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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]

This paper presents a data-driven method for estimating annual continuous dynamic rating of power transformers to serve the long-term planning purpose. Historically, research works on dynamic rating have been focused on real-time/near-future system operations. There has been a lack of research for long-term planning oriented applications. Currently, most utility companies still rely on static rating numbers when planning power transformers for the next few years. In response, this paper proposes a novel and comprehensive method to analyze the past 5-year temperature, loading and load composition data of existing power transformers in a planning region. Based on such data and the forecasted area load composition, a future power transformer's load shape profile can be constructed by using Gaussian Mixture Model. Then according to IEEE std. C57.91-2011, a power transformer thermal aging model can be established to incorporate future loading and temperature profiles. As a result, annual continuous dynamic rating profiles under different temperature scenarios can be determined. The profiles can reflect the long-term thermal overloading risk in a much more realistic and granular way, which can significantly improve the accuracy of power transformer planning. A real utility application example in Canada has been presented to validate and demonstrate the practicality and usefulness of this method. [ABSTRACT FROM AUTHOR]

Monitoring the sea ice cover is important for both climate studies and ice operations, such as shipping. It is challenging to validate even basic essential variables, such as the sea ice extent, due to a lack of appropriate validation data. Instead of focusing on validation, this paper looks at the use of categorical triple collocation (CTC) for the task of quantitatively comparing three colocated data sets. CTC has been developed and used in earlier studies to rank binary data sets. In this paper, we extend earlier studies and bring in recent results from the binary classification community to estimate the class imbalance (the relative proportion of each class, ice or water). We then use this class imbalance to obtain quantitative estimates of the proportion correct of ice (sensitivity) and the proportion correct of water (specificity). The methodology is first tested using toy data, after which three data sets from the Gulf of Saint Lawrence, on the east coast of Canada, are used. These data sets are from an ice–ocean model, a passive microwave sea ice concentration retrieval, and a sea ice concentration retrieval from synthetic aperture radar (SAR). By looking at both the sensitivity and the specificity, it is found that the passive microwave data have difficulty in recognizing ice during freeze-up, but they perform well at obtaining the correct water observations. This distinction cannot be made by ranking the data sets. The CTC method is compared with, and found to be complementary to, a validation using ice/water states from the interactive multisensor snow and ice mapping system. [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]

In power systems, an asset class is a group of power equipment that has the same function and shares similar electrical and/or mechanical characteristics. Predicting failures for different asset classes is critical for electric utilities toward developing cost-effective asset management strategies. Previously, physical age based Weibull distribution has been widely used for failure prediction. However, this mathematical model cannot incorporate asset condition data. As a result, the prediction cannot be very specific and accurate for individual assets. To solve this important problem, this paper proposes a novel and comprehensive data-driven approach based on asset condition data: K-means clustering as an unsupervised learning method is used to analyze the inner structure of historical asset condition data and produce the asset conditional ages; logistic regression as a supervised learning method takes in both asset physical ages and conditional ages to classify and predict asset operation statuses. Furthermore, an index called average aging rate is defined to quantify, track, and estimate the relationship between asset physical age and conditional age. This approach was applied to a medium-voltage cable class in an urban distribution system in West Canada. Case studies and comparison with standard Weibull distribution are provided. The proposed approach demonstrates higher accuracy measured by F1-Score than Weibull distribution method for asset class failure prediction. [ABSTRACT FROM AUTHOR]

The Canadian Department of National Defence (DND) is developing an experimental over-the-horizon radar (OTHR) with the potential for surveillance of Canada. Because of dynamically changing ionospheric conditions in the Earth’s high-latitude and polar regions, the operating OTHR transmission frequency and elevation angle need to be adjusted regularly to maintain constant illumination of downrange targets. In this paper, the feasible operating frequency and elevation angle radar parameters are determined for short- and long-range OTHR operation using 3-D ionosphere ray-tracing simulations. Together, the collection of all feasible radar configurations forms a characteristic profile which shifts and deforms as factors such as the time of day, season, and solar activity are varied. The range of operating frequencies and elevation angles obtained from this paper will aid developing the transmitter and receiver antenna layouts for experimental OTHR configurations in the poorly understood high-latitude and polar regions. These methods will also help to form the basis of the frequency monitoring systems (FMS) that will control the configuration of these polar OTHR systems in real time. [ABSTRACT FROM AUTHOR]

