Polyphase distance relay by 6-input phase-sequence detector

1976 ◽  
Vol 123 (10) ◽  
pp. 1017
Author(s):  
U.S. Hazra ◽  
S.K. Basu ◽  
S. Chowdhuri
Keyword(s):  
Distance Relay ◽  
Phase Sequence ◽  
Input Phase

scholarly journals Remote Control, Protection and Monitoring of Pump for Agricultural Purposes

2020 ◽  
Vol 6 (12) ◽  
pp. 7-10
Author(s):  
Neetika Sengar ◽  
Arun Parakh
Keyword(s):  
Remote Control ◽  
Natural Resources ◽  
Power Quality ◽  
Power Supply ◽  
Cost Effective ◽  
Small Field ◽  
Power Requirement ◽  
Phase Sequence ◽  
Operational Issues ◽  
Input Phase

With the increasing pressure on our natural resources and diminishing soil quality, there is a need to make better efforts to conserve them. This project aims to help control the irrigation in a small field. The motor pump set equipped at the field is prone to a variety of operational issues like Power Quality issues, Reverse run of the pump, Irregular Power Supply. Using an ATMEGA Microcontroller equipped with GSM Module for connectivity, this model is able to provide the user with remote control for the motor pump set equipped at his field. Additionally, this model comes with protection schemes for detection of Input Phase Sequence Supply and protection against Over Voltage and Over Current. This system aims to reduce the man power requirement in the field by providing remote or at home control of the pump set applied on the field with cost effective and easy control which can be used by unskilled operators as well.

Analysis of phase-sequence detector for polyphase distance relay

1972 ◽  
Vol 119 (10) ◽  
pp. 1503 ◽  
Author(s):  
S.P. Patra ◽  
S.K. Basu ◽  
S. Choudhuri
Keyword(s):  
Distance Relay ◽  
Phase Sequence

scholarly journals Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ruichao Zhu ◽  
Tianshuo Qiu ◽  
Jiafu Wang ◽  
Sai Sui ◽  
Chenglong Hao ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Electromagnetic Waves ◽  
Inverse Method ◽  
Inverse Design ◽  
Learning Network ◽  
Design Paradigm ◽  
Simulation And Experiment ◽  
Meta Atom ◽  
Trained Network ◽  
Input Phase

AbstractMetasurfaces have provided unprecedented freedom for manipulating electromagnetic waves. In metasurface design, massive meta-atoms have to be optimized to produce the desired phase profiles, which is time-consuming and sometimes prohibitive. In this paper, we propose a fast accurate inverse method of designing functional metasurfaces based on transfer learning, which can generate metasurface patterns monolithically from input phase profiles for specific functions. A transfer learning network based on GoogLeNet-Inception-V3 can predict the phases of 28×8 meta-atoms with an accuracy of around 90%. This method is validated via functional metasurface design using the trained network. Metasurface patterns are generated monolithically for achieving two typical functionals, 2D focusing and abnormal reflection. Both simulation and experiment verify the high design accuracy. This method provides an inverse design paradigm for fast functional metasurface design, and can be readily used to establish a meta-atom library with full phase span.

Betalogger: Smartphone Sensor-based Side-channel Attack Detection and Text Inference Using Language Modeling and Dense MultiLayer Neural Network

2021 ◽  
Vol 20 (5) ◽  
pp. 1-17
Author(s):  
Abdul Rehman Javed ◽  
Saif Ur Rehman ◽  
Mohib Ullah Khan ◽  
Mamoun Alazab ◽  
Habib Ullah Khan
Keyword(s):  
Neural Network ◽  
Language Modeling ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Second Phase ◽  
Sensitive Data ◽  
Phase Sequence ◽  
Smartphone Technology ◽  
Conventional Machine ◽  
Sequence Generator

With the recent advancement of smartphone technology in the past few years, smartphone usage has increased on a tremendous scale due to its portability and ability to perform many daily life tasks. As a result, smartphones have become one of the most valuable targets for hackers to perform cyberattacks, since the smartphone can contain individuals’ sensitive data. Smartphones are embedded with highly accurate sensors. This article proposes BetaLogger , an Android-based application that highlights the issue of leaking smartphone users’ privacy using smartphone hardware sensors (accelerometer, magnetometer, and gyroscope). BetaLogger efficiently infers the typed text (long or short) on a smartphone keyboard using Language Modeling and a Dense Multi-layer Neural Network (DMNN). BetaLogger is composed of two major phases: In the first phase, Text Inference Vector is given as input to the DMNN model to predict the target labels comprising the alphabet, and in the second phase, sequence generator module generate the output sequence in the shape of a continuous sentence. The outcomes demonstrate that BetaLogger generates highly accurate short and long sentences, and it effectively enhances the inference rate in comparison with conventional machine learning algorithms and state-of-the-art studies.

Mitigating the Impacts of CCVT Subsidence Transients on the Distance Relay

2012 ◽  
Vol 27 (2) ◽  
pp. 497-505 ◽  
Author(s):  
Firouz Badrkhani Ajaei ◽  
Majid Sanaye-Pasand ◽  
Mahdi Davarpanah ◽  
Afshin Rezaei-Zare ◽  
Reza Iravani
Keyword(s):  
Distance Relay
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