IEEE Educational Events

Machine Learning for Reliable Communication in 5G and 6G Wireless Systems
Machine Learning for Reliable Communication in 5G and 6G Wireless Systems 150 150 ieeeeduweek

Machine Learning for Reliable Communication in 5G and  6G Wireless Systems

On Wednesday 30th November at 12H00 PM Montreal Time zone

ZOOM LINK: https://uqtr.zoom.us/j/84721640964?pwd=YWxrZmRwY203ajdvSVlVWUdMZkxHUT09

MEETING ID: 847 2164 0964

PASSWORD: 170254

ABSTRACT

Machine Learning (ML) has shown a great potential in revolutionizing communication system worldwide. The ever-increasing complexity of the wireless network, and the emergence of novel use cases such as autonomous cars, industrial automation, virtual reality, e-health, and several intelligent applications, machine learning (ML) is expected to be essential to assist in making the 5G vision conceivable. The cognitive optimization of essential radio resources for efficient end-to-end communication, enabling improvement in conventional communication theories and new architectural development can be facilitated in 5G and 6G by the use of machine learning. Hence, a comprehensive overview of emerging studies on machine learning and deep learning (DL) based physical and MAC layer enhancements in 5G will be presented. To be specific a brief idea regarding the 5G service classes (i.e. eMBB, URLLC and mMTC) and the scope of ML and DL in facilitating the QoS requirements will be focused. In addition to this, the challenges and possible solutions in implementing ML and DL in 5G will be presented. The open research areas will be presented for motivating further developments in enabling 5G and beyond wireless communication.

IEEE PES Foothill Section Seminar: Insight to Action – Grid Analytics Journey
IEEE PES Foothill Section Seminar: Insight to Action – Grid Analytics Journey 150 150 ieeeeduweek

IEEE PES Foothill Section Seminar

 

Thursday, November 10 | 16:00 – 17:00 PM (CDT)

14:00 – 15:00 PM (PST)

Zoom Meeting Link:  https://ucr.zoom.us/j/97226988824

Meeting ID: 972 2698 8824

 

Insight to Action – Grid Analytics Journey

 

Abstract:

Exelon’s Infrastructure and Safety Analytics team is helping develop analytic insights to support Distribution, Transmission, Operation optimizations to drive reliability, resiliency, cost benefits for grid investments and enhancing organization safety by moving from lagging to leading indicators. This webinar will focus on a T&S Advanced Analytics project, to showcase the importance of investment during project ideation on adoption strategy, creating actionable insights, empowering business/ engineers to lead and take active role in analytic projects, how to identify change management and external dependencies upfront as part of analytics prioritization.

Utilities have been heavily reliant on subject matter knowledge in the last century to drive preventive maintenance (PM) program and schedule PM for a variety of assets. Our engineers are experts in the industry and have leveraged their and industry knowledge of asset measurements and inspection results to manage these programs, however, as human beings, we are limited in terms of considering a variety of factors and assessing the distribution of each factor. This is where data driven approaches shine and enables subject matter experts with checking million to billion combinations to come up with the best model to predict future. These models can then be used to supplement engineer knowledge, and support industry-wide acceptance of a systematic approach to transition to condition-based maintenance programs. Latest model was validated against business-as-usual to provide 85% accuracy using success criteria developed for this project. PECO team is quantifying and confirming the benefits of model output to extract technical and data science success, including that model can be used in the real world “what would be do differently”.

 

 

 

 

 

 

 

 

Mr. Po-Chen Chen

Exelon

 Po-Chen Chen received his B.Sc. and M.Sc. degrees in electrical engineering from Polytechnic Institute of New York University, Brooklyn, NY, in 2010 and 2012, respectively. His power system expertise includes distributed generation, power system analysis, power system protection and control, voltage quality and stability studies, geographical information system, and big data application for distribution system.

Po-Chen Chen is currently a data science manager in Exelon’s Infrastructure and Safety Analytics team. In his most recent role at Sentient Energy, he focused on agile product development to develop predictive analytics using waveform classification for outage detection. At Duke Energy, as a data scientist lead expert, his team developed advanced data analytics for load research and rate design and developed models for behavioral demand response programs and winter peak analysis for energy modeling. At Oncor Electric Delivery, he helped architect HDFS and data lake ecosystems on IBM cloud and developed AI solutions on Spark-Hadoop and GPU platforms for their T&D customers.

Mr. Chen’s publications include 18 conference proceedings, 6 journal articles and 1 book chapter. He is also an elite reviewer for more than 16 journals and have been recognized by IEEE Transactions societies with 4 exceptional reviewer awards.

[WORKSHOP RAS UNMSM] TALLER DE PYTHON USO DE LIBRERIAS NUMPY, PANDAS, MATPLOTLIB
[WORKSHOP RAS UNMSM] TALLER DE PYTHON USO DE LIBRERIAS NUMPY, PANDAS, MATPLOTLIB 843 843 ieeeeduweek

En este taller se observarán las funciones más utilizadas de las librerías Numpy, Matplotlib y Pandas, además se desarrollarán múltiples aplicaciones en el campo de Data Science, Machine Learning y problemas computacionales de física. También se analizarán librerías que complementan a estas 3 así como otras librerías alternativas.

IEEE German EMC Chapter – Professional Talk: E&H – Instant Best Friends ForeverMethods of Machine Learning: Tools or Toys for EMC Engineering?
IEEE German EMC Chapter – Professional Talk: E&H – Instant Best Friends ForeverMethods of Machine Learning: Tools or Toys for EMC Engineering? 150 150 ieeeeduweek

Over the past few years various methods of machine learning (ML) have attracted attention in engineering disciplines. EMC engineering is one of them and in this presentation we try to understand why and how they help with tasks that are essential to EMC. After a short 
introduction into ML representative EMC related publications are reviewed and results from our own research in the area of power integrity are presented.

Machine Intelligence in Image Processing
Machine Intelligence in Image Processing 150 150 ieeeeduweek

Sarvajanik College of Engineering and Technology, Surat, in association with IEEE SPS (Signal Processing Society) SCET Student Branch Chapter, IEEE SPS GS (Gujarat Section) and technically co-sponsored by IEEE Computer Society Chapter, Computational Intelligence Society chapter and IEEE SCET Student Branch is organising a talk series on “Machine Intelligence in Image Processing” on 15th February 2022.

MACHINE LEARNING APLICADO A LA INGENIERÍA USANDO BASADO EN PYTHON, (DIA 3)
MACHINE LEARNING APLICADO A LA INGENIERÍA USANDO BASADO EN PYTHON, (DIA 3) 150 150 ieeeeduweek

Machine learning aplicado a la ingeniería usando basado en Python

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