Artificial Intelligence for Sign Language Recognition and Translation

In a world where people are more connected, the barriers between deaf people and hearing people is more visible than ever. A neural sign language translation system would break many of these barriers. However, there are still many tasks to be solved before full automatic sign language translation is possible. Sign Language Translation is a difficult multimodal machine translation problem with no clear one-to-one mapping to any spoken language. In this paper I give a review of sign language and its challenges regarding neural machine translation. I evaluate the state-of-the-art Sign Language Translation approach, and apply a modified version of the Evolved Transformer to the existing Sign Language Transformer. I show that the Evolved Transformer encoder produces better results over the Transformer encoder with lower dimensions

Individual temperature compensation

Dielectric testing techniques in the time and frequency domains are increasingly being used by transformer manufacturers, power utilities and researchers for transformer oil-paper insulation systems condition assessment. Since 1997, when the first portable device designed to carry out dielectric response tests in the frequency domain in the field was put on the market, the technology has evolved and new features have been incorporated. One of these features is becoming a “must have” tool for power transformer dielectric condition assessment: individual temperature compensation

Individual temperature compensation

Dielectric testing techniques in the time and frequency domains are increasingly being used by transformer manufacturers, power utilities and researchers for transformer oil-paper insulation systems condition assessment. Since 1997, when the first portable device designed to carry out dielectric response tests in the frequency domain in the field was put on the market, the technology has evolved and new features have been incorporated. One of these features is becoming a “must have” tool for power transformer dielectric condition assessment: individual temperature compensation

Soft market issues

The growths and declines of transformer market through the decades have been driven by grid expansions, global economic crises and in recent years by emerging distributed generation, energy efficiency initiatives, and sophisticated control through smart grid and FACT systems. Every aspect of transformer design and use has evolved and will continue to evolve over the coming decades. Due to price pressure and competition, transformers are nowadays built more compactly with reduced usage of materials which consequently gives rise to quality issues. Sophisticated network management complicates the definition of requirements placed on the transformer and the definition based solely on the lowest price is no longer sufficient

Prediction of remaining life of power transformers based on left truncated and right censored lifetime data

Prediction of the remaining life of high-voltage power transformers is an important issue for energy companies because of the need for planning maintenance and capital expenditures. Lifetime data for such transformers are complicated because transformer lifetimes can extend over many decades and transformer designs and manufacturing practices have evolved. We were asked to develop statistically-based predictions for the lifetimes of an energy company's fleet of high-voltage transmission and distribution transformers. The company's data records begin in 1980, providing information on installation and failure dates of transformers. Although the dataset contains many units that were installed before 1980, there is no information about units that were installed and failed before 1980. Thus, the data are left truncated and right censored. We use a parametric lifetime model to describe the lifetime distribution of individual transformers. We develop a statistical procedure, based on age-adjusted life distributions, for computing a prediction interval for remaining life for individual transformers now in service. We then extend these ideas to provide predictions and prediction intervals for the cumulative number of failures, over a range of time, for the overall fleet of transformers.Comment: Published in at http://dx.doi.org/10.1214/00-AOAS231 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

UHF diagnostic monitoring techniques for power transformers

This paper initially gives an introduction to ultra-high frequency (UHF) partial discharge monitoring techniques and their application to gas insulated substations. Recent advances in the technique, covering its application to power transformers, are then discussed and illustrated by means of four site trials. Mounting and installation of the UHF sensors is described and measurements of electrical discharges inside transformers are presented in a range of formats, demonstrating the potential of the UHF method. A procedure for locating sources of electrical discharge is described and demonstrated by means of a practical example where a source of sparking on a tap changer lead was located to within 15 cm. Progress with the development of a prototype on-line monitoring and diagnostic system is reviewed and possible approaches to its utilization are discussed. New concepts for enhancing the capabilities of the UHF technique are presented, including the possibility of monitoring the internal mechanical integrity of plant. The research presented provides sufficient evidence to justify the installation of robust UHF sensors on transformer tanks to facilitate their monitoring if and when required during the service lifetime

Orchestrating Lightpath Adaptation and Flexible Functional Split to Recover Virtualized RAN Connectivity

This study shows that a two-step recovery scheme orchestrating lightpath transmission adaptation and evolved NodeB (eNB) functional split reconfiguration preserves the Virtualized RAN fronthaul connectivity even when network capacity is scarce.This work has been partially funded by the EU H2020 “5G-Transformer” Project (grant no. 761536

Determination of transformer performance index based on dissolved gas analysis

Dissolved Gas Analysis (DGA) is the single most widely used and most effective test to determine transformer’s operation conditions. The detection of gases generated in the transformer is the first available indication of the transformer malfunction. Several methods used to interpret of DGA results but not single method will necessarily give a true indication. In this project, the samples from energise transformers are extracted and analysis of gases evolved from oil degradation due to thermal and electrical faults. Several DGA interpretation schemes will be propose and applied for fault diagnostics. Some of the applied interpretation techniques are IEC 60599, Rogers and Durenburg Ratio, Duval Method and Key Gas analysis. Also, a case study on actual transformer faults is taken into account in this interpretation. The results of the interpretation will represent the condition of the power transformers and needs to quantify into a condition-based DGA index with a purpose to carry out necessary prevention actio

A wideband noise-canceling CMOS LNA exploiting a transformer

A broadband LNA incorporating single-ended to differential conversion, has been successfully implemented using a noise-canceling technique and a single on-chip transformer. The LNA achieves a high voltage gain of 19dB, a wideband input match (2.5-4.0 GHz), and a noise figure of 4-5.4 dB, while consuming only 8mW. The LNA is implemented in a 90nm CMOS process with 6 metal layers