Internet video traffic has been rapidly increasing and is further expected to increase with the emerging 5G applications, such as higher definition videos, the IoT, and augmented/virtual reality applications. As end users consume video in massive amounts and in an increasing number of ways, the content distribution network (CDN) should be efficiently managed to improve the system efficiency. The streaming service can include multiple caching tiers, at the distributed servers and the edge routers, and efficient content management at these locations affects the quality of experience (QoE) of the end users. In this paper, we propose a model for video streaming systems, typically composed of a centralized origin server, several CDN sites, and edge-caches located closer to the end user. We comprehensively consider different systems design factors, including the limited caching space at the CDN sites, allocation of CDN for a video request, choice of different ports (or paths) from the CDN and the central storage, bandwidth allocation, the edge-cache capacity, and the caching policy. We focus on minimizing a performance metric, stall duration tail probability (SDTP), and present a novel and efficient algorithm accounting for the multiple design flexibilities. The theoretical bounds with respect to the SDTP metric are also analyzed and presented. The implementation of a virtualized cloud system managed by Openstack demonstrates that the proposed algorithms can significantly improve the SDTP metric compared with the baseline strategies. [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]

Emerging new applications demand the current Internet to provide new functionalities. Although many future Internet architectures and protocols have been proposed to fulfill such needs, ISPs have been reluctant to deploy many of these architectures. We believe technical issues are not the main reasons as many of these new proposals are technically sound. In this paper, we take an economic perspective and seek to answer: Why do most new Internet architectures fail to be deployed? How can the deployability of a new architecture be enhanced? We develop a game-theoretic model to characterize the outcome of an architecture’s deployment through the equilibrium of ISPs’ decisions. This model enables us to: (1) analyze several key factors of the deployability of a new architecture such as the number of critical ISPs and the change of routing path; (2) explain the deploying outcomes of some previously proposed architectures/protocols such as IPv6, DiffServ, CDN, etc., and shed light on the “Internet flattening phenomenon”; (3) predict the deployability of a new architecture such as NDN, and compare its deployability with competing architectures. Our study suggests that the difficulty to deploy a new Internet architecture comes from the “coordination” of distributed ISPs. Finally, we design a mechanism to enhance the deployability of new architectures. [ABSTRACT FROM AUTHOR]

In compensated distributed networks (CDN), minimizing the asymmetry in the shunt parameters of the power lines is a key factor for avoiding excessive neutral voltages which is necessary not only for safety but also for improving the sensitivity of detecting ground faults, particularly when bush-fire mitigation is a major concern. This paper offers a comprehensive analysis and modeling of the asymmetry of the power lines in CDN. Unlike the existing analyses which assume the asymmetry can be lumped in one phase, the proposed analysis is based on more practical assumption by considering the asymmetry in all of the phases of the power lines. The proposed analysis is used to develop an equivalent circuit for asymmetrical CDN which then used to develop a new method for calculating the per-phase asymmetries in the shunt parameters. The proposed method has been validated based on simulations of real networks. [ABSTRACT FROM AUTHOR]

In 1999, BC Hydro installed optical fibers in the fluid channel of 525 kV self-contained fluid-filled submarine cables originally installed in the 1980s that connect substations in the Vancouver area of British Columbia, Canada, for the purposes of monitoring temperatures. In combination with this work, a real-time monitoring was used to collect load data and measured temperatures in an effort to optimize the ratings of the cables. The dynamic thermal circuit rating system developed by the Electric Power Research Institute was applied to the 525 kV cables as part of a project to modify the system for support of the unique characteristics of the cable system installation. Specifically, the submarine cables utilize a parallel pipe water cooling system for the shore zones. This paper describes the software modifications that were implemented to enhance the dynamic rating system to model the unique characteristics of the 525 kV cable system and discusses how recorded data collected over several seasons were used to verify and refine the cable rating model. The resulting evaluation showed good agreement of the dynamic rating system calculations with measured results and helped the utility better understand the ratings on these circuits and permitted the utility to perform simulations to evaluate various loading conditions. [ABSTRACT FROM AUTHOR]

