Search

In the context of project management, the attention given to project portfolio management has increased in recent years. The use of mathematical programming for portfolio management is also on the rise, because it integrates the project interactions with the multiple objectives of portfolio management into a single model. Among the possible objectives, recent studies have paid special attention to the emerging objective of future preparedness, which has not yet been incorporated into the existing mathematical models. This paper presents a mixed-integer nonlinear optimization model for portfolio selection that considers four main performance measures for project management, namely, value maximization, strategic alignment, balance, and future preparedness. Given the importance of the last measure, the purpose of this paper is to provide a more complete model that provides the marginal contribution and the best combination of projects according to the needs of the company. The model is tested using real data from two companies, one in Brazil and one in Canada, and the results obtained are coherent with their respective practices. [ABSTRACT FROM AUTHOR]

The paper describes the recent development of a system for calibration of electrical instruments with harmonics measurement capability, such as revenue meters and power analyzers, under nonsinusoidal test conditions at National Research Council (NRC) Canada. The improvements implemented with respect to an earlier NRC calibration system are aimed at increasing the calibration accuracy in a wider frequency range. [ABSTRACT FROM AUTHOR]

Tide refers to a phenomenon that causes the change of water level in oceans. Tidal level forecasting plays an important role in many real-world applications especially those related to oceanic and coastal areas. For instance, accurate forecasting of tidal level can significantly increase the vessels’ safety as an excessive level of tidal makes serious problems in the movement of vessels. In this work, we propose a deep learning-based prediction interval framework in order to model the forecasting uncertainties of tidal current datasets. The proposed model develops optimum prediction intervals (PIs) focused on the deep learning-based CNN-LSTM model (CLSTM), and nonparametric approach termed as the lower upper bound estimation (LUBE) model. Moreover, we develop a novel deep neuroevolution algorithm based on a two-stage modification of the gaining-sharing knowledge optimization algorithm to optimize the architecture of the CLSTM automatically without the procedure of trial and error. This leads to a decline in the complexity raises in designing manually the deep learning architectures, as well as an enhancement in the performance of the PIs. We also utilize coverage width criterion to establish an excellent correlation appropriately between both the PI coverage probability and PI normalized average width. We indicate the searching efficiency and high accuracy of our proposed framework named as MGSK-CLSTM-LUBE by examining over the practical collected tidal current datasets from the Bay of Fundy, NS, Canada. [ABSTRACT FROM AUTHOR]

The demand for high-performance graphics systems used for artificial intelligence, cloud game, and virtual reality continues to grow; this trend requires graphics systems to achieve ever higher bandwidths. This article proposes a GDDR6 dynamic random access memory (DRAM) with a half-rate clocking architecture and optimized receiver and transmitter to improve high-speed operation. Furthermore, this article adopts a staggered PAD using the redistribution layer (RDL) to reduce the distance to four PADs; it enables the mitigation of bandwidth limitation of half-rate clocking, a lower phase mismatch, and a reduced propagation delay. The proposed half-rate clocking-based GDDR6 DRAM achieves 24 Gb/s/pin on a 1.35-V DRAM process. Also, the power-supply-induced-jitter (PSIJ) value is improved from 9.97 to 3.22 ps, compared to a GDDR6 design using a quarter-rate clocking. In addition, the phase mismatch of the proposed clock distribution network (CDN) is reduced compared to the conventional CDN, resulting in an improvement of the 3- $\sigma $ value of the phase skew from 4.16 to 2.25 ps. [ABSTRACT FROM AUTHOR]

This article is a case study from the Hebron Offshore Oil Platform located in Atlantic Canada focusing on fire-resistant medium-voltage enclosures that were supplied for the seawater lift pumps and fire water pumps. The purpose of this article is to show where fire-resistant enclosures may be utilized and to detail the testing involved in achieving fire resistance with the enclosures for the Hebron Platform. [ABSTRACT FROM AUTHOR]

This article will discuss the requirements for evaluating an equipment assembly for evidence of compliance to applicable standards, for the purpose of obtaining certification or a listing from a certification body. This would allow the equipment to be marked as suitable for use in explosive atmospheres. Historically there was little guidance for evaluating ex equipment assemblies and as such evaluations could be complicated and costly. In 2017, a technical specification, IEC TS 60079-46, was published for the evaluation of equipment assemblies for explosive environments. In 2018, Canada adopted the IEC TS 60079-46 with Canadian deviations, as CAN/CSA C22.2 No. 60079-46. The USA is in the process of adopting a version of IEC TS 60079-46 with national deviations. This is a step forward in being able to approve assemblies so that any additional investigation in the field is minimized or eliminated. [ABSTRACT FROM AUTHOR]

Snow water equivalent (SWE) is an important surface parameter for understanding a number of Earth system processes. Synthetic aperture radar (SAR) has considerable potential for measuring SWE of dry-snow because SAR can penetrate through the snow to the ground surface and is both amplitude- and phase-sensitive to refraction from the snow. Previous work on refraction-based SWE measurement by SAR has utilized the repeat-pass InSAR phase signal to estimate changes in SWE that occur between SAR acquisitions. These are subject to temporal decorrelation effects and consider only the refraction that occurs along the SAR beam center rather than the entire synthetic aperture. This study examines the refractive effect of dry-snow along the synthetic aperture and its impact on SAR image formation including defocusing and phase bias of the system impulse response. Snow phase compensation during time domain processing to recover the snow-free impulse response function (IRF) is described and demonstrated. The feasibility of using the mapdrift and image sharpness autofocus methods to estimate SWE is examined, and the effect of key system parameters on the estimation performance is derived. Experimental validation of the method was conducted by acquiring L-band data with the Simon Fraser University (SFU) Airborne SAR System over a pair of corner reflectors installed on the Kluane icefield in northwestern Canada. Results from both simulations and the icefield experiment are presented and compared including an analysis of errors affecting the estimation. [ABSTRACT FROM AUTHOR]

