University of Texas at Arlington

About: University of Texas at Arlington is a education organization based out in Arlington, Texas, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 11758 authors who have published 28598 publications receiving 801626 citations. The organization is also known as: UT Arlington & University of Texas-Arlington.

Analysis and control of a buck DC-DC converter operating with constant power load in sea and undersea vehicles

Abstract: Power-electronics-based zonal direct current (dc) power distribution systems are being considered for sea and undersea vehicles. The stability of the dc power-electronics-based power distribution systems is a significant design consideration because of the potential for negative-impedance-induced instabilities. In this paper, the dynamic properties and control of a buck converter feeding a downstream dc-dc converter are studied. The controller in this system combines an instantaneous current feedback loop using hysteresis with a proportional-integral (PI) algorithm to regulate the output voltage of the converter. Based on a large-signal-averaged model of the converter, the stability-in-large around the operation point is presented. The complete analysis is carried out considering a buck dc-dc converter operating with a constant power load (CPL). Simulations and experimental results are provided to verify the analysis.

Agreeableness as a predictor of aggression in adolescence

Abstract: This multi-method research linked the Big Five personality dimensions to aggression in early adolescence. Agreeableness was the personality dimension of focus because this dimension is associated with motives to maintain positive interpersonal relations. In two studies, middle school children were assessed on the Big Five domains of personality. Study 1 showed that agreeableness was associated with both indirect and direct aggression. In addition, the link between agreeableness and aggression was strongest for direct strategies. Study 2 examined the hypotheses that agreeableness predicts social cognitions associated with aggression, peer reports of direct aggression, and teacher reports of adjustment. Agreeableness predicted peer reports of aggression and social cognitions associated with aggression. In addition, aggression mediated the link between agreeableness and adjustment. Results suggest that of the Big Five dimensions, Agreeableness is most closely associated with processes and outcomes related to aggression in adolescents.

Protecting Location Privacy in Sensor Networks against a Global Eavesdropper

Abstract: While many protocols for sensor network security provide confidentiality for the content of messages, contextual information usually remains exposed Such contextual information can be exploited by an adversary to derive sensitive information such as the locations of monitored objects and data sinks in the field Attacks on these components can significantly undermine any network application Existing techniques defend the leakage of location information from a limited adversary who can only observe network traffic in a small region However, a stronger adversary, the global eavesdropper, is realistic and can defeat these existing techniques This paper first formalizes the location privacy issues in sensor networks under this strong adversary model and computes a lower bound on the communication overhead needed for achieving a given level of location privacy The paper then proposes two techniques to provide location privacy to monitored objects (source-location privacy)-periodic collection and source simulation-and two techniques to provide location privacy to data sinks (sink-location privacy)-sink simulation and backbone flooding These techniques provide trade-offs between privacy, communication cost, and latency Through analysis and simulation, we demonstrate that the proposed techniques are efficient and effective for source and sink-location privacy in sensor networks

Stable feature selection via dense feature groups

Abstract: Many feature selection algorithms have been proposed in the past focusing on improving classification accuracy. In this work, we point out the importance of stable feature selection for knowledge discovery from high-dimensional data, and identify two causes of instability of feature selection algorithms: selection of a minimum subset without redundant features and small sample size. We propose a general framework for stable feature selection which emphasizes both good generalization and stability of feature selection results. The framework identifies dense feature groups based on kernel density estimation and treats features in each dense group as a coherent entity for feature selection. An efficient algorithm DRAGS (Dense Relevant Attribute Group Selector) is developed under this framework. We also introduce a general measure for assessing the stability of feature selection algorithms. Our empirical study based on microarray data verifies that dense feature groups remain stable under random sample hold out, and the DRAGS algorithm is effective in identifying a set of feature groups which exhibit both high classification accuracy and stability.