Today’s anywhere and anytime broadband connection and audio/video capture have boosted the deployment of crowdsourced livecast services (or crowdcast). Bridging a massive amount of geo-distributed broadcasters and their fellow viewers, such representatives as Twitch.tv, Youtube Gaming, and Inke.tv, have greatly changed the generation and distribution landscape of streaming content. They also enable rich online interactions among the crowd, and strive to offer personalized Quality-of-Experience (QoE) for individual viewers. Given the ultra-large scale and the dynamics of the crowd, personalizing QoE however is much more challenging than in early generation streaming services. The rich interactions among the broadcasters, viewers, and the network system, on the other hand, also offer invaluable data that could be utilized towards informed management. This paper presents DeepCast, an edge-assisted crowdcast framework that explores the sheer amount of viewing data towards intelligent decisions for personalized QoE demands. DeepCast seamlessly integrates cloud, CDN, and edge servers for crowdcast content distribution, and advocates a data-driven design that extracts the hidden information from the complex interactions among the system components. Through deep reinforcement learning (DRL), it automatically identifies the most suitable strategies for viewer assignment and transcoding at edges. We collect multiple real-world datasets and evaluate the performance of DeepCast with trace-driven experiments. The results demonstrate its flexibility and effectiveness towards better personalized QoE and lower cost for crowdcast systems. [ABSTRACT FROM AUTHOR]

The paper proposes a novel framework based on the contract theory and Lyapunov optimization for content sharing in a wireless content delivery network (CDN) with edge caching and device-to-device (D2D) communications. The network is partitioned into a set of clusters. In a cluster, users can share contents via D2D links in coordination with the cluster head. Upon receiving the content request from any user in its cluster, the cluster head either delivers the content itself or forwards the request to another node, i.e., a base station (BS) or another user in the cluster. The content access at the BS and in each cluster is modeled as a queuing system, where arrivals represent the content requests directed to respective nodes. The objective is to assign content delivery nodes to stabilize all queues while minimizing the time-averaged network cost given incomplete information about content sharing costs of the users and unknown distribution of the network state defined by users’ locations and their cached/requested content. The proposed framework allows the users to truthfully reveal their content sharing expenditures, minimize the time-averaged network cost and stabilize the queuing system representing the CDN. Based on this framework, a distributed content access and delivery algorithm where the node assignments are made by every cluster head independently is developed. It is shown that the algorithm converges to the optimal policy with the trade-off in total queue backlog and achieves a superior performance compared with some other D2D content sharing policies. [ABSTRACT FROM AUTHOR]

With the rapid proliferation of cloud computing, the energy cost of datacenters and the impact of reactive power on the grid have become major concerns. This paper presents two energy management control schemes for distributed uninterruptible power supply (UPS) architectures utilizing lithium-ion ultracapacitors (LIC) for improved system efficiency and reactive power mitigation. One scheme is proposed for low-power servers with an internal dc bus, while another is proposed for medium-to-high-power server racks with an in-rack dc bus. The LIC and the dc bus interface with a bidirectional dc–dc converter, which allows the power supply unit to run in the optimal operating region for the improved system efficiency and power factor. The server-level UPS architecture is experimentally tested using a 200-kHz, bidirectional, multiphase dc–dc converter with hysteretic current-mode control, while achieving 33% reactive power mitigation. Based on the SciNet datacenter in Toronto, ON, Canada, a rack-level distributed UPS architecture is built in MATLAB, and system-level simulations indicate a 37% reduction in the reactive power. [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]

Traditional connection of a generating station to the transmission grid typically requires either a new transmission line to an existing substation or building a new substation to segment an existing transmission line. Both of these options are capital intensive which often hurts economic viability especially small- or medium-size nonconventional generating stations. The direct connection to an existing transmission line, without the switching station, is the most economical solution to interconnect the generating station but it desensitizes the line protection and poses a risk to the ability of the protection to detect faults, particularly ground faults involving fault resistances. Multifunction relays, combined with communication-assisted schemes, now offer innovative solutions to overcome protection desensitization concerns. This paper describes these solutions which are successfully implemented by a large Canadian electric utility over the last two decades to tap connect numerous generators to the transmission grid. [ABSTRACT FROM PUBLISHER]

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]

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]