Nuclear reactor inspections are critical to ensure the safety and reliability of a nuclear facility’s operation. In Canada, ultrasonic testing (UT) is used to inspect the health of pressure tubes that are part of Canada’s Deuterium Uranium (CANDU) reactor’s fuel channels. Currently, analysis of UT scans is performed by manual visualization and measurement to locate, characterize, and disposition flaws. Therefore, there is motivation to develop an automated method that is fast and accurate. In this article, a proof of concept (PoC) that automates the detection of flaws in nuclear fuel channel UT scans using a convolutional neural network (CNN) is presented. The CNN model was trained after constructing a dataset using historical UT scans and the corresponding inspection results. The requirement for this prototype was to identify the location of at least a portion of each flaw in UT scans while minimizing false positives (FPs). The proposed CNN model achieves this target by automatically identifying at least a portion of each flaw where further manual analysis is performed to identify the width, the length, and the type of the flaw. [ABSTRACT FROM AUTHOR]

It is commonly believed that observer-based compensation is an effective way for disturbance rejection. A less talked about fact is that such disturbance rejection control technique may also degrade control performance. In this article, we present a typical cross-coupling system to reveal this problem and, more importantly, propose a new design principle of conditional disturbance negation (CDN) to eliminate its potential drawbacks of disturbance observer-based compensation. Qualitative analysis is first given for a general form of such cross-coupling systems, indicating the necessity of CDN. The analysis and control design principle of CDN is then exemplified through two applications. A numerical linear application produces abundant quantitative results through the powerful transfer function and frequency domain tools. A more complex nonlinear flexible air-breathing hypersonic vehicle application shows how conventional compensation deteriorates the couplings between rigid and flexible modes, and validates the effectiveness of CDN through comprehensive model analysis and simulation results. The proposed CDN design principle also arouses awareness of the importance of: 1) understanding the characteristics of the plant to be controlled and 2) recognizing the critical role the information plays in engineering practice. [ABSTRACT FROM AUTHOR]

Welcome to a Special Issue of the IEEE Canadian Journal of Electrical and Computer Engineering (ICJECE), which presents some of the top articles (in extended form) from the Electric Power and Energy Conference (EPEC 2020) held on November 9–12, 2020. The theme was “Power Systems in Transition.” This conference was originally planned to take place at the University of Alberta, Edmonton, AB, Canada, but the COVID-19 global pandemic required pivoting to a virtual (online) platform. This marks the first time this conference was held remotely. EPEC 2020 was hosted by the Northern Canada Section, with five cosponsors (IEEE Canada, IEEE Southern Alberta Section, IEEE North Saskatchewan Section, IEEE Winnipeg Section, and the IEEE IAS/PES Northern Canada Chapter) and two technical cosponsors (IEEE PES and IEEE PELS). [ABSTRACT FROM AUTHOR]

Uncertainty modeling of renewable energy sources, load demand, electricity price, etc. create a high volume of data in smart grids. Accordingly, in this article, a precise forecasting method based on a deep learning concept with microclustering (MC) task is presented. The MC method is structured based on hybrid unsupervised and supervised clustering tasks by K-means and Gaussian support vector machine, respectively. In the proposed method, the input data sequence is clustered by the MC task, and then the forecasting process is employed. By applying the MC, input data in each hour are categorized into different groups, and a distinctive forecasting unit is allocated to each one. In this way, more clusters and forecasting networks are earmarked for the hours with higher fluctuation rates. The bi-directional long short-term memory (B-LSTM), which is one of the newest recurrent artificial neural networks, is proposed as the forecasting unit. The B-LSTM has bidirectional memory—feedforward and feedback loops—that helps us to investigate both previous and future hidden layers data. The optimal number of clusters in each hour is determined based on the Davies–Bouldin index. To evaluate the performance of the proposed method, in this study, three forecasting tasks including the wind speed, load demand, and electricity price are studied in different periods using the Ontario province, Canada, data set. The results are compared with other benchmarking methods to verify the robustness and effectiveness of the proposed method. In fact, the proposed method, which is equipped with the MC technique and B-LSTM networks, significantly promotes the forecasting results, especially in spike points. [ABSTRACT FROM AUTHOR]

The extraction of oceanic wave spectrum information from radar data has been a challenging problem that has been the subject of a vast amount of research over the past several decades. This research has resulted in a multitude of approaches to extract ocean wave spectra from Doppler spectrum returns. One common feature of many of these methods is the reduction of the wave spectrum extraction problem from a nonlinear problem to a linear one. In this article, a new approach is introduced, which does not linearize the Fredholm integral equation relating the ocean wave spectrum to the radar Doppler spectrum, but instead maintains its nonlinear nature. Also, unlike previous nonlinear optimization solutions, the proposed method is automatic in the sense that no regularization parameters have to be manually set purely dependent on the radar data from which the ocean wave parameters are being extracted, thereby reducing the need for human intervention in the wave spectrum extraction process. In addition to describing this new method for wave spectrum extraction, this article presents results from a case study on field data from Argentia, NL, Canada, comparing the oceanographic parameters obtained with the proposed method to those recorded by in situ buoy instrumentation. The significant wave height calculated from ocean wave spectra extracted via the method are found to match with those from the buoy, whereas the values of other oceanographic parameters, such as wave period and direction extracted using the proposed method, are less accurate potentially due to the low quality of data available to test the method. [ABSTRACT FROM AUTHOR]