University of Windsor

About: University of Windsor is a education organization based out in Windsor, Ontario, Canada. It is known for research contribution in the topics: Population & Argumentation theory. The organization has 10654 authors who have published 22307 publications receiving 435906 citations. The organization is also known as: UWindsor & Assumption University of Windsor.

The electrical performance of polymeric insulating materials under accelerated aging in a fog chamber

Abstract: The materials evaluated in fog produced from low (250 mu s/cm) and high (1000 mu s/cm) conductivity water include cylindrical rod samples of high-temperature-vulcanized silicone rubber and ethylene-propylene-diene monomer rubber (EPDM) containing various amounts of either alumina trihydrate or silica fillers, or both. Comparison is made with material performance results obtained with AC, which was reported in an earlier study. In both low- and high-conductivity fog, the time to failure with AC and +DC was very similar, but a reduction by a factor of about four was observed in the time to failure with -DC. For both AC and DC, silicone rubber performed better than EPDM samples in low-conductivity fog, while the order of performance was reversed in high-conductivity fog. A theoretical model to determine the effect of dry band discharges on material is presented. Good agreement between the predicted behavior and the experimental findings is shown. >

A fuzzy neural network and its application to pattern recognition

Abstract: Defines four types of fuzzy neurons and proposes the structure of a four-layer feedforward fuzzy neural network (FNN) and its associated learning algorithm. The proposed four-layer FNN performs well when used to recognize shifted and distorted training patterns. When an input pattern is provided, the network first fuzzifies this pattern and then computes the similarities of this pattern to all of the learned patterns. The network then reaches a conclusion by selecting the learned pattern with the highest similarity and gives a nonfuzzy output. The 26 English alphabets and the 10 Arabic numerals, each represented by 16/spl times/16 pixels, were used as original training patterns. In the simulation experiments, the original 36 exemplar patterns were shifted in eight directions by 1 pixel (6.25% to 8.84%) and 2 pixels (12.5% to 17.68%). After the FNN has been trained by the 36 exemplar patterns, the FNN can recall all of the learned patterns with 100% recognition rate. It can also recognize patterns shifted by 1 pixel in eight directions with 100% recognition rate and patterns shifted by 2 pixels in eight directions with an average recognition rate of 92.01%. After the FNN has been trained by the 36 exemplar patterns and 72 shifted patterns, it can recognize patterns shifted by 1 pixel with 100% recognition rate and patterns shifted by 2 pixels with an average recognition rate of 98.61%. The authors have also tested the FNN with 10 kinds of distorted patterns for each of the 36 exemplars. The FNN can recognize all of the distorted patterns with 100% recognition rate. The proposed FNN can also be adapted for applications in some other pattern recognition problems. >

Popularity and propagule pressure: determinants of introduction and establishment of aquarium fish

Abstract: Propagule pressure is frequently cited as an important determinant of invasion success for terrestrial taxa, but its importance for aquatic species is unclear. Using data on aquarium fishes in stores and historical records of fish introduced and established in Canadian and United States waters, we show clear relationships exist between frequency of occurrence in shops and likelihood of introduction and of establishment. Introduced and established taxa are also typically larger than those available from stores, consistent with the propagule pressure hypothesis in that larger fish may be released more frequently due to outgrowing their aquaria. Attempts to reduce the numbers of introductions may be the most practical mechanism to reduce the number of new successful invasions.

Oboe: auto-tuning video ABR algorithms to network conditions

Abstract: Most content providers are interested in providing good video delivery QoE for all users, not just on average. State-of-the-art ABR algorithms like BOLA and MPC rely on parameters that are sensitive to network conditions, so may perform poorly for some users and/or videos. In this paper, we propose a technique called Oboe to auto-tune these parameters to different network conditions. Oboe pre-computes, for a given ABR algorithm, the best possible parameters for different network conditions, then dynamically adapts the parameters at run-time for the current network conditions. Using testbed experiments, we show that Oboe significantly improves BOLA, MPC, and a commercially deployed ABR. Oboe also betters a recently proposed reinforcement learning based ABR, Pensieve, by 24% on average on a composite QoE metric, in part because it is able to better specialize ABR behavior across different network states.