Search Constraints
Filtering by:
Date Created
2015
Remove constraint Date Created: 2015
Language
English
Remove constraint Language: English
Resource Type
Conference Proceeding
Remove constraint Resource Type: Conference Proceeding
1 - 6 of 6
Number of results to display per page
Search Results
-
- Resource Type:
- Conference Proceeding
- Creator:
- Bucking, Scott and Cotton, James S.
- Abstract:
- Net zero energy (NZE) communities are becoming pivotal to the energy vision of developers. Communities that produce as much energy as they consume provide many benefits, such as reducing life-cycle costs and better resilience to grid outages. If deployed using smart-grid technology, NZE communities can act as a grid node and aid in balancing electrical demand. However, identifying cost-effective pathways to NZE requires detailed energy and economic models. Information required to build such models is not typically available at the early master-planning stages, where the largest energy and economic saving opportunities exist. Methodologies that expedite and streamline energy and economic modeling could facilitate early decision making. This paper describes a reproducible methodology that aids modelers in identifying energy and economic savings opportunities in the early community design stages. As additional information becomes available, models can quickly be recreated and evaluated. The proposed methodology is applied to the first-phase design of a NZE community under development in Southwestern Ontario.
- Date Created:
- 2015-01-01
-
- Resource Type:
- Conference Proceeding
- Creator:
- Oommen, B. John and Astudillo, César A.
- Abstract:
- We present a method that employs a tree-based Neural Network (NN) for performing classification. The novel mechanism, apart from incorporating the information provided by unlabeled and labeled instances, re-arranges the nodes of the tree as per the laws of Adaptive Data Structures (ADSs). Particularly, we investigate the Pattern Recognition (PR) capabilities of the Tree-Based Topology-Oriented SOM (TTOSOM) when Conditional Rotations (CONROT) [8] are incorporated into the learning scheme. The learning methodology inherits all the properties of the TTOSOM-based classifier designed in [4]. However, we now augment it with the property that frequently accessed nodes are moved closer to the root of the tree. Our experimental results show that on average, the classification capabilities of our proposed strategy are reasonably comparable to those obtained by some of the state-of-the-art classification schemes that only use labeled instances during the training phase. The experiments also show that improved levels of accuracy can be obtained by imposing trees with a larger number of nodes.
- Date Created:
- 2015-01-01
-
- Resource Type:
- Conference Proceeding
- Creator:
- Yazidi, Anis, Oommen, B. John, and Hammer, Hugo Lewi
- Abstract:
- The problem of clustering, or unsupervised classification, has been solved by a myriad of techniques, all of which depend, either directly or implicitly, on the Bayesian principle of optimal classification. To be more specific, within a Bayesian paradigm, if one is to compare the testing sample with only a single point in the feature space from each class, the optimal Bayesian strategy would be to achieve this based on the distance from the corresponding means or central points in the respective distributions. When this principle is applied in clustering, one would assign an unassigned sample into the cluster whose mean is the closest, and this can be done in either a bottom-up or a top-down manner. This paper pioneers a clustering achieved in an “Anti-Bayesian” manner, and is based on the breakthrough classification paradigm pioneered by Oommen et al. The latter relies on a radically different approach for classifying data points based on the non-central quantiles of the distributions. Surprisingly and counter-intuitively, this turns out to work equally or close-to-equally well to an optimal supervised Bayesian scheme, which thus begs the natural extension to the unexplored arena of clustering. Our algorithm can be seen as the Anti-Bayesian counter-part of the wellknown k-means algorithm (The fundamental Anti-Bayesian paradigm need not just be used to the k-means principle. Rather, we hypothesize that it can be adapted to any of the scores of techniques that is indirectly based on the Bayesian paradigm.), where we assign points to clusters using quantiles rather than the clusters’ centroids. Extensive experimentation (This paper contains the prima facie results of experiments done on one and two-dimensional data. The extensions to multi-dimensional data are not included in the interest of space, and would use the corresponding multi-dimensional Anti-Na¨ıve-Bayes classification rules given in [1].) demonstrates that our Anti-Bayesian clustering converges fast and with precision results competitive to a k-means clustering.
- Date Created:
- 2015-01-01
-
- Resource Type:
- Conference Proceeding
- Creator:
- Polk, Spencer and Oommen, B. John
- Abstract:
- This paper pioneers the avenue of enhancing a well-known paradigm in game playing, namely the use of History-based heuristics, with a totally-unrelated area of computer science, the field of Adaptive Data Structures (ADSs). It is a well-known fact that highly-regarded game playing strategies, such as alpha-beta search, benefit strongly from proper move ordering, and from this perspective, the History heuristic is, probably, one of the most acclaimed techniques used to achieve AI-based game playing. Recently, the authors of this present paper have shown that techniques derived from the field of ADSs, which are concerned with query optimization in a data structure, can be applied to move ordering in multi-player games. This was accomplished by ranking opponent threat levels. The work presented in this paper seeks to extend the utility of ADS-based techniques to two-player and multi-player games, through the development of a new move ordering strategy that incorporates the historical advantages of the moves. The resultant technique, the History-ADS heuristic, has been found to produce substantial (i.e, even up to 70%) savings in a variety of two-player and multi-player games, at varying ply depths, and at both initial and midgame board states. As far as we know, results of this nature have not been reported in the literature before.
- Date Created:
- 2015-01-01
-
- Resource Type:
- Conference Proceeding
- Creator:
- Labiche, Yvan and Barros, Márcio
- Date Created:
- 2015-01-01
-
- Resource Type:
- Conference Proceeding
- Creator:
- Brubaker, Jed R., Handel, Mark, Yarosh, Svetlana, Bivens, Rena, Haimson, Oliver L., and Lingel, Jessa
- Abstract:
- Online systems often struggle to account for the complicated self-presentation and disclosure needs of those with complex identities or specialized anonymity. Using the lenses of gender, recovery, and performance, our proposed panel explores the tensions that emerge when the richness and complexity of individual personalities and subjectivities run up against design norms that imagine identity as simplistic or one-dimensional. These models of identity not only limit the ways individuals can express their own identities, but also establish norms for other users about what to expect, causing further issues when the inevitable dislocations do occur. We discuss the challenges in translating identity into these systems, and how this is further marred by technical requirements and normative logics that structure cultures and practices of databases, algorithms and computer programming.
- Date Created:
- 2015-01-01