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In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

(1990). ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY. Electric Machines & Power Systems: Vol. 18, No. 2, pp. 209-210.

Energy function analysis for power system stability

This book provides an overview of the theory and practice of continuous and discrete wavelet transforms. Divided into seven chapters, the first three chapters of the book are introductory, describing the various forms of the wavelet transform and their computation, while the remaining chapters are devoted to applications in fluids, engineering, medicine and miscellaneous areas. Each chapter is well introduced, with suitable examples to demonstrate key concepts. Illustrations are included where appropriate, thus adding a visual dimension to the text. A noteworthy feature is the inclusion, at the end of each chapter, of a list of further resources from the academic literature which the interested reader can consult. The first chapter is purely an introduction to the text. The treatment of wavelet transforms begins in the second chapter, with the definition of what a wavelet is. The chapter continues by defining the continuous wavelet transform and its inverse and a description of how it may be used to interrogate signals. The continuous wavelet transform is then compared to the short-time Fourier transform. Energy and power spectra with respect to scale are also discussed and linked to their frequency counterparts. Towards the end of the chapter, the two-dimensional continuous wavelet transform is introduced. Examples of how the continuous wavelet transform is computed using the Mexican hat and Morlet wavelets are provided throughout. The third chapter introduces the discrete wavelet transform, with its distinction from the discretized continuous wavelet transform having been made clear at the end of the second chapter. In the first half of the chapter, the logarithmic discretization of the wavelet function is described, leading to a discussion of dyadic grid scaling, frames, orthogonal and orthonormal bases, scaling functions and multiresolution representation. The fast wavelet transform is introduced and its computation is illustrated with an example using the Haar wavelet. The second half of the chapter groups together miscellaneous points about the discrete wavelet transform, including coefficient manipulation for signal denoising and smoothing, a description of Daubechies' wavelets, the properties of translation invariance and biorthogonality, the two-dimensional discrete wavelet transforms and wavelet packets. The fourth chapter is dedicated to wavelet transform methods in the author's own specialty, fluid mechanics. Beginning with a definition of wavelet-based statistical measures for turbulence, the text proceeds to describe wavelet thresholding in the analysis of fluid flows. The remainder of the chapter describes wavelet analysis of engineering flows, in particular jets, wakes, turbulence and coherent structures, and geophysical flows, including atmospheric and oceanic processes. The fifth chapter describes the application of wavelet methods in various branches of engineering, including machining, materials, dynamics and information engineering. Unlike previous chapters, this (and subsequent) chapters are styled more as literature reviews that describe the findings of other authors. The areas addressed in this chapter include: the monitoring of machining processes, the monitoring of rotating machinery, dynamical systems, chaotic systems, non-destructive testing, surface characterization and data compression. The sixth chapter continues in this vein with the attention now turned to wavelets in the analysis of medical signals. Most of the chapter is devoted to the analysis of one-dimensional signals (electrocardiogram, neural waveforms, acoustic signals etc.), although there is a small section on the analysis of two-dimensional medical images. The seventh and final chapter of the book focuses on the application of wavelets in three seemingly unrelated application areas: fractals, finance and geophysics. The treatment on wavelet methods in fractals focuses on stochastic fractals with a short section on multifractals. The treatment on finance touches on the use of wavelets by other authors in studying stock prices, commodity behaviour, market dynamics and foreign exchange rates. The treatment on geophysics covers what was omitted from the fourth chapter, namely, seismology, well logging, topographic feature analysis and the analysis of climatic data. The text concludes with an assortment of other application areas which could only be mentioned in passing. Unlike most other publications in the subject, this book does not treat wavelet transforms in a mathematically rigorous manner but rather aims to explain the mechanics of the wavelet transform in a way that is easy to understand. Consequently, it serves as an excellent overview of the subject rather than as a reference text. Keeping the mathematics to a minimum and omitting cumbersome and detailed proofs from the text, the book is best-suited to those who are new to wavelets or who want an intuitive understanding of the subject. Such an audience may include graduate students in engineering and professionals and researchers in engineering and the applied sciences.

