising from measurement of social, biological, and other non-traditional phenomena has been an important and growing area of signal processing research over the past decade. Here, we explore the application of statistical modeling and signal processing concepts to data obtained from the Global Group Relations Project, specifically to understand and quantify the effects and interactions of social psychological factors related to intergroup conflicts. We use Bayesian networks to specify prospective models of conditional dependence. Bayesian networks are determined between social psychological factors and conflict variables, and modeled by directed acyclic graphs, while the significant interactions are modeled as conditional probabilities. Since the data are sparse and multi-dimensional, we regress Gaussian mixture models (GMMs) against the data to estimate the conditional probabilities of interest. The parameters of GMMs are estimated using the expectation-maximization (EM) algorithm. However, the EM algorithm may suffer from over-fitting problem due to the high dimensionality and limited observations entailed in this data set. Therefore, the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) are used for GMM order estimation. To assist intuitive understanding of the interactions of social variables and the intergroup conflicts, we introduce a color-based visualization scheme. In this scheme, the intensities of colors are proportional to the conditional probabilities observed.
ContributorsLiu, Hui (Author) / Taylor, Thomas (Thesis advisor) / Cochran, Douglas (Thesis advisor) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2012
Description
In this thesis, an approach to develop low-frequency accelerometer based on molecular electronic transducers (MET) in an electrochemical cell is presented. Molecular electronic transducers are a class of inertial sensors which are based on an electrochemical mechanism. Motion sensors based on MET technology consist of an electrochemical cell that can be used to detect the movement of liquid electrolyte between electrodes by converting it to an output current. Seismometers based on MET technology are attractive for planetary applications due to their high sensitivity, low noise, small size and independence on the direction of sensitivity axis. In addition, the fact that MET based sensors have a liquid inertial mass with no moving parts makes them rugged and shock tolerant (basic survivability has been demonstrated to >20 kG).
A Zn-Cu electrochemical cell (Galvanic cell) was applied in the low-frequency accelerometer. Experimental results show that external vibrations (range from 18 to 70 Hz) were successfully detected by this accelerometer as reactions Zn→〖Zn〗^(2+)+2e^- occurs around the anode and 〖Cu〗^(2+)+2e^-→Cu around the cathode. Accordingly, the sensitivity of this MET device design is to achieve 10.4 V/G at 18 Hz. And the sources of noise have been analyzed.
A Zn-Cu electrochemical cell (Galvanic cell) was applied in the low-frequency accelerometer. Experimental results show that external vibrations (range from 18 to 70 Hz) were successfully detected by this accelerometer as reactions Zn→〖Zn〗^(2+)+2e^- occurs around the anode and 〖Cu〗^(2+)+2e^-→Cu around the cathode. Accordingly, the sensitivity of this MET device design is to achieve 10.4 V/G at 18 Hz. And the sources of noise have been analyzed.
ContributorsZhao, Zuofeng (Author) / Yu, Hongyu (Thesis advisor) / Zhang, Junshan (Committee member) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2015