DC-ECN: A machine-learning based dynamic threshold control scheme for ECN marking in DCN

2020 ◽  
Vol 150 ◽  
pp. 334-345 ◽  
Author(s):  
Akbar Majidi ◽  
Nazila Jahanbakhsh ◽  
Xiaofeng Gao ◽  
Jiaqi Zheng ◽  
Guihai Chen
Keyword(s):  
Machine Learning ◽  
Dynamic Threshold ◽  
Threshold Control ◽  
Control Scheme

0.5V FinFET SRAM with dynamic threshold control of pass gates for salvaging malfunctioned bits

2010 ◽  
Author(s):  
S. O'uchi ◽  
K. Endo ◽  
Y. X. Liu ◽  
T. Nakagawa ◽  
T. Matsukawa ◽  
...  
Keyword(s):  
Dynamic Threshold ◽  
Threshold Control

Low-Voltage-Signaling CMOS Receiver with Dynamic Threshold Control

2005 ◽  
Vol 44 (4B) ◽  
pp. 2088-2092
Author(s):  
Kazunori Tsuda ◽  
Yutaka Arima ◽  
Tanemasa Asano
Keyword(s):  
Low Voltage ◽  
Dynamic Threshold ◽  
Threshold Control ◽  
Cmos Receiver

Simple backgated MOSFET structure for dynamic threshold control in fully depleted SOI CMOS

1996 ◽  
Vol 32 (12) ◽  
pp. 1093 ◽  
Author(s):  
N.G. Tarr ◽  
R. Soreefan ◽  
T.W. MacElwee ◽  
W.M. Snelgrove ◽  
S. Bazarjani
Keyword(s):  
Dynamic Threshold ◽  
Threshold Control ◽  
Fully Depleted ◽  
Soi Cmos

scholarly journals Smart Helmet: Thresh-Learner–Online Machine Learning on Data Streams

2019 ◽  
Vol 9 (1S3) ◽  
pp. 466-473
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Real Life ◽  
General Purpose ◽  
Streaming Data ◽  
Series Data ◽  
Dynamic Threshold ◽  
Wide Range ◽  
Set Up

Today, with an enormous generation and availability of time series data and streaming data, there is an increasing need for an automatic analyzing architecture to get fast interpretations and results. One of the significant potentiality of streaming analytics is to train and model each stream with unsupervised Machine Learning (ML) algorithms to detect anomalous behaviors, fuzzy patterns, and accidents in real-time. If executed reliably, each anomaly detection can be highly valuable for the application. In this paper, we propose a dynamic threshold setting system denoted as Thresh-Learner, mainly for the Internet of Things (IoT) applications that require anomaly detection. The proposed model enables a wide range of real-life applications where there is a necessity to set up a dynamic threshold over the streaming data to avoid anomalies, accidents or sending alerts to distant monitoring stations. We took the major problem of anomalies and accidents in coal mines due to coal fires and explosions. This results in loss of life due to the lack of automated alarming systems. We propose Thresh-Learner, a general purpose implementation for setting dynamic thresholds. We illustrate it through the Smart Helmet for coal mine workers which seamlessly integrates monitoring, analyzing and dynamic thresholds using IoT and analysis on the cloud.

Technology Focus: Flow Assurance (November 2021)

2021 ◽  
Vol 73 (11) ◽  
pp. 72-72
Author(s):  
Galen Dino
Keyword(s):  
Machine Learning ◽  
Multiphase Flow ◽  
Slug Flow ◽  
Flow Assurance ◽  
The Novel ◽  
Empirical Correlations ◽  
Liquid Holdup ◽  
Control Scheme ◽  
Technical Effort ◽  
Additional Reading

I sincerely hope that all JPT readers and your families, peers, and employers remain safe and healthy and have work as they read this year’s Flow Assurance feature. Flow-assurance effects from slug-flow engineering, design, maintenance, and operations technical concerns still create and sustain challenging technical issues requiring safe, economical solutions for both onshore unconventional and offshore conventional production facilities. The recurring long-term mitigation of slugging and various flow-assurance phenomena—along with the prevention of wax, erosion, asphaltenes, corrosion, and salt deposition—and gas hydrate prediction and handling still demand attention and considerable project technical effort. Slug-flow assessments present opportunities for significant optimization in work flows to target governing operating scenarios. Paper OTC 30172 describes an integrated iterative approach between the flow-assurance and pipeline-engineering disciplines to streamline the work flow based on the value or cost associated with changes in input parameters that affect pipeline fatigue-assessment outcomes. Case studies on two multiphase pipelines are presented to illustrate this design approach. The results show that early identification of the key pipeline profile features and dominating spans for pipeline slugging fatigue assessments facilitated the optimization of slug-flow modeling and reduced computational time. The second paper, SPE 203448, presents decision trees that are considered as nonparametric machine-learning models. The data sets used in training and testing the predictive model are experimental and were collected from literature. Air/kerosene and air/water mixtures were used in obtaining the experimental data points. Results show that the proposed boosted decision tree regression (BDTR) model outperforms the best empirical correlations and the fuzzy-logic model used in estimating liquid holdup in gas/liquid multiphase flows. For the built model, the most important input feature in estimating liquid holdup is the superficial gas velocity. The empirical correlations developed in the past for identifying liquid holdup in multiphase flow can be applied only under the flow conditions by which they were originally developed. However, this machine-learning model does not suffer from this limitation. The third paper, OTC 31298, describes a slugging-control solution that was rejected because of the use of a pseudovariable as the principal control point. A novel control scheme, therefore, was developed and tested on simulations for both hydrodynamic slugging and severe riser-induced slugging in an Angolan field. The project implemented the novel active slugging control using a topsides control valve and topsides instrumentation. While a pseudovariable, a pseudoflow controller, was used, it was part of a cascade scheme such that the principal control variable was a real top-side pressure measurement. Upon com-missioning, slugging at the facility was found to be more severe than anticipated during design, but the novel active slug-control scheme was effective in controlling incoming slugs. The desire to understand better how to describe and improve flow assurance and multiphase flow for both offshore and onshore facilities drives new production technology research, applications, and approaches. The three papers listed for additional reading focus on developing further new analytical tools while providing safe, cost-effective, and reliable operations for flow assurance. I hope you find them as interesting as I did. In addition, I invite you to join the Flow Assurance Technical Section to augment your learning. Recommended additional reading at OnePetro: www.onepetro.org. OTC 30177 - Real-Time Online Hydrate Monitoring and Prevention in Offshore Fields by Syahida Husna Azman, Petronas, et al. SPE 201316 - Modeling Dynamic Loads Induced by Slug Flows Considering Gas Expansion Caused by the Pressure Gradient in a Free Span Horizontal Hanging Pipeline by Gabriel Meneses Santos, Universidade Estadual de Campinas, et al. OTC 31238 - Taylor Bubbles of Viscous Slug Flow in Inclined Pipes by Longtong Abednego Dafyak, University of Nottingham, et al.

Low-Voltage-Signaling CMOS Receiver with Dynamic Threshold Control

2004 ◽  
Author(s):  
Kazunori TSUDA ◽  
Yutaka ARIMA ◽  
Tanemasa ASANO
Keyword(s):  
Low Voltage ◽  
Dynamic Threshold ◽  
Threshold Control ◽  
Cmos Receiver
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