The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance

To determine the optimal size of an energy storage system (ESS) in a fast electric vehicle (EV) charging station, minimization of ESS cost, enhancement of EVs’ resilience, and reduction of peak load have been considered in this article. Especially, the resilience aspect of the EVs is focused due to its significance for EVs during power outages. First, the stochastic load of the fast-charging station (FCS) and the resilience load of the EVs are estimated using probability distribution functions. This information is utilized to maintain the energy level in the ESS to ensure the resilience of EVs during power outages. Then, the annualized cost of the ESS is determined using the annual interest rate and lifetime of ESS components. Finally, the optimal ESS size is determined using the annualized ESS cost, penalty cost for buying power during peak hours, and penalty cost for resilience violations. Simulations along with sensitivity analysis of uncertainties (market price, arrival time of EVs, and the residual energy level of EVs), number of EVs in the FCS, and converter ratings are conducted. Simulation results have shown that increasing the penalty cost for peak intervals is a viable solution to decrease the peak load while controlling the cost of the FCS.

Optimal Sizing of Battery Energy Storage System in a Fast EV Charging Station Considering Power Outages

Several safety regulations, particularly concerning the charging of electric vehicles (EVs) are developed to ensure electric safety and prevent hazardous accidents, in which safety requirements for the EV supply equipment (EVSE) and the EV battery are two main driving factors. At present, quantitative assessment of electrical safety considering the operation conditions of large-scale EV charging stations (EVCSs) has still remained a challenge. Driven by the hierarchy of hazard control mechanisms, this article proposes a holistic approach to evaluate the electrical safety of the large-scale EVCSs when coupled to renewable power generation. Our approach mainly focuses on several topics on the operational safety of EVCS primarily concerning: 1) the facility degradation which could potentially result in a compromised EVSE reliability performance and EVCS protection failure; 2) the cyberattack challenges when the smart charging and the communication between EVCSs and electric utilities are enabled; and 3) the potential mismatch between the renewable output and EVCS demand, which could trigger the system stability challenges during normal operation and inability to supply the critical EV loads during outages. The proposed framework will provide informative guidelines to the EVCS operators for continuous monitoring and effective management of the day-to-day EVCS operation.

Electrical Safety Considerations in Large-Scale Electric Vehicle Charging Stations

The smart electricity grids have been evolving to a more complex cyber-physical ecosystem of infrastructures with integrated communication networks, new carbon-free sources of power generation, advanced monitoring and control systems, and a myriad of emerging modern physical hardware technologies. With the unprecedented complexity and heterogeneity in dynamic smart grid networks comes additional vulnerability to emerging threats such as cyber attacks. Rapid development and deployment of advanced network monitoring and communication systems on one hand, and the growing interdependence of the electric power grids to a multitude of lifeline critical infrastructures on the other, calls for holistic defense strategies to safeguard the power grids against cyber adversaries. In order to improve the resilience of the power grid against adversarial attacks and cyber intrusions, advancements should be sought on detection techniques, protection plans, and mitigation practices in all electricity generation, transmission, and distribution sectors. This survey discusses such major directions and recent advancements from a lens of different detection techniques, equipment protection plans, and mitigation strategies to enhance the energy delivery infrastructure resilience and operational endurance against cyber attacks. This undertaking is essential since even modest improvements in resilience of the power grid against cyber threats could lead to sizeable monetary savings and an enriched overall social welfare.

/pdf/electric-power-grid-resilience-to-cyber-adversaries-state-of-190drb8ihn.pdf

Electric Power Grid Resilience to Cyber Adversaries: State of the Art

Power grid operation continuously undergoes state transitions caused by internal and external uncertainties, e.g., equipment failures and weather-driven faults, among others. This prompts an observation of different types of waveforms at the measurement points (substations) in power systems. Modern power systems utilize phasor measurement units (PMUs) and intelligent electronic devices embedded with PMU functionality to capture the corresponding peculiarities through synchrophasor measurements. However, existing PMU devices are equipped with only one synchrophasor estimation algorithm (SEA) and are, thus, not always robust to handle different types of signals across the network. This article proposes a PMU-embedded framework that ensures real-time grid surveillance and potentially enables adaptive selection of preinstalled SEAs in the PMU. Therefore, it ensures high-fidelity measurements at all times and irrespective of the input signals. Our proposed framework consists of: 1) a pseudocontinuous quadrature wavelet transform which generates the featured scalograms and 2) a convolutional neural network for event classification based on the extracted features in the scalograms. Our experiments demonstrate that the proposed framework achieves high classification accuracy on multiple types of prevailing events in power grids, through which an enhanced grid-scale situational awareness in real time can be realized.

Power Grid Online Surveillance Through PMU-Embedded Convolutional Neural Networks

Modern power delivery systems are rapidly evolving with high proliferation of power-electronic (PE)-interfaced distributed energy resources (DERs). Compared to the conventional sources of generation, the PE-interfaced DERs, e.g., solar and wind resources, are attributed substantially different characteristics such as lower overload capability and limited frequency response patterns. This paper focuses on effective management and control mechanisms for PE-interfaced DERs in power distribution systems with high penetration of renewables, particularly under fault, voltage-sag, load variations, and other prevailing conditions in the grid. Aiming at the solutions to enhance the system performance resilience, we introduce an advanced model predictive control (MPC) based scheme to control the DER units, minimize the impact of transients and disruptions, speed up the response and recovery of particular metrics and parameters, and maintain an acceptable operation condition. The performance of the suggested control scheme is tested on a modified IEEE 34-bus test feeder, where the proposed solution demonstrates its effectiveness to minimize the system transient during faults, with an enhanced grid-edge and system-wide resilience characteristics in voltage profiles.

/pdf/advanced-control-solutions-for-enhanced-resilience-of-modern-6va34n6adx.pdf

Advanced control solutions for enhanced resilience of modern power-electronic-interfaced distribution systems

Power grid operation continuously experiences state transitions caused by the internal and external uncertainties, e.g., equipment failures and weather-driven faults. This prompts an observation of different types of waveforms at the measurement points (substations) in power systems captured by the phasor measurement units (PMUs) and intelligent electronic devices (IEDs) embedded with PMU functionality, e.g., digital relays and fault recorders. The PMU should be, hence, equipped with either one synchrophasor estimation algorithm (SEA) that is accurate and robust to many different types of signals any time across the network, or should adaptively select the promising SEA, among an embedded suite of algorithms. This paper proposes a PMU-embedded framework that can ensure real-time grid surveillance and potentially enables adaptive selection of SEA for more accurate synchrophasor estimation. Our proposed framework is consisted of two components: (i) a pseudo continuous quadrature wavelet transform (PCQ-WT) algorithm using a modified Gabor wavelet transform, which generates the featured-scalograms; and (ii) a convolutional neural network (CNN), that classifies the events based on the extracted features in the scalograms. Our experiments demonstrate that the proposed framework achieves state-of-the-art classification accuracy on multiple types of prevailing events in power grids, through which an enhanced grid-scale situational awareness in real-time can be realized.

Shiyuan Wang

Papers

Electrical Safety Considerations in Large-Scale Electric Vehicle Charging Stations

Electric Power Grid Resilience to Cyber Adversaries: State of the Art

Power Grid Online Surveillance Through PMU-Embedded Convolutional Neural Networks

Advanced control solutions for enhanced resilience of modern power-electronic-interfaced distribution systems

Power Grid Online Surveillance through PMU-Embedded Convolutional Neural Networks