It has been observed in the literature that as the cardinality of the prescribed discrete input-output data set increases, the corresponding four-bar linkages that minimise the Euclidean norm of the design and structural errors tend to converge to the same linkage. The important implication is that minimising the Euclidean norm, or any p-norm, of the structural error, which leads to a nonlinear least-squares problem requiring iterative solutions, can be accomplished implicitly by minimising that of the design error, which leads to a linear least-squares problem that can be solved directly. Apropos, the goal of this paper is to take the first step towards proving that as the cardinality of the data set tends towards infinity the observation is indeed true. In this paper we will integrate the synthesis equations in the range between minimum and maximum input values, thereby reposing the discrete approximate synthesis problem as a continuous one. Moreover, we will prove that a lower bound of the Euclidean norm, and indeed of any p-norm, of the design error for planar RRRR function-generating linkages exists and is attained with continuous approximate synthesis.
Building Performance Simulation (BPS) is a powerful tool to estimate and reduce building energy consumption at the design stage. However, the true potential of BPS remains unrealized if trial and error simulation methods are practiced to identify combinations of parameters to reduce energy use of design alternatives. Optimization algorithms coupled with BPS is a process-orientated tool which identifies optimal building configurations using conflicting performance indicators. However, the application of optimization approaches to building design is not common practice due to time and computation requirements. This paper proposes a hybrid evolutionary algorithm which uses information gained during previous simulations to expedite and improve algorithm convergence using targeted deterministic searches. This technique is applied to a net-zero energy home case study to optimize trade-offs in passive solar gains and active solar generation using a cost constraint.
We describe a novel Distributed Storage protocol in Disruption (Delay) Tolerant Networks (DTN). Since DTNs can not guarantee the connectivity of the network all the time, distributed data storage and look up has to be performed in a store-and-forward way. In this work, we define local distributed location regions which are called cells to facilitate the data storage and look up process. Nodes in a cell have high probability of moving within their cells. Our protocol resorts to storing data items in cells which have hierarchical structure to reduce routing information storage at nodes. Multiple copies of a data item may be stored at nodes to counter the adverse impact of the nature of DTNs. The cells are relatively stable regions and as a result, data exchange overheads among nodes are reduced. Through experimentation, we show that the proposed distributed storage protocol achieves higher successful data storage ratios with lower delays and limited data item exchange requirements than other protocols in the literature.
we present a method of segmenting video to detect cuts with accuracy equal to or better than both histogram and other feature based methods. As well, the method is faster than other feature based methods. By utilizing feature tracking on corners, rather than lines, we are able to reliably detect features such as cuts, fades and salient frames. Experimental evidence shows that the method is able to withstand high motion situations better than existing methods. Initial implementations using full sized video frames are able to achieve processing rates of 10-30 frames per second depending on the level of motion and number of features being tracked; this includes the time to generate the MPEG decompressed frames.
The design and analysis of community-scale energy systems and incentives is a non-trivial task. The challenge of such undertakings is the well-documented uncertainty of building occupant behaviours. This is especially true in the residential sector, where occupants are given more freedom of activity compared to work environments. Further complicating matters is the dearth of available measured data. Building performance simulation tools are one approach to community energy analysis, however such tools often lack realistic models for occupant-driven demands, such as appliance and lighting (AL) loads. For community-scale analysis, such AL models must also be able to capture the temporal and inter-dwelling variation to achieve realistic estimates of aggregate electrical demand. This work adapts the existing Centre for Renewable Energy Systems Technology (CREST) residential energy model to simulate Canadian residential AL demands. The focus of the analysis is to determine if the daily, seasonal, and inter-dwelling variation of AL demands estimated by the CREST model is realistic. An in-sample validation is conducted on the model using 22 high-resolution measured AL demand profiles from dwellings located in Ottawa, Canada. The adapted CREST model is shown to broadly capture the variation of AL demand variations observed in the measured data, however seasonal variation in daily AL demand behaviour was found to be under-estimated by the model. The average and variance of daily load factors was found to be similar between measured and modelled. The model was found to under-predict the daily coincidence factors of aggregated demands, although the variance of coincident factors was shown to be similar between measured and modelled. A stochastic baseload input developed for this work was found to improve estimates of the magnitude and variation of both baseload and peak demands.
This article describes the progress made toward implementing Resource Description and Access (RDA) in libraries across Canada, as of Fall 2013. Differences in the training experiences in the English-speaking cataloging communities and French-speaking cataloging communities are discussed. Preliminary results of a survey of implementation in English-Canadian libraries are included as well as a summary of the support provided for French-Canadian libraries. Data analysis includes an examination of the rate of adoption in Canada by region and by sector. Challenges in RDA training delivery in a Canadian context are identified, as well as opportunities for improvement and expansion of RDA training in the future.