Cause Identification of Electromagnetic Transient Events using Spatiotemporal Feature Learning

This paper presents a spatiotemporal unsupervised feature learning method for cause identification of electromagnetic transient events (EMTE) in power grids. The proposed method is formulated based on the availability of time-synchronized high-frequency measurement, and using the convolutional neural network (CNN) as the spatiotemporal feature representation along with softmax function. Despite the existing threshold-based, or energy-based events analysis methods, such as support vector machine (SVM), autoencoder, and tapered multi-layer perception (t-MLP) neural network, the proposed feature learning is carried out with respect to both time and space. The effectiveness of the proposed feature learning and the subsequent cause identification is validated through the EMTP simulation of different events such as line energization, capacitor bank energization, lightning, fault, and high-impedance fault in the IEEE 30-bus, and the real-time digital simulation (RTDS) of the WSCC 9-bus system.Comment: 9 pages, 7 figure

CONTENT-BASED SYLLABUS

There is growing interest in a model of language education that integrates language ad content instruction in EFL/ESL classroom. The current paper looks at content-based syllabus, with content (subject matter) providing the point of departure for it. Influences leading to the emergence of content-based instruction are discussed, followed by a brief description of the syllabus as well as the relevant frameworks for organizing and integrating. The paper then deals with several rationales for the integration of language and content. Next, some techniques, strategies, and activities used in implementing content-based syllabus are briefly mentioned. It is also suggested that pre-service and in-service teacher education can benefit from a focus on language and content integration. Some advantages and disadvantages of the syllabus are discussed at the end

Convergence Analysis of Non-Strongly-Monotone Stochastic Quasi-Variational Inequalities

While Variational Inequality (VI) is a well-established mathematical framework that subsumes Nash equilibrium and saddle-point problems, less is known about its extension, Quasi-Variational Inequalities (QVI). QVI allows for cases where the constraint set changes as the decision variable varies allowing for a more versatile setting. In this paper, we propose extra-gradient and gradient-based methods for solving a class of monotone Stochastic Quasi-Variational Inequalities (SQVI) and establish a rigorous convergence rate analysis for these methods. Our approach not only advances the theoretical understanding of SQVI but also demonstrates its practical applicability. Specifically, we highlight its effectiveness in reformulating and solving problems such as generalized Nash Equilibrium, bilevel optimization, and saddle-point problems with coupling constraints

CONTENT-BASED SYLLABUS

There is growing interest in a model of language education that integrates language ad content instruction in EFL/ESL classroom. The current paper looks at content-based syllabus, with content (subject matter) providing the point of departure for it. Influences leading to the emergence of content-based instruction are discussed, followed by a brief description of the syllabus as well as the relevant frameworks for organizing and integrating. The paper then deals with several rationales for the integration of language and content. Next, some techniques, strategies, and activities used in implementing content-based syllabus are briefly mentioned. It is also suggested that pre-service and in-service teacher education can benefit from a focus on language and content integration. Some advantages and disadvantages of the syllabus are discussed at the end