In the transmission line grounding system design, spatial and temporal/seasonal variations of tower footing impedance should be considered. However, in real life, only spatial variations are taken into account at the design stage, seasonal variations have not been included. In this article, seasonal variations of tower footing impedance of several types of transmission line grounding systems at Manitoba Hydro, Canada, are investigated through field measurements for a whole year. The industrial practice uses simulated tower footing impedances by the software current distribution, electromagnetic interference, grounding and soil structure analysis (CDEGS) in the design of transmission line grounding systems, however, the accuracy of simulated tower footing impedances as a critical design parameter has never been evaluated. In this article, this industrial practice is evaluated by comparing simulated and measured tower footing impedances for various transmission line grounding systems. The importance to use proper tower footing impedance for lightning conditions is also demonstrated. [ABSTRACT FROM AUTHOR]

The time resolution of active pixel sensors whose timestamp mechanism is based on time-to-digital converters is critically linked to the accuracy in the distribution of the master clock signal that latches the timestamp values across the detector. The clock distribution network (CDN) that delivers the master clock signal must compensate process–voltage–temperature variations to reduce static time errors (skew) and minimize the power supply bounce to prevent dynamic time errors (jitter). To achieve sub-100-ps time resolution within pixel detectors and thus enable a step forward in multiple imaging applications, the network latencies must be adjusted in steps well below that value. Power consumption must be kept as low as possible. In this work, a self-regulated CDN that fulfills these requirements is presented for the FastICpix single-photon detector aiming at a 65-nm process. A 40-MHz master clock is distributed to 64 × 64 pixels over an area of 2.4 × 2.4 cm2 using digital delay-locked loops, achieving clock leaf skew below 20 ps with a power consumption of 26 mW. Guidelines are provided to adapt the system to arbitrary chip area and pixel pitch values, yielding a versatile design with very fine time resolution. [ABSTRACT FROM AUTHOR]

We present an architecture for the provision of video Content Delivery Network (CDN) functionality as a service over a multi-domain cloud. We introduce the concept of a CDN slice, that is, a CDN service instance which is created upon a content provider's request, is autonomously managed, and spans multiple, potentially heterogeneous, edge cloud infrastructures. Our design is tailored to a 5G mobile network context, building on its inherent programmability, management flexibility, and the availability of cloud resources at the mobile edge level, thus close to end users. We exploit Network Functions Virtualization (NFV) and Multi-access Edge Computing (MEC) technologies, proposing a system which is aligned with the recent NFV and MEC standards. To deliver a Quality-of-Experience (QoE) optimized video service, we derive empirical models of video QoE as a function of service workload, which, coupled with multi-level service monitoring, drive our slice resource allocation and elastic management mechanisms. These management schemes feature autonomic compute resource scaling, and on-the-fly transcoding to adapt video bit-rate to the current network conditions. Their effectiveness is demonstrated via testbed experiments. [ABSTRACT FROM AUTHOR]

In this article, an unsupervised clustering-based method for identifying rain-contaminated and low-backscatter regions in X-band marine radar images is presented. Rain blurs the wave signatures of radar images, and low-backscatter images caused by calibration errors or too-low wind speed contain little or no wave signatures. In both cases, ocean surface parameter measurement using X-band marine radar will be negatively affected. Four types of features can be extracted based on the distinct difference in texture and pixel intensity distribution between rain-free, rain-contaminated, and low-backscatter regions. Features extracted from each pixel are combined into a feature vector and mapped onto a $10\times 10$ -neuron self-organizing map (SOM). Then, the hierarchical agglomerative clustering algorithm is introduced, which clustered those neurons into three types (i.e., rain-free, rain-contaminated, and low-backscatter). The method is validated using the shipborne marine radar data collected on the East Coast of Canada. The good agreement between the pixel-based clustering results and manually segmented reference images indicates that both rain-contaminated and low-backscatter regions can be identified effectively using the proposed method. [ABSTRACT FROM AUTHOR]

In recent years there has been a rapid increase of short video traffic in CDN. While the video contributors change from large video studios to distributed ordinary end users, edge computing naturally matches the cache requirements from short video network. But distributed edge caching exposes some unique characteristics: non-stationary user access pattern and temporal and spatial video popularity pattern, which severely challenge the edge caching performance. While the QoE in traditional CDN has been much improved, prior solutions become invalid in solving the above challenges. In this article, we present AutoSight, a distributed edge caching system for short video network, which significantly boosts cache performance. AutoSight consists of two main components, solving the above two challenges respectively: the CoStore predictor, which solves the non-stationary and unpredictability of local access pattern, by analyzing the complex video correlations; and a caching engine Viewfinder, which solves the temporal and spatial video popularity problem by automatically adjusting future horizon according to video life span. All these inspirations and experiments are based on the real traces of more than 28 million videos with 100 million accesses from 488 servers located in 33 cities. Experiment results show that AutoSight brings significant boosts on distributed edge caching in short video network. [ABSTRACT FROM AUTHOR]

Canada-wide climate change plans have been put in place in the form of legislation and emission reduction programs. As part of these measures, reduced amounts of natural gas can be vented, incinerated, or flared. These programs have a direct impact on the Canadian oil and gas industry. Other measures mandate the decommissioning of high-emission thermal power plants, such as coal-fired generation. At the same time, governments are encouraging the wide-scale adoption of distributed energy resources (DERs). Because of these changes, natural gas fired DERs have been proliferating in oilfields to consume the byproduct natural gas and most major oil and gas companies are diversifying their portfolio to include generation assets. It is a more sustainable and environmentally friendly scenario as compared to the previous status quo. From an electric utility standpoint, this represents a change in paradigm, because distribution systems are traditionally planned to be radial and only experience power flowing from the substation to the load centers. This change requires utilities to adapt to new concerns and requirements, which span from operation to health and safety hazards. Among these, anti-islanding is likely the most important concern to allow the safe and reliable operation of a distribution system. The standard method to avoid unintended islanded operation is the direct transfer trip. Under exceptional circumstances, the utility may waive this requirement if an alternative acceptable method is in use. This article addresses existing anti-islanding schemes and covers in detail an alternative anti-islanding method native to many inverters. This article contains the philosophy of such method and measurement results captured during commissioning of a DER containing the scheme. Furthermore, this article highlights the evolution of inverter-based generator technologies and how the revision of applicable standards (IEEE 1547) can improve the reliable operation of such DERs. [ABSTRACT FROM AUTHOR]

Using the observed or desired stability results of each decision maker (DM) and the equilibrium of a conflict, an algorithm is proposed that determines whether the Nash, general metarationality, symmetric metarationality or sequential stability definition best explains the observations or how to achieve the desired equilibrium. The matrix representation of the graph model for conflict resolution, normally used to determine stabilities that reflect different models of human interaction in conflict, is utilized here to keep track of available one-step moves for each DM, as well as the DMs’ relative preferences among states. A key theorem involving matrix representation of graph models determines which stability types are consistent with the observations or achievement of the desired equilibrium, taking into account specified preferences of the DMs, for the case of two or more DMs. To illustrate the use of the algorithm in practice, behavioral analysis is applied to a softwood lumber dispute between Canada and the USA. [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]

Agent-based models (ABMs) are computer simulation models that define interactions among agents and simulate emergent behaviors that arise from the ensemble of local decisions. ABMs have been increasingly used to examine trends in infectious disease epidemiology. However, the main limitation of ABMs is the high computational cost for a large-scale simulation. To improve the computational efficiency for large-scale ABM simulations, we built a parallelizable sliding region algorithm (SRA) for ABM and compared it to a nonparallelizable ABM. We developed a complex agent network and performed two simulations to model hepatitis C epidemics based on the real demographic data from Saskatchewan, Canada. The first simulation used the SRA that processed on each postal code subregion subsequently. The second simulation processed the entire population simultaneously. It was concluded that the parallelizable SRA showed computational time saving with comparable results in a province-wide simulation. Using the same method, SRA can be generalized for performing a country-wide simulation. Thus, this parallel algorithm enables the possibility of using ABM for large-scale simulation with limited computational resources. [ABSTRACT FROM PUBLISHER]

Aerial drones are increasingly being repurposed for domestic use and legislated for public and commercial purposes around the world. Japan, Australia, Canada, and the U.K. are among some of the countries with civil drone regulations in place, and there are many others soon to follow. In fact, Canada had legislated civilian use of drones as early as 1996, when the term ?nonpiloted aircraft? was introduced into the official rules governing Canada?s airspace, the Canadian Aviation Regulations. [ABSTRACT FROM PUBLISHER]

The 34th Electrical Insulation Conference will be held in Montreal, QC, Canada, June 19?22, 2016 (see http://sites.ieee.org/ eic). The conference is returning to Montreal for the second time for EIC and also the fifth time including the ISEI conferences. The conference venue is the Hotel Bonaventure Montreal, 900 De La Gauchetiere W., Montreal. Further details will appear on the conference website. [ABSTRACT FROM AUTHOR]