297 research outputs found